From cbfe81fff08f540182bbdc898433d4a58a8f328f Mon Sep 17 00:00:00 2001
From: Raphaelle Versini <rapha.versini@gmail.com>
Date: Mon, 26 Aug 2024 20:35:45 +0200
Subject: [PATCH 01/15] first version of the tutorial

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+---
+layout: page
+title: "Protein-Protein modelling tutorial using a local version of HADDOCK3"
+excerpt: "A tutorial describing the use of HADDOCK3 to model a Protein-Protein complex"
+tags: [HADDOCK, HADDOCK3, installation, preparation, proteins, docking, analysis, workflows]
+image:
+  feature: pages/banner_education-thin.jpg
+---
+This tutorial consists of the following sections:
+
+* table of contents
+{:toc}
+
+<hr>
+<hr>
+
+## Introduction
+
+This tutorial demonstrates the use of the new modular HADDOCK3 version for predicting the structure of a protein-protein complex from NMR chemical shift perturbation (CSP) data. 
+Namely, we will dock two E. coli proteins involved in glucose transport: the glucose-specific enzyme IIA (E2A) and the histidine-containing phosphocarrier protein (HPr). 
+The structures in the free form have been determined using X-ray crystallography (E2A) (PDB ID [1F3G](https://www.ebi.ac.uk/pdbe/entry/pdb/1f3g){:target="_blank"}) and NMR spectroscopy (HPr) (PDB ID [1HDN](https://www.ebi.ac.uk/pdbe/entry/pdb/1hdn){:target="_blank"}). 
+The structure of the native complex has also been determined with NMR (PDB ID [1GGR](https://www.ebi.ac.uk/pdbe/entry/pdb/1ggr){:target="_blank"}). 
+These NMR experiments have also provided us with an array of data on the interaction itself (chemical shift perturbations, intermolecular NOEs, residual dipolar couplings, and simulated diffusion anisotropy data), which will be useful for the docking. 
+For this tutorial, we will only make use of inteface residues identified from NMR chemical shift perturbation data as described in [Wang *et al*, EMBO J (2000)](https://onlinelibrary.wiley.com/doi/10.1093/emboj/19.21.5635/abstract){:target="_blank"}.
+
+Throughout the tutorial, colored text will be used to refer to questions or instructions, and/or PyMOL commands.
+
+<a class="prompt prompt-question">This is a question prompt: try answering it!</a>
+<a class="prompt prompt-info">This an instruction prompt: follow it!</a>
+<a class="prompt prompt-pymol">This is a PyMOL prompt: write this in the PyMOL command line prompt!</a>
+<a class="prompt prompt-cmd">This is a Linux prompt: insert the commands in the terminal!</a>
+
+<hr>
+<hr>
+
+## Setup/Requirements
+
+In order to follow this tutorial you will need to work on a Linux or MacOSX
+system. We will also make use of [**PyMOL**][link-pymol] (freely available for
+most operating systems) in order to visualize the input and output data. We will
+provide you links to download the various required software and data.
+
+Further we are providing pre-processed PDB files for docking and analysis (but the
+preprocessing of those files will also be explained in this tutorial). The files have been processed
+to facilitate their use in HADDOCK and for allowing comparison with the known reference
+structure of the complex. For this _download and unzip the following_
+[zip archive](https://surfdrive.surf.nl/files/index.php/s/HvXxgxCTY1DiPsV){:target="_blank"}
+_and note the location of the extracted PDB files in your system_. In it you should find the following directories:
+
+* `haddock3`: Contains HADDOCK3 configuration and job files for the various scenarios in this tutorial
+* `pdbs`: Contains the pre-processed PDB files
+* `plots`: Contains pre-generated html plots for the various scenarios in this tutorial
+* `restraints`: Contains the interface information and the correspond restraint files for HADDOCK
+* `runs`: Contains pre-calculated (partial) run results for the various scenarios in this tutorial
+* `scripts`: Contains a variety of scripts used in this tutorial
+
+
+<hr>
+<hr>
+
+## HADDOCK general concepts
+
+HADDOCK (see [https://www.bonvinlab.org/software/haddock2.4](https://www.bonvinlab.org/software/haddock2.4){:target="_blank"})
+is a collection of python scripts derived from ARIA ([https://aria.pasteur.fr](https://aria.pasteur.fr){:target="_blank"})
+that harness the power of CNS (Crystallography and NMR System – [https://cns-online.org](https://cns-online.org){:target="_blank"})
+for structure calculation of molecular complexes. What distinguishes HADDOCK from other docking software is its ability,
+inherited from CNS, to incorporate experimental data as restraints and use these to guide the docking process alongside
+traditional energetics and shape complementarity. Moreover, the intimate coupling with CNS endows HADDOCK with the
+ability to actually produce models of sufficient quality to be archived in the Protein Data Bank.
+
+A central aspect to HADDOCK is the definition of Ambiguous Interaction Restraints or AIRs. These allow the
+translation of raw data such as NMR chemical shift perturbation or mutagenesis experiments into distance
+restraints that are incorporated in the energy function used in the calculations. AIRs are defined through
+a list of residues that fall under two categories: active and passive. Generally, active residues are those
+of central importance for the interaction, such as residues whose knockouts abolish the interaction or those
+where the chemical shift perturbation is higher. Throughout the simulation, these active residues are
+restrained to be part of the interface, if possible, otherwise incurring in a scoring penalty. Passive residues
+are those that contribute for the interaction, but are deemed of less importance. If such a residue does
+not belong in the interface there is no scoring penalty. Hence, a careful selection of which residues are
+active and which are passive is critical for the success of the docking.
+
+
+<hr>
+<hr>
+
+## A brief introduction to HADDOCK3
+
+
+HADDOCK3 is the next generation integrative modelling software in the
+long-lasting HADDOCK project. It represents a complete rethinking and rewriting
+of the HADDOCK2.X series, implementing a new way to interact with HADDOCK and
+offering new features to users who can now define custom workflows.
+
+In the previous HADDOCK2.x versions, users had access to a highly
+parameterisable yet rigid simulation pipeline composed of three steps:
+`rigid-body docking (it0)`, `semi-flexible refinement (it1)`, and `final refinement (itw)`.
+
+<figure style="text-align: center;">
+<img width="75%" src="./HADDOCK2-stages.png">
+</figure>
+
+In HADDOCK3, users have the freedom to configure docking workflows into
+functional pipelines by combining the different HADDOCK3 modules, thus
+adapting the workflows to their projects. HADDOCK3 has therefore developed to
+truthfully work like a puzzle of many pieces (simulation modules) that users can
+combine freely. To this end, the “old” HADDOCK machinery has been modularized,
+and several new modules added, including third-party software additions. As a
+result, the modularization achieved in HADDOCK3 allows users to duplicate steps
+within one workflow (e.g., to repeat twice the `it1` stage of the HADDOCK2.x
+rigid workflow).
+
+Note that, for simplification purposes, at this time, not all functionalities of
+HADDOCK2.x have been ported to HADDOCK3, which does not (yet) support NMR RDC,
+PCS and diffusion anisotropy restraints, cryo-EM restraints and coarse-graining.
+Any type of information that can be converted into ambiguous interaction
+restraints can, however, be used in HADDOCK3, which also supports the
+*ab initio* docking modes of HADDOCK.
+
+<figure style="text-align: center;">
+<img width="75%" src="./HADDOCK3-workflow-scheme.png">
+</figure>
+
+To keep HADDOCK3 modules organized, we catalogued them into several
+categories. But, there are no constraints on piping modules of different
+categories.
+
+The main module categories are "topology", "sampling", "refinement",
+"scoring", and "analysis". There is no limit to how many modules can belong to a
+category. Modules are added as developed, and new categories will be created
+if/when needed. You can access the HADDOCK3 documentation page for the list of
+all categories and modules. Below is a summary of the available modules:
+
+* **Topology modules**
+    * `topoaa`: *generates the all-atom topologies for the CNS engine.*
+* **Sampling modules**
+    * `rigidbody`: *Rigid body energy minimization with CNS (`it0` in haddock2.x).*
+    * `lightdock`: *Third-party glow-worm swam optimization docking software.*
+* **Model refinement modules**
+    * `flexref`: *Semi-flexible refinement using a simulated annealing protocol through molecular dynamics simulations in torsion angle space (`it1` in haddock2.x).*
+    * `emref`: *Refinement by energy minimisation (`itw` EM only in haddock2.4).*
+    * `mdref`: *Refinement by a short molecular dynamics simulation in explicit solvent (`itw` in haddock2.X).*
+* **Scoring modules**
+    * `emscoring`: *scoring of a complex performing a short EM (builds the topology and all missing atoms).*
+    * `mdscoring`: *scoring of a complex performing a short MD in explicit solvent + EM (builds the topology and all missing atoms).*
+* **Analysis modules**
+    * `caprieval`: *Calculates CAPRI metrics (i-RMSD, l-RMSD, Fnat, DockQ) with respect to the top scoring model or reference structure if provided.*
+    * `clustfcc`: *Clusters models based on the fraction of common contacts (FCC)*
+    * `clustrmsd`: *Clusters models based on pairwise RMSD matrix calculated with the `rmsdmatrix` module.*
+    * `rmsdmatrix`: *Calculates the pairwise RMSD matrix between all the models generated in the previous step.*
+    * `seletop`: *Selects the top N models from the previous step.*
+    * `seletopclusts`: *Selects top N clusters from the previous step.*
+
+The HADDOCK3 workflows are defined in simple configuration text files, similar to the TOML format but with extra features.
+Contrarily to HADDOCK2.X which follows a rigid (yet highly parameterisable)
+procedure, in HADDOCK3, you can create your own simulation workflows by
+combining a multitude of independent modules that perform specialized tasks.
+
+
+<hr>
+<hr>
+## Software requirements
+
+
+### Installing CNS
+The other required piece of software to run HADDOCK is its computational engine,
+CNS (Crystallography and NMR System –
+[https://cns-online.org](https://cns-online.org){:target="_blank"}). CNS is
+freely available for non-profit organizations. In order to get access to all
+features of HADDOCK you will need to compile CNS using the additional files
+provided in the HADDOCK distribution in the `varia/cns1.3` directory. Compilation of
+CNS might be non-trivial. Some guidance on installing CNS is provided in the online
+HADDOCK3 documentation page [here](https://www.bonvinlab.org/haddock3/CNS.html){:target="_blank"}.
+
+
+### Installing HADDOCK3
+
+In this tutorial we will make use of the HADDOCK3 version. In case HADDOCK3
+is not pre-installed in your system you will have to install it.
+
+To obtain HADDOCK3 navigate to [its repository][haddock-repo], fill the
+registration form, and then follow the [installation instructions](https://www.bonvinlab.org/haddock3/INSTALL.html){:target="_blank"}.
+
+
+### Auxiliary software
+
+**[FreeSASA][link-freesasa]**: FreeSASA will be used to identify surface-accessible residues
+(pre-calculated data are provided).
+
+**[PDB-tools][link-pdbtools]**: A useful collection of Python scripts for the
+manipulation (renumbering, changing chain and segIDs...) of PDB files is freely
+available from our GitHub repository. `pdb-tools` is automatically installed
+with HADDOCK3. If you have activated the HADDOCK3 Python environment you have
+access to the pdb-tools package.
+
+**[PyMol][link-pymol]**: We will make use of PyMol for visualization. If not
+already installed on your system, download and install PyMol.
+
+
+<hr>
+<hr>
+
+## Preparing PDB files for docking
+
+In this section we will prepare the PDB files of the antibody and antigen for docking.
+Crystal structures of both the antibody and the antigen in their free forms are available from the
+[PDBe database](https://www.pdbe.org){:target="_blank"}. In the case of the antibody which consists
+of two chains (L+H) we will have to prepare it for use in HADDOCK such as it can be treated as
+a single chain with non-overlapping residue numbering. For this we will be making use of `pdb-tools` from the command line.
+
+_**Note**_ that `pdb-tools` is also available as a [web service](https://wenmr.science.uu.nl/pdbtools/){:target="_blank"}.
+
+
+_**Note**_: Before starting to work on the tutorial, make sure to activate haddock3 (follow the workshop-specific instructions above), or, e.g. if installed using `conda`
+
+<a class="prompt prompt-cmd">
+conda activate haddock3
+</a>
+
+
+<hr>
+
+### Inspecting and preparing E2A for docking
+
+We will now inspect the E2A structure. For this start PyMOL and in the command line window of PyMOL (indicated by PyMOL>) type:
+
+<a class="prompt prompt-pymol">
+fetch 1F3G<br>
+show cartoon<br>
+hide lines<br>
+show sticks, resn HIS<br>
+</a>
+
+You should see a backbone representation of the protein with only the histidine side-chains visible.
+Try to locate the histidines in this structure.
+
+<a class="prompt prompt-question">Is there any phosphate group present in this structure?</a>
+
+Note that you can zoom on the histidines by typing in PyMOL:
+
+<a class="prompt prompt-pymol">zoom resn HIS</a>
+
+Revert to a full view with:
+
+<a class="prompt prompt-pymol">zoom vis</a>
+
+As a preparation step before docking, it is advised to remove any irrelevant water and other small molecules (e.g. small molecules from the crystallisation buffer), however do leave relevant co-factors if present. For E2A, the PDB file only contains water molecules. You can remove those in PyMOL by typing:
+
+<a class="prompt prompt-pymol">remove resn HOH</a>
+
+Now let's vizualize the residues affected by binding as identified by NMR. From [Wang *et al*, EMBO J (2000)](https://onlinelibrary.wiley.com/doi/10.1093/emboj/19.21.5635/abstract){:target="_blank"} the following residues of E2A were identified has having significant chemical shift perturbations:
+
+<a class="prompt prompt-info">38,40,45,46,69,71,78,80,94,96,141</a>
+
+We will now switch to a surface representation of the molecule and highlight the NMR-defined interface. In PyMOL type the following commands:
+
+<a class="prompt prompt-pymol">
+color white, all<br>
+show surface<br>
+select e2a_active, (1F3G and resi 38,40,45,46,69,71,78,80,94,96,141)<br>
+color red, e2a_active<br>
+</a>
+
+<figure align="center">
+<img src="./e2a-surface-airs.png">
+</figure>
+
+Inspect the surface.
+
+<a class="prompt prompt-question">Do the identified residues form a well defined patch on the surface?</a>
+<a class="prompt prompt-question">Do they form a contiguous surface?</a>
+
+The answer to the last question should be no: We can observe residue in the center of the patch that do not seem significantly affected while still being in the middle of the defined interface. This is the reason why in HADDOCK we also define "*passive*" residues that correspond to surface neighbors of active residues. These can be selected manually, or more conveniently you can let the HADDOCK server do it for you (see [Setting up the docking run](#setting-up-the-docking-run) below).
+
+As final step save the molecule as a new PDB file which we will call: *e2a_1F3G.pdb*<br>
+For this in the PyMOL menu on top select:
+
+<a class="prompt prompt-info">File -> Export molecule...</a>
+<a class="prompt prompt-info">Click on the save button</a>
+<a class="prompt prompt-info">Select as ouptut format PDB (*.pdb *.pdb.gz)</a>
+<a class="prompt prompt-info">Name your file *e2a_1F3G.pdb* and note its location</a>
+
+After saving the molecule delete it from the Pymol window or close Pymol. You can remove the molecule by typing this into the command line window of PyMOL:
+
+<a class="prompt prompt-pymol">
+delete 1F3G
+</a>
+
+In a terminal, change the chain of e2a from a to B. 
+
+<a class="prompt prompt-cmd">
+pdb_chain -B e2a_1F3G.pdb > e2a_1F3G_B.pdb
+</a>
+
+This will be usefull in the docking phase, as HADDOCK3 needs different chain associated to each protein involved in the docking.
+
+<hr>
+
+### Adding a phosphate group
+
+Since the biological function of this complex is to transfer a phosphate group from one protein to another, via histidines side-chains, it is relevant to make sure that a phosphate group be present for docking. As we have seen above none is currently present in the PDB files. HADDOCK does support a list of modified amino acids which you can find at the following link: [https://wenmr.science.uu.nl/haddock2.4/library](https://wenmr.science.uu.nl/haddock2.4/library){:target="_blank"}.
+
+<a class="prompt prompt-question">Check the list of supported modified amino acids.</a>
+<a class="prompt prompt-question">What is the proper residue name for a phospho-histidine in HADDOCK?</a>
+
+In order to use a modified amino-acid in HADDOCK, the only thing you will need to do is to edit the PDB file and change the residue name of the amino-acid you want to modify. Don't bother deleting irrelevant atoms or adding missing ones, HADDOCK will take care of that. For E2A, the histidine that is phosphorylated has residue number 90. In order to change it to a phosphorylated histidine do the following:
+
+<a class="prompt prompt-info">Edit the PDB file (*e2a_1F3G_B.pdb*) in your favorite editor</a>
+<a class="prompt prompt-info">Change the name of histidine 90 to NEP </a>
+<a class="prompt prompt-info">Save the file (as simple text file) under a new name, e.g. *e2aP_1F3G.pdb*</a>
+
+**Note:** The same procedure can be used to introduce a mutation in an input protein structure.
+
+
+<hr>
+
+### Inspecting and preparing HPR for docking
+
+We will now inspect the HPR structure. For this start PyMOL and in the command line window of PyMOL type:
+
+<a class="prompt prompt-pymol">
+fetch 1HDN<br>
+show cartoon<br>
+hide lines<br>
+</a>
+
+Since this is an NMR structure it does not contain any water molecules and we don't need to remove them.
+
+Let's vizualize the residues affected by binding as identified by NMR. From [Wang *et al*, EMBO J (2000)](https://onlinelibrary.wiley.com/doi/10.1093/emboj/19.21.5635/abstract){:target="_blank"} the following residues were identified has having significant chemical shift perturbations:
+
+<a class="prompt prompt-info">15,16,17,20,48,49,51,52,54,56</a>
+
+We will now switch to a surface representation of the molecule and highlight the NMR-defined interface. In PyMOL type the following commands:
+
+<a class="prompt prompt-pymol">
+color white, all<br>
+show surface<br>
+select hpr_active, (1HDN and resi 15,16,17,20,48,49,51,52,54,56)<br>
+color red, hpr_active<br>
+</a>
+
+Again, inspect the surface.
+
+<a class="prompt prompt-question">Do the identified residues form a well defined patch on the surface?</a>
+<a class="prompt prompt-question">Do they form a contiguous surface?</a>
+
+Now since this is an NMR structure, it actually consists of an ensemble of models. HADDOCK can handle such ensemble, using each conformer in turn as starting point for the docking. We however recommend to limit the number of conformers used for docking, since the number of conformer combinations of the input molecules might explode (e.g. 10 conformers each will give 100 starting combinations and if we generate 1000 ridig body models (see [HADDOCK general concepts](#haddock-general-concepts) above) each combination will only be sampled 10 times).
+
+Now let's vizualise this NMR ensemble. In PyMOL type:
+
+<a class="prompt prompt-pymol">
+hide all<br>
+show ribbon<br>
+set all_states, on<br>
+</a>
+
+You should now be seing the 30 conformers present in this NMR structure. To illustrate the potential benefit of using an ensemble of conformations as starting point for docking let's look at the side-chains of the active residues:
+
+<a class="prompt prompt-pymol">
+show lines, hpr_active<br>
+</a>
+
+<figure align="center">
+<img src="./hpr-ensemble.png">
+</figure>
+
+You should be able to see the amount of conformational space sampled by those surface side-chains. You can clearly see that some residues do sample a large variety of conformations, one of which might lead to much better docking results.
+
+**Note:** Pre-sampling of possible conformational changes can thus be beneficial for the docking, but again do limit the number of conformers used for the docking (or increase the number of sampled models, which is possible for users with expert- or guru-level access. The default access level is however only easy - for a higher level access do request it after registration).
+
+As final step, save the molecule as a new PDB file which we will call: *hpr-ensemble.pdb*
+For this in the PyMOL menu select:
+
+<a class="prompt prompt-info">File -> Export molecule...</a>
+<a class="prompt prompt-info">Select as State 0 (all states)</a>
+<a class="prompt prompt-info">Click on Save...</a>
+<a class="prompt prompt-info">Select as ouptut format PDB (*.pdb *.pdb.gz)</a>
+<a class="prompt prompt-info">Name your file *hpr-ensemble.pdb* and note its location</a>
+
+
+<hr>
+<hr>
+
+## Defining restraints for docking
+
+Before setting up the docking we need first to generate distance restraint files
+in a format suitable for HADDOCK.  HADDOCK uses [CNS][link-cns]{:target="_blank"} as computational
+engine. A description of the format for the various restraint types supported by
+HADDOCK can be found in our [Nature Protocol][nat-pro]{:target="_blank"} paper, Box 4.
+
+Distance restraints are defined as:
+
+<pre style="background-color:#DAE4E7">
+assign (selection1) (selection2) distance, lower-bound correction, upper-bound correction
+</pre>
+
+The lower limit for the distance is calculated as: distance minus lower-bound
+correction and the upper limit as: distance plus upper-bound correction.  The
+syntax for the selections can combine information about chainID - `segid`
+keyword -, residue number - `resid` keyword -, atom name - `name` keyword.
+Other keywords can be used in various combinations of OR and AND statements.
+Please refer for that to the [online CNS manual](http://cns-online.org/v1.3/){:target="_blank"}.
+
+<hr>
+
+### Defining active and passive residues for E2A
+
+As stated before, the following residues were identified has having significant chemical shift perturbations from [Wang *et al*, EMBO J (2000)](https://onlinelibrary.wiley.com/doi/10.1093/emboj/19.21.5635/abstract){:target="_blank"}:
+
+<a class="prompt prompt-info">38,40,45,46,69,71,78,80,94,96,141</a>
+
+Hence, we are using these residues as `active` residues for the docking run. However, we have to define `passive` residues before the run. 
+These passive residues allows us to deal with potentially incomplete binding sites by defining surface neighbors as `passive` residues.
+These are added to the definition of the interface but will not lead to any energetic penalty if they are not part of the
+binding site in the final models, while the residues defined as `active` (typically the identified or predicted binding
+site residues) will. When using the HADDOCK server, `passive` residues will be automatically defined. Here since we are
+using a local version, we need to define those manually and create a file in which the active and passive residues will be listed.
+
+This can easily be done using a script from our [haddock-tools][haddock-tools] repository, which is also provided for convenience
+in the `scripts` directly of the archive you downloaded for this tutorial:
+
+<a class="prompt prompt-cmd">
+echo "38 40 45 46 69 71 78 80 94 96 141" > e2a.act-pass
+python ./scripts/passive_from_active.py e2a_1F3G.pdb 38,40,45,46,69,71,78,80,94,96,141 >> e2a.act-pass
+</a>
+
+The NMR-identified residues and their surface neighbors generated with the above command can be used to define ambiguous interactions restraints, either using the NMR identified residues as active in HADDOCK, or combining those with the surface neighbors and use this combination as passive only. Here we decided to treat the NMR-identified residues as active residues. 
+Note the file consists of two lines, with the first one defining the `active` residues and
+the second line the `passive` ones. We will use later these files to generate the ambiguous distance restraints for HADDOCK.
+
+In general it is better to be too generous rather than too strict in the
+definition of passive residues.
+
+An important aspect is to filter both the active (the residues identified from
+your mapping experiment) and passive residues by their solvent accessibility.
+Our web service uses a default relative accessibility of 15% as cutoff. This is
+not a hard limit. You might consider including even more buried residues if some
+important chemical group seems solvent accessible from a visual inspection.
+
+<hr>
+
+### Defining active and passive residues for HPR
+
+As stated before, the following residues were identified has having significant chemical shift perturbations from [Wang *et al*, EMBO J (2000)](https://onlinelibrary.wiley.com/doi/10.1093/emboj/19.21.5635/abstract){:target="_blank"}:
+
+<a class="prompt prompt-info">15,16,17,20,48,49,51,52,54,56</a>
+
+Using the same script provided in the archive of this tutorial: 
+
+<a class="prompt prompt-cmd">
+echo "15 16 17 20 48 49 51 52 54 56" > hpr.act-pass
+python ./scripts/passive_from_active.py hpr-ensemble.pdb 15,16,17,20,48,49,51,52,54,56 >> hpr.act-pass
+</a>
+
+<hr>
+
+### Defining the position restraints locally
+
+Once you have defined your active and passive residues for both molecules, you
+can proceed with the generation of the ambiguous interaction restraints (AIR) file for HADDOCK.
+For this you can either make use of our online [GenTBL][gentbl] web service, entering the
+list of active and passive residues for each molecule, and saving the resulting
+restraint list to a text file, or use the relevant `haddock-tools` script.
+
+To use our `haddock-tools` `active-passive-to-ambig.py` script (also found in the archive of the tutorial) you need to create for each molecule a file containing two lines:
+
+* The first line corresponds to the list of active residues (numbers separated by spaces)
+* The second line corresponds to the list of passive residues.
+
+* For E2A (the file called `e2a.act-pass`):
+<pre style="background-color:#DAE4E7">
+38 40 45 46 69 71 78 80 94 96 141
+35 37 39 42 43 44 47 48 64 66 68 70 72 74 81 82 83 84 86 88 97 98 99 100 105 109 110 131 132 133 142 143 144 145
+</pre>
+
+* and for HPR (the file called `hpr.act-pass`):
+<pre style="background-color:#DAE4E7">
+15 16 17 20 48 49 51 52 54 56
+9 10 11 12 21 24 25 37 38 40 41 43 45 46 47 53 55 57 58 59 60 84 85
+</pre>
+
+Using those two files, we can generate the CNS-formatted AIR restraint files
+with the following command:
+
+<a class="prompt prompt-cmd">
+python ./scripts/active-passive-to-ambig.py e2a.act-pass hpr.act-pass > e2a-hpr_air.tbl
+</a>
+
+This generates a file called `ambig-prot-prot.tbl` that contains the AIR
+restraints. The default distance range for those is between 0 and 2Å, which
+might seem short but makes senses because of the 1/r^6 summation in the AIR
+energy function that makes the effective distance be significantly shorter than
+the shortest distance entering the sum.
+
+The effective distance is calculated as the SUM over all pairwise atom-atom
+distance combinations between an active residue and all the active+passive on
+the other molecule: SUM[1/r^6]^(-1/6).
+
+If you modify manually this file, it is possible to quickly check if the format is valid.
+To do so, you can find in our [haddock-tools][haddock-tools] repository a folder named
+`haddock_tbl_validation` that contains a script called `validate_tbl.py` (also provided here in the `scripts` directory).
+To use it, type:
+
+<a class="prompt prompt-cmd">
+python ./scripts/validate_tbl.py \-\-silent e2a-hpr_air.tbl
+</a>
+
+No output means that your TBL file is valid.
+
+<hr>
+<hr>
+
+## Setting up the docking with HADDOCK3
+
+Now that we have all required files at hand (PBD and restraints files) it is time to setup our docking protocol.
+For this we need to create a HADDOCK3 configuration file that will define the docking workflow. In contrast to HADDOCK2.X,
+we have much more flexibility in doing this. We will illustrate this flexibility by introducing a clustering step
+after the initial rigid-body docking stage, select up to 10 models per cluster and refine all of those.
+
+HADDOCK3 also provides an analysis module (`caprieval`) that allows
+to compare models to either the best scoring model (if no reference is given) or a reference structure, which in our case
+we have at hand. This will directly allow us to assess the performance of the protocol for the following three scenarios:
+
+1. Scenario 1: Docking using the paratope information only and the surface of the antigen
+2. Scenario 2a: Docking using the paratope and the NMR-identified epitope as passive
+3. Scenario 2b: Docking using the paratope and the NMR-identified epitope as active
+
+The basic workflow for all three scenarios will consists of the following modules, with some differences in the restraints used and some parameter settings (see below):
+
+1. **`topoaa`**: *Generates the topologies for the CNS engine and build missing atoms*
+2. **`rigidbody`**: *Rigid body energy minimisation (`it0` in haddock2.x)*
+3. **`clustfcc`**: *Clustering of models based on the fraction of common contacts (FCC)*
+4. **`seletopclusts`**: *Selection of the top10 models of all clusters*
+5. **`flexref`**: *Semi-flexible refinement of the interface (`it1` in haddock2.4)*
+6. **`emref`**: *Final refinement by energy minimisation (`itw` EM only in haddock2.4)*
+7. **`clustfcc`**: *Clustering of models based on the fraction of common contacts (FCC)*
+8. **`caprieval`**: *Calculates CAPRI metrics (i-RMSD, l-RMSD, Fnat, DockQ) with respect to the top scoring model or reference structure if provided*
+
+The input PDB files are the same for all three scenarios. The differences are in the ambiguous interaction restraint files used and the sampling at the rigid body stage in the case of scenario1.
+
+
+<hr>
+
+### HADDOCK3 execution modes
+
+HADDOCK3 currently supports three difference execution modes that are defined in the first section of the configuration file of a run.
+
+#### 1. local mode
+
+In this mode HADDOCK3 will run on the current system, using the defined number of cores (`ncores`) in the config file
+to a maximum of the total number of available cores on the system minus one. An example of the relevant parameters to be defined in the first section of the config file is:
+
+{% highlight toml %}
+# compute mode
+mode = "local"
+#  1 nodes x 96 ncores
+ncores = 96
+{% endhighlight %}
+
+In this mode HADDOCK3 can be started from the command line with as argument the configuration file of the defined workflow.
+
+<a class="prompt prompt-cmd">
+haddock3 \<my-workflow-configuration-file\>
+</a>
+
+Alternatively redirect the output to a log file and send haddock3 to the background.
+
+_**Note**_: This is the execution mode you should use on the NMRBox resources. For the tutorial we limit the number of cores to 10.
+
+
+<a class="prompt prompt-cmd">
+haddock3 \<my-workflow-configuration-file\> \> haddock3.log &
+</a>
+
+_**Note**_: This is also the execution mode that should be used for example when submitting the HADDOCK3 job to a node of a cluster, requesting X number of cores.
+
+<details>
+  <summary style="bold">
+    <i>View an example script for submitting via the slurm batch system</i> <i class="material-icons">expand_more</i>
+  </summary>
+
+  {% highlight shell %}
+  #!/bin/bash
+  #SBATCH --nodes=1
+  #SBATCH --tasks-per-node=96
+  #SBATCH -J haddock3
+  #SBATCH --partition=medium
+
+  # load haddock3 module
+  module load haddock3
+  # or activate the haddock3 conda environment
+  ##source $HOME/miniconda3/etc/profile.d/conda.sh
+  ##conda activate haddock3
+
+  # go to the run directory
+  cd $HOME/HADDOCK3-antibody-antigen
+
+  # execute
+  haddock3 scenario1-surface-node.cfg
+  {% endhighlight %}
+  <br>
+</details>
+
+<details>
+<summary style="bold">
+View an EU-ASEAN HPC school example script for submitting to the Fugaku batch system <i class="material-icons">expand_more</i>
+ </summary>
+{% highlight shell %}
+#!/bin/bash
+#PJM -L  "node=1"                            # Assign 1 node
+#PJM -L  "elapse=02:00:00"                   # Elapsed time limit 2 hour
+#PJM -x PJM_LLIO_GFSCACHE=/vol0003:/vol0006  # volume names that job uses
+#PJM -s                                      # Statistical information output
+
+# active the haddock3 conda environment
+source /vol0601/share/ra020021/LifeScience/20221208_Bonvin/miniconda3-arm8/etc/profile.d/conda.sh
+conda activate haddock3
+
+# go to the tutorial directory in your home directory
+# edit if needed to specify the correct location
+cd $HOME/HADDOCK3-antibody-antigen
+
+# execute haddock3
+haddock3 scenario2a-NMR-epitope-pass-node.cfg
+{% endhighlight %}
+<br>
+</details>
+
+<br>
+
+#### 2. batch mode
+
+In this mode HADDOCK3 will typically be started on your local server (e.g. the login node) and will dispatch jobs to the batch system of your cluster.
+Two batch systems are currently supported: `slurm` and `torque` (defined by the `batch_type` parameter). In the configuration file you will
+have to define the `queue` name and the maximum number of concurrent jobs sent to the queue (`queue_limit`). Since HADDOCK3 single model
+calculations are quite fast, it is recommended to calculate multiple models within one job submitted to the batch system.
+The number of model per job is defined by the `concat` parameter in the configuration file.
+You want to avoid sending thousands of very short jobs to the batch system if you want to remain friend with your system administrators...
+
+An example of the relevant parameters to be defined in the first section of the config file is:
+
+{% highlight toml %}
+# compute mode
+mode = "batch"
+# batch system
+batch_type = "slurm"
+# queue name
+queue = "short"
+# number of concurrent jobs to submit to the batch system
+queue_limit = 100
+# number of models to produce per submitted job
+concat = 10
+{% endhighlight %}
+
+In this mode HADDOCK3 can be started from the command line as for the local mode.
+
+#### 3. MPI mode
+
+HADDOCK3 supports a parallel MPI implementation (functional but still very experimental at this stage). For this to work, the `mpi4py` library
+must have been installed at installation time. Refer to the [MPI-related instructions](https://www.bonvinlab.org/haddock3/tutorials/mpi.html).
+The execution mode should be set to `mpi` and the total number of cores should match the requested resources when submitting to the batch system.
+
+An example of the relevant parameters to be defined in the first section of the config file is:
+
+{% highlight toml %}
+# compute mode
+mode = "mpi"
+#  5 nodes x 50 tasks = ncores = 250
+ncores = 250
+{% endhighlight %}
+
+In this execution mode the HADDOCK3 job should be submitted to the batch system requesting the corresponding number of nodes and cores per node.
+
+<details>
+  <summary style="bold">
+    <i>View an example script for submitting an MPI HADDOCK3 job the slurm batch system</i> <i class="material-icons">expand_more</i>
+  </summary>
+  {% highlight shell %}
+  #!/bin/bash
+  #SBATCH --nodes=5
+  #SBATCH --tasks-per-node=50
+  #SBATCH -J haddock3mpi
+
+  # load haddock3 module
+  module load haddock3
+  # or make sure haddock3 is activated
+  ##source $HOME/miniconda3/etc/profile.d/conda.sh
+  ##conda activate haddock3
+
+  # go to the run directory
+  # edit if needed to specify the correct location
+  cd $HOME/HADDOCK3-antibody-antigen
+
+  # execute
+  haddock3 scenario2a-NMR-epitope-pass-mpi.cfg
+  {% endhighlight %}
+  <br>
+</details>
+
+<hr>
+
+### Scenario 1: 1000 rigidbody docking models, selection of top200 and flexible refinement + EM of those
+
+Now that we have all data ready, and know about execution modes of HADDOCK3 it is time to setup the docking for the first scenario in which we will use the paratope on the antibody to guide the docking, targeting the entire surface of the antibody. The restraint file to use for this is `e2a-hpr_air.tbl`. We proceed to produce 1000 rigidbody docking models, from which 200 will be selected and refined through flexible refinment and energy minimization. [DESCRIBE BETTER]. 
+For the analysis following the docking results, we are using the solved complex [1GGR](https://www.rcsb.org/structure/1GGR), named e2a-hpr_1GGR.pdb.
+The configuration file for this scenario (assuming a local running mode, eventually submitted to the batch system requesting a full node) is:
+
+{% highlight toml %}
+# ====================================================================
+# Protein-protein docking example with NMR-derived ambiguous interaction restraints
+
+# directory in which the scoring will be done
+run_dir = "run1-full"
+
+# execution mode
+mode = "batch"
+#  it will take the system's default
+# queue = "short"
+# concatenate models inside each job, concat = 5 each .job will produce 5 models
+concat = 5
+#  Limit the number of concurrent submissions to the queue
+queue_limit = 100
+
+# molecules to be docked
+molecules =  [
+    "data/e2aP_1F3G.pdb",
+    "data/hpr_ensemble.pdb"
+    ]
+
+# ====================================================================
+# Parameters for each stage are defined below, prefer full paths
+# ====================================================================
+[topoaa]
+autohis = false
+[topoaa.mol1]
+nhisd = 0
+nhise = 1
+hise_1 = 75
+[topoaa.mol2]
+nhisd = 1
+hisd_1 = 76
+nhise = 1
+hise_1 = 15
+
+[rigidbody]
+tolerance = 5
+ambig_fname = "data/e2a-hpr_air.tbl"
+sampling = 1000
+
+[caprieval]
+reference_fname = "data/e2a-hpr_1GGR.pdb"
+
+[seletop]
+select = 200
+
+[caprieval]
+reference_fname = "data/e2a-hpr_1GGR.pdb"
+
+[flexref]
+tolerance = 5
+ambig_fname = "data/e2a-hpr_air.tbl"
+
+[caprieval]
+reference_fname = "data/e2a-hpr_1GGR.pdb"
+
+[emref]
+tolerance = 5
+ambig_fname = "data/e2a-hpr_air.tbl"
+
+[caprieval]
+reference_fname = "data/e2a-hpr_1GGR.pdb"
+
+[clustfcc]
+
+[seletopclusts]
+top_models = 4
+
+[caprieval]
+reference_fname = "data/e2a-hpr_1GGR.pdb"
+
+# ====================================================================
+{% endhighlight %}
+
+This configuration file is provided in the `haddock3` directory of the downloaded data set for this tutorial as `docking-protein-protein-full.cfg`.
+
+If you have everything ready, you can launch haddock3 either from the command line, or, better,
+submitting it to the batch system requesting in this local run mode a full node (see local execution mode above).
+
+_**Note**_ that this scenario is computationally more expensive because of the increased sampling.
+On our own cluster, running in batch mode took 12 minutes and 37 seconds.
+Running in MPI mode with 250 cores on AMD EPYC 7451 processors took [TO BE TESTED].
+The same run on a single node using all 96 threads took on the same architecture [TO BE TESTED].
+
+<hr>
+
+### Scenario 2: 1000 rigidbody docking models, selection of top200 and flexible refinement + final refinement in explicit solvent (water) of those 
+
+In scenario 2 we are setting up the docking in which [DESCRIBE BETTER]. 
+The configuration file for this scenario (assuming a batch running mode) is:
+
+{% highlight toml %}
+# ====================================================================
+# Protein-protein docking example with NMR-derived ambiguous interaction restraints
+
+# directory in which the scoring will be done
+run_dir = "run2-mdref-full"
+
+# execution mode
+mode = "batch"
+# in which queue the jobs should run, if nothing is defined
+#  it will take the system's default
+# queue = "short"
+# concatenate models inside each job, concat = 5 each .job will produce 5 models
+concat = 5
+#  Limit the number of concurrent submissions to the queue
+queue_limit = 100
+# cns_exec = "path/to/bin/cns" # optional
+
+# molecules to be docked
+molecules =  [
+    "data/e2aP_1F3G.pdb",
+    "data/hpr_ensemble.pdb"
+    ]
+
+# ====================================================================
+# Parameters for each stage are defined below, prefer full paths
+# ====================================================================
+[topoaa]
+autohis = false
+[topoaa.mol1]
+nhisd = 0
+nhise = 1
+hise_1 = 75
+[topoaa.mol2]
+nhisd = 1
+hisd_1 = 76
+nhise = 1
+hise_1 = 15
+
+[rigidbody]
+tolerance = 5
+ambig_fname = "data/e2a-hpr_air.tbl"
+sampling = 1000
+
+[caprieval]
+reference_fname = "data/e2a-hpr_1GGR.pdb"
+
+[seletop]
+select = 200
+
+[caprieval]
+reference_fname = "data/e2a-hpr_1GGR.pdb"
+
+[flexref]
+tolerance = 5
+ambig_fname = "data/e2a-hpr_air.tbl"
+
+[caprieval]
+reference_fname = "data/e2a-hpr_1GGR.pdb"
+
+[mdref]
+tolerance = 5
+ambig_fname = "data/e2a-hpr_air.tbl"
+
+[caprieval]
+reference_fname = "data/e2a-hpr_1GGR.pdb"
+
+[clustfcc]
+
+[seletopclusts]
+top_models = 4
+
+[caprieval]
+reference_fname = "data/e2a-hpr_1GGR.pdb"
+
+# ====================================================================
+{% endhighlight %}
+
+This configuration is provided in the `haddock3` directory of the downloaded data set for this tutorial as `docking-protein-protein-mdref-full.cfg`. 
+
+If you have everything ready, you can launch haddock3 either from the command line, or, better, submitting it to the batch system requesting in this local run mode a full node (see local execution mode above).
+
+_**Note**_ On our own cluster, running in MPI mode with 250 cores on AMD EPYC 7451 processors the run completed in about [TO BE COMPLETED]. The same run on a single node using all 96 threads took on the same architecture about [TO BE COMPLETED]. In `batch` mode, using 100 queue slots and 10 models per job, the same run completed in about [TO BE COMPLETED].
+
+On the Fugaku supercomputer used for the EU ASEAN HPC school, running on a single node (48 [armv8 A64FX](https://github.com/fujitsu/A64FX){:target="_blank"} processors), this run completed in about [TO BE COMPLETED]. 
+
+<hr>
+
+### Scenario 3: 1000 rigidbody docking models, FCC clustering and selection of max 20 models per cluster followed by flexible refinement and EM 
+
+Scenario 3 is [TO BE COMPETED]
+The configuration file for this scenario (assuming a batch running mode) is:
+
+{% highlight toml %}
+# ====================================================================
+# Protein-protein docking example with NMR-derived ambiguous interaction restraints
+# ====================================================================
+
+# directory in which the scoring will be done
+run_dir = "run3-cltsel-full"
+
+# execution mode
+mode = "batch"
+# concatenate models inside each job, concat = 5 each .job will produce 5 models
+concat = 5
+#  Limit the number of concurrent submissions to the queue
+queue_limit = 100
+
+# molecules to be docked
+molecules =  [
+    "data/e2aP_1F3G.pdb",
+    "data/hpr_ensemble.pdb"
+    ]
+
+# ====================================================================
+# Parameters for each stage are defined below, prefer full paths
+# ====================================================================
+
+[topoaa]
+autohis = false
+[topoaa.mol1]
+nhisd = 0
+nhise = 1
+hise_1 = 75
+[topoaa.mol2]
+nhisd = 1
+hisd_1 = 76
+nhise = 1
+hise_1 = 15
+
+[rigidbody]
+tolerance = 5
+ambig_fname = "data/e2a-hpr_air.tbl"
+
+[caprieval]
+reference_fname = "data/e2a-hpr_1GGR.pdb"
+
+[clustfcc]
+
+[seletopclusts]
+## select the best 4 models of each cluster
+top_models = 20
+
+[caprieval]
+reference_fname = "data/e2a-hpr_1GGR.pdb"
+
+[flexref]
+tolerance = 5
+ambig_fname = "data/e2a-hpr_air.tbl"
+
+[caprieval]
+reference_fname = "data/e2a-hpr_1GGR.pdb"
+
+[emref]
+tolerance = 5
+ambig_fname = "data/e2a-hpr_air.tbl"
+
+[caprieval]
+reference_fname = "data/e2a-hpr_1GGR.pdb"
+
+[clustfcc]
+
+[seletopclusts]
+top_models = 4
+
+[caprieval]
+reference_fname = "data/e2a-hpr_1GGR.pdb"
+
+# ====================================================================
+{% endhighlight %}
+
+This configuration file is provided in the `haddock3` directory of the downloaded data set for this tutorial as `docking-protein-protein-cltsel-full.cfg`. 
+
+If you have everything ready, you can launch haddock3 either from the command line, or, better, submitting it to the batch system requesting in this local run mode a full node (see local execution mode above).
+
+_**Note**_ The running time for this scenario is similar to that of scenario 2a (see above).
+
+
+<hr>
+<hr>
+
+## Analysis of docking results
+
+### Structure of the run directory
+
+Once your run has completed inspect the content of the resulting directory. You will find the various steps (modules) of the defined workflow numbered sequentially, e.g.:
+
+{% highlight shell %}
+> ls scenario3/
+    00_topoaa/
+    01_rigidbody/
+    02_caprieval/
+    03_clustfcc/
+    04_seletopclusts/
+    05_caprieval/
+    06_flexref/
+    07_caprieval/
+    08_emref/
+    09_caprieval/
+    10_clustfcc/
+    11_seletopclusts/
+    12_caprieval/
+    analysis/
+    data/
+    log
+{% endhighlight %}
+
+There is in addition the log file (text file) and two additional directories:
+
+- the `data` directory containing the input data (PDB and restraint files) for the various modules
+- the `analysis` directory containing various plots to visualise the results for each `caprieval` step
+
+You can find information about the duration of the run at the bottom of the log file. Each sampling/refinement/selection module will contain PBD files.
+
+For example, the `X_seletopclusts` directory contains the selected models from each cluster. The clusters in that directory are numbered based
+on their rank, i.e. `cluster_1` refers to the top-ranked cluster. Information about the origin of these files can be found in that directory in the `seletopclusts.txt` file.
+
+The simplest way to extract ranking information and the corresponding HADDOCK scores is to look at the `X_caprieval` directories (which is why it is a good idea to have it as the final module, and possibly as intermediate steps). This directory will always contain a `capri_ss.tsv` file, which contains the model names, rankings and statistics (score, iRMSD, Fnat, lRMSD, ilRMSD and dockq score). E.g.:
+
+<pre style="background-color:#DAE4E7">
+model	md5	caprieval_rank	score	irmsd	fnat	lrmsd	ilrmsd	dockq	cluster_id	cluster_ranking	model-cluster_ranking	air	angles	bonds	bsa	cdih	coup	dani	desolv	dihe	elec	improper	rdcs	rg	sym	total	vdw	vean	xpcs
+../07_emref/emref_33.pdb	-	1	-147.229	0.894	0.889	1.452	1.542	0.866	-	-	-	6.877	0.000	0.000	1533.550	0.000	0.000	0.000	-10.230	0.000	-522.517	0.000	0.000	0.000	0.000	-548.824	-33.184	0.000	0.000
+../07_emref/emref_3.pdb	-	2	-145.818	0.949	0.917	2.103	1.801	0.858	-	-	-	7.810	0.000	0.000	1569.000	0.000	0.000	0.000	-9.026	0.000	-533.832	0.000	0.000	0.000	0.000	-556.827	-30.806	0.000	0.000
+../07_emref/emref_52.pdb	-	3	-141.925	1.016	0.889	1.378	1.678	0.850	-	-	-	12.488	0.000	0.000	1591.170	0.000	0.000	0.000	-9.507	0.000	-482.747	0.000	0.000	0.000	0.000	-507.376	-37.117	0.000	0.000
+../07_emref/emref_4.pdb	-	4	-141.400	1.067	0.778	2.299	2.094	0.791	-	-	-	4.617	0.000	0.000	1515.630	0.000	0.000	0.000	-10.495	0.000	-526.925	0.000	0.000	0.000	0.000	-548.288	-25.981	0.000	0.000
+../07_emref/emref_81.pdb	-	5	-137.507	1.569	0.639	4.430	3.047	0.634	-	-	-	30.617	0.000	0.000	1562.350	0.000	0.000	0.000	-16.298	0.000	-442.005	0.000	0.000	0.000	0.000	-447.257	-35.870	0.000	0.000
+....
+</pre>
+
+If clustering is performed prior to calling the `caprieval` module, the `capri_ss.tsv` will also contain information about to which cluster the model belongs to and its ranking within the cluster as shown above.
+
+The relevant statistics are:
+
+* **score**: *the HADDOCK score (arbitrary units)*
+* **irmsd**: *the interface RMSD, calculated over the interfaces the molecules*
+* **fnat**: *the fraction of native contacts*
+* **lrmsd**: *the ligand RMSD, calculated on the ligand after fitting on the receptor (1st component)*
+* **ilrmsd**: *the interface-ligand RMSD, calculated over the interface of the ligand after fitting on the interface of the receptor (more relevant for small ligands for example)*
+* **dockq**: *the DockQ score, which is a combination of irmsd, lrmsd and fnat and provides a continuous scale between 1 (equal to reference) and 0*
+
+The iRMSD, lRMSD and Fnat metrics are the ones used in the blind protein-protein prediction experiment [CAPRI](https://capri.ebi.ac.uk/) (Critical PRediction of Interactions).
+
+In CAPRI the quality of a model is defined as (for protein-protein complexes):
+
+* **acceptable model**: i-RMSD < 4Å or l-RMSD<10Å and Fnat > 0.1
+* **medium quality model**: i-RMSD < 2Å or l-RMSD<5Å and Fnat > 0.3
+* **high quality model**: i-RMSD < 1Å or l-RMSD<1Å and Fnat > 0.5
+
+<a class="prompt prompt-question">
+What is based on this CAPRI criterion the quality of the best model listed above (emref_33.pdb)?
+</a>
+
+In case the `caprieval` module is called after a clustering step an additional file will be present in the directory: `capri_clt.tsv`.
+This file contains the cluster ranking and score statistics, averaged over the minimum number of models defined for clustering
+(4 by default), with their corresponding standard deviations. E.g.:
+
+<pre style="background-color:#DAE4E7">
+cluster_rank    cluster_id      n       under_eval      score   score_std       irmsd   irmsd_std       fnat    fnat_std        lrmsd   lrmsd_std       dockq   dockq_std       air     air_std bsa     bsa_std desolv  desolv_std      elec    elec_std        total   total_std       vdw     vdw_std caprieval_rank
+1       1       4       -       -124.146        3.141   1.022   0.113   0.785   0.074   1.812   0.477   0.808   0.045   19.088  8.507   1493.348        101.980 -14.374 2.868   -390.668        44.141  -405.127        39.003  -33.547 7.112   1
+2       2       4       -       -109.733        4.447   8.384   0.538   0.153   0.063   15.962  0.969   0.135   0.029   64.065  29.996  1461.225        113.842 -13.164 2.827   -394.903        13.092  -354.834        34.074  -23.996 4.254   2
+3       6       4       -       -105.989        3.889   4.022   0.232   0.243   0.050   6.572   0.337   0.331   0.025   38.555  17.146  1385.205        39.561  -6.273  3.174   -425.420        56.558  -405.353        38.939  -18.487 5.586   3
+...
+</pre>
+
+In this file you find the cluster rank, the cluster ID (which is related to the size of the cluster, 1 being always the largest cluster), the number of models (n) in the cluster and the corresponding statistics (averages + standard deviations). The corresponding cluster PDB files will be found in the processing `X_seletopclusts` directory.
+
+<hr>
+
+### Analysis scenario 1: 
+
+Let us now analyze the docking results for this scenario. Use for that either your own run or a pre-calculated run provided in the `runs` directory (note that to save space only partial data have been kept in this pre-calculated runs, but all relevant information for this tutorial is available).
+
+First of all let us check the final cluster statistics.
+
+<a class="prompt prompt-info">Inspect the _capri_clt.tsv_ file</a>
+
+<details style="background-color:#DAE4E7">
+<summary>
+<i>View the pre-calculated 11_caprieval/capri_clt.tsv file</i> <i class="material-icons">expand_more</i>
+ </summary>
+<pre>
+cluster_rank    cluster_id      n       under_eval      score   score_std       irmsd   irmsd_std       fnat    fnat_std        lrmsd   lrmsd_std       dockq   dockq_std       air     air_std bsa     bsa_std desolv  desolv_std      elec    elec_std        total   total_std       vdw     vdw_std caprieval_rank
+1       1       4       -       -144.093        2.488   0.982   0.066   0.868   0.053   1.808   0.400   0.841   0.030   7.948   2.867   1552.338        29.521  -9.815  0.581   -516.505        19.903  -540.329        19.324  -31.772 4.032   1
+2       2       4       -       -114.725        2.332   7.857   0.537   0.160   0.060   15.260  1.442   0.145   0.031   20.796  13.446  1463.065        62.919  -15.198 1.933   -366.827        51.294  -374.272        36.604  -28.241 6.257   2
+3       3       4       -       -94.068 5.808   3.275   0.819   0.438   0.069   6.634   0.746   0.421   0.078   39.979  8.006   1355.070        46.720  -15.369 2.124   -277.523        45.985  -264.737        43.408  -27.193 3.176   3
+4       4       4       -       -90.571 9.613   7.949   0.272   0.139   0.020   13.948  0.422   0.148   0.007   11.023  13.529  1180.695        135.973 -12.646 2.946   -267.354        21.668  -281.889        19.831  -25.557 6.065   4
+</pre>
+</details>
+<br>
+
+<a class="prompt prompt-question">How many clusters are generated?</a>
+
+<a class="prompt prompt-question">Look at the score of the first few clusters: Are they significantly different if you consider their average scores and standard deviations?</a>
+
+Since for this tutorial we have at hand the crystal structure of the complex, we provided it as reference to the `caprieval` modules.
+This means that the iRMSD, lRMSD, Fnat and DockQ statistics report on the quality of the docked model compared to the reference crystal structure.
+
+<a class="prompt prompt-question">How many clusters of acceptable or better quality have been generate according to CAPRI criteria?</a>
+
+<a class="prompt prompt-question">What is the rank of the best cluster generated?</a>
+
+<a class="prompt prompt-question">What is the rank of the first acceptable of better cluster generated?</a>
+
+
+We are providing in the `scripts` directory a simple script that extract some cluster statistics for acceptable or better clusters from the `caprieval` steps.
+To use is simply call the script with as argument the run directory you want to analyze, e.g.:
+
+<a class="prompt prompt-cmd">
+   ./scripts/extract-capri-stats-clt.sh ./scenario1-surface
+</a>
+
+<details style="background-color:#DAE4E7">
+ <summary>
+  <i>View the output of the script</i> <i class="material-icons">expand_more</i>
+ </summary>
+<pre>
+==============================================
+==============================================
+== run1-full/02_caprieval/capri_clt.tsv
+==============================================
+Total number of acceptable or better clusters:  0  out of  1
+Total number of medium or better clusters:      0  out of  1
+Total number of high quality clusters:          0  out of  1
+
+First acceptable cluster - rank:   i-RMSD:   Fnat:   DockQ: 
+First medium cluster     - rank:   i-RMSD:   Fnat:   DockQ:
+Best cluster             - rank:  -  i-RMSD:  7.330  Fnat:  0.125  DockQ:  0.155      
+==============================================
+== run1-full/04_caprieval/capri_clt.tsv
+==============================================
+Total number of acceptable or better clusters:  0  out of  1
+Total number of medium or better clusters:      0  out of  1
+Total number of high quality clusters:          0  out of  1
+
+First acceptable cluster - rank:   i-RMSD:   Fnat:   DockQ: 
+First medium cluster     - rank:   i-RMSD:   Fnat:   DockQ:
+Best cluster             - rank:  -  i-RMSD:  7.330  Fnat:  0.125  DockQ:  0.155      
+==============================================
+== run1-full/06_caprieval/capri_clt.tsv
+==============================================
+Total number of acceptable or better clusters:  0  out of  1
+Total number of medium or better clusters:      0  out of  1
+Total number of high quality clusters:          0  out of  1
+
+First acceptable cluster - rank:   i-RMSD:   Fnat:   DockQ: 
+First medium cluster     - rank:   i-RMSD:   Fnat:   DockQ:
+Best cluster             - rank:  -  i-RMSD:  4.563  Fnat:  0.424  DockQ:  0.455      
+==============================================
+== run1-full/08_caprieval/capri_clt.tsv
+==============================================
+Total number of acceptable or better clusters:  1  out of  1
+Total number of medium or better clusters:      1  out of  1
+Total number of high quality clusters:          1  out of  1
+
+First acceptable cluster - rank:  -  i-RMSD:  0.982  Fnat:  0.868  DockQ:  0.841
+First medium cluster     - rank:  -  i-RMSD:  0.982  Fnat:  0.868  DockQ:  0.841      
+Best cluster             - rank:  -  i-RMSD:  0.982  Fnat:  0.868  DockQ:  0.841      
+==============================================
+== run1-full/11_caprieval/capri_clt.tsv
+==============================================
+Total number of acceptable or better clusters:  2  out of  4
+Total number of medium or better clusters:      1  out of  4
+Total number of high quality clusters:          1  out of  4
+
+First acceptable cluster - rank:  1  i-RMSD:  0.982  Fnat:  0.868  DockQ:  0.841
+First medium cluster     - rank:  1  i-RMSD:  0.982  Fnat:  0.868  DockQ:  0.841      
+Best cluster             - rank:  1  i-RMSD:  0.982  Fnat:  0.868  DockQ:  0.841      
+</pre>
+</details>
+
+<br>
+
+Similarly some simple statistics can be extracted from the single model `caprieval` `capri_ss.tsv` files with the `extract-capri-stats.sh` script:
+
+
+<a class="prompt prompt-cmd">
+   ./scripts/extract-capri-stats.sh ./runs/scenario1-surface
+</a>
+
+<details style="background-color:#DAE4E7">
+<summary>
+<i>View the output of the script:</i> <i class="material-icons">expand_more</i>
+ </summary>
+<pre>
+==============================================
+== run1-full/02_caprieval/capri_ss.tsv
+==============================================
+Total number of acceptable or better models:  351  out of  1000
+Total number of medium or better models:      201  out of  1000
+Total number of high quality models:          0  out of  1000
+
+First acceptable model - rank:  15  i-RMSD:  1.677  Fnat:  0.472  DockQ:  0.568
+First medium model     - rank:  15  i-RMSD:  1.677  Fnat:  0.472  DockQ:  0.568       
+Best model             - rank:  48  i-RMSD:  1.146  Fnat:  0.556  DockQ:  0.713       
+==============================================
+== run1-full/04_caprieval/capri_ss.tsv
+==============================================
+Total number of acceptable or better models:  138  out of  200
+Total number of medium or better models:      131  out of  200
+Total number of high quality models:          0  out of  200
+
+First acceptable model - rank:  15  i-RMSD:  1.677  Fnat:  0.472  DockQ:  0.568
+First medium model     - rank:  15  i-RMSD:  1.677  Fnat:  0.472  DockQ:  0.568       
+Best model             - rank:  48  i-RMSD:  1.146  Fnat:  0.556  DockQ:  0.713       
+==============================================
+== run1-full/06_caprieval/capri_ss.tsv
+==============================================
+Total number of acceptable or better models:  141  out of  200
+Total number of medium or better models:      123  out of  200
+Total number of high quality models:          29  out of  200
+
+First acceptable model - rank:  3  i-RMSD:  0.966  Fnat:  0.833  DockQ:  0.828
+First medium model     - rank:  3  i-RMSD:  0.966  Fnat:  0.833  DockQ:  0.828        
+Best model             - rank:  92  i-RMSD:  0.890  Fnat:  0.806  DockQ:  0.840       
+==============================================
+== run1-full/08_caprieval/capri_ss.tsv
+==============================================
+Total number of acceptable or better models:  140  out of  200
+Total number of medium or better models:      124  out of  200
+Total number of high quality models:          45  out of  200
+
+First acceptable model - rank:  1  i-RMSD:  0.894  Fnat:  0.889  DockQ:  0.866
+First medium model     - rank:  1  i-RMSD:  0.894  Fnat:  0.889  DockQ:  0.866        
+Best model             - rank:  52  i-RMSD:  0.838  Fnat:  0.861  DockQ:  0.866       
+==============================================
+== run1-full/11_caprieval/capri_ss.tsv
+==============================================
+Total number of acceptable or better models:  8  out of  16
+Total number of medium or better models:      5  out of  16
+Total number of high quality models:          2  out of  16
+
+First acceptable model - rank:  1  i-RMSD:  0.894  Fnat:  0.889  DockQ:  0.866
+First medium model     - rank:  1  i-RMSD:  0.894  Fnat:  0.889  DockQ:  0.866        
+Best model             - rank:  1  i-RMSD:  0.894  Fnat:  0.889  DockQ:  0.866        
+</pre>
+</details>
+
+<br>
+
+_**Note**_ that this kind of analysis only makes sense when we know the reference complex and for benchmarking / performance analysis purposes.
+
+<a class="prompt prompt-info">Look at the single structure statistics provided by the script</a>
+
+<a class="prompt prompt-question">How does the quality of the model changes after flexible refinement? Consider here the various metrics.</a>
+
+<details style="background-color:#DAE4E7">
+  <summary style="bold">
+    <i>Answer</i> <i class="material-icons">expand_more</i>
+  </summary>
+  <p>
+    In terms of iRMSD values we only observe very small differences in the best models, but the change in ranking is impressive!
+    The fraction of native contacts and the DockQ scores are however improving much more after flexible refinement.
+    All this will of course depend on how different are the bound and unbound conformations and the amount of data
+    used to drive the docking process. In general, from our experience, the more and better data at hand,
+    the larger the conformational changes that can be induced.
+  </p>
+  </details>
+
+<br>
+
+<a class="prompt prompt-question">Is the best model always rank as first?</a>
+
+<details style="background-color:#DAE4E7">
+  <summary style="bold">
+    <i>Answer</i> <i class="material-icons">expand_more</i>
+  </summary>
+  <p>
+    This is clearly not the case. The scoring function is not perfect, but does a reasonable job in ranking models of acceptable or better quality on top in this case.
+  </p>
+</details>
+
+<br>
+
+<hr>
+
+### Analysis scenario 2: 
+
+Let us now analyse the docking results for this scenario. Use for that either your own run or a pre-calculated run provided in the `runs` directory.
+Go into the _analysis/9_caprieval_analysis_  directory of the respective run directory and
+
+<a class="prompt prompt-info">Inspect the final cluster statistics in _capri_clt.tsv_ file </a>
+
+<details style="background-color:#DAE4E7">
+<summary>
+<i>View the pre-calculated 9_caprieval/capri_clt.tsv file</i> <i class="material-icons">expand_more</i>
+ </summary>
+<pre>
+cluster_rank    cluster_id      n       under_eval      score   score_std       irmsd   irmsd_std       fnat    fnat_std        lrmsd   lrmsd_std       dockq   dockq_std       air     air_std bsa     bsa_std desolv  desolv_std      elec    elec_std        total   total_std       vdw     vdw_std caprieval_rank
+1       1       4       -       -152.266        6.827   1.063   0.088   0.875   0.046   1.919   0.354   0.831   0.030   9.789   4.782   1666.013        73.943  -14.186 2.889   -451.427        36.126  -490.412        39.908  -48.774 6.566   1
+2       2       4       -       -118.927        4.544   8.218   0.226   0.084   0.034   16.077  0.481   0.111   0.014   15.749  9.293   1446.352        88.238  -11.992 2.298   -382.010        16.477  -398.369        16.682  -32.108 1.977   2
+3       3       4       -       -92.965 7.490   3.203   0.522   0.416   0.027   6.760   0.336   0.406   0.024   18.864  13.757  1293.035        117.159 -18.657 4.710   -234.961        19.534  -245.298        24.082  -29.202 6.667   3
+4       4       4       -       -88.999 5.805   7.961   0.279   0.146   0.023   13.985  0.473   0.150   0.003   5.901   6.214   1224.918        78.277  -15.469 3.235   -212.758        22.313  -238.425        21.362  -31.568 4.338   4
+5       5       4       -       -79.140 9.087   3.194   1.314   0.312   0.135   6.154   1.904   0.406   0.135   24.820  15.208  1180.793        111.786 -14.802 4.462   -230.060        40.784  -226.049        33.401  -20.809 3.137   5
+</pre>
+</details>
+
+<br>
+
+<a class="prompt prompt-question">How many clusters are generated?</a>
+
+<a class="prompt prompt-question">Look at the score of the first few clusters: Are they significantly different if you consider their average scores and standard deviations?</a>
+
+Since for this tutorial we have at hand the crystal structure of the complex, we provided it as reference to the `caprieval` modules.
+This means that the iRMSD, lRMSD, Fnat and DockQ statistics report on the quality of the docked model compared to the reference crystal structure.
+
+<a class="prompt prompt-question">How many clusters of acceptable or better quality have been generate according to CAPRI criteria?</a>
+
+<a class="prompt prompt-question">What is the rank of the best cluster generated?</a>
+
+<a class="prompt prompt-question">What is the rank of the first acceptable of better cluster generated?</a>
+
+
+We are providing in the `scripts` a simple script that extract some cluster statistics for acceptable or better clusters from the `caprieval` steps.
+To use is simply call the script with as argument the run directory you want to analyze, e.g.:
+
+<a class="prompt prompt-cmd">
+./scripts/extract-capri-stats-clt.sh ./run2-mdref-full
+</a>
+
+<details style="background-color:#DAE4E7">
+<summary>
+<i>View the output of the script</i> <i class="material-icons">expand_more</i>
+ </summary>
+<pre>
+==============================================
+== run2-full/02_caprieval/capri_clt.tsv
+==============================================
+Total number of acceptable or better clusters:  0  out of  1
+Total number of medium or better clusters:      0  out of  1
+Total number of high quality clusters:          0  out of  1
+
+First acceptable cluster - rank:   i-RMSD:   Fnat:   DockQ: 
+First medium cluster     - rank:   i-RMSD:   Fnat:   DockQ:
+Best cluster             - rank:  -  i-RMSD:  7.330  Fnat:  0.125  DockQ:  0.155      
+==============================================
+== run2-full/04_caprieval/capri_clt.tsv
+==============================================
+Total number of acceptable or better clusters:  0  out of  1
+Total number of medium or better clusters:      0  out of  1
+Total number of high quality clusters:          0  out of  1
+
+First acceptable cluster - rank:   i-RMSD:   Fnat:   DockQ: 
+First medium cluster     - rank:   i-RMSD:   Fnat:   DockQ:
+Best cluster             - rank:  -  i-RMSD:  7.330  Fnat:  0.125  DockQ:  0.155      
+==============================================
+== run2-full/06_caprieval/capri_clt.tsv
+==============================================
+Total number of acceptable or better clusters:  0  out of  1
+Total number of medium or better clusters:      0  out of  1
+Total number of high quality clusters:          0  out of  1
+
+First acceptable cluster - rank:   i-RMSD:   Fnat:   DockQ: 
+First medium cluster     - rank:   i-RMSD:   Fnat:   DockQ:
+Best cluster             - rank:  -  i-RMSD:  4.563  Fnat:  0.424  DockQ:  0.455      
+==============================================
+== run2-full/08_caprieval/capri_clt.tsv
+==============================================
+Total number of acceptable or better clusters:  1  out of  1
+Total number of medium or better clusters:      1  out of  1
+Total number of high quality clusters:          0  out of  1
+
+First acceptable cluster - rank:  -  i-RMSD:  1.063  Fnat:  0.875  DockQ:  0.831
+First medium cluster     - rank:  -  i-RMSD:  1.063  Fnat:  0.875  DockQ:  0.831      
+Best cluster             - rank:  -  i-RMSD:  1.063  Fnat:  0.875  DockQ:  0.831      
+==============================================
+== run2-full/11_caprieval/capri_clt.tsv
+==============================================
+Total number of acceptable or better clusters:  3  out of  5
+Total number of medium or better clusters:      1  out of  5
+Total number of high quality clusters:          0  out of  5
+
+First acceptable cluster - rank:  1  i-RMSD:  1.063  Fnat:  0.875  DockQ:  0.831
+First medium cluster     - rank:  1  i-RMSD:  1.063  Fnat:  0.875  DockQ:  0.831      
+Best cluster             - rank:  1  i-RMSD:  1.063  Fnat:  0.875  DockQ:  0.831      
+</pre>
+</details>
+
+<br>
+
+Similarly some simple statistics can be extracted from the single model `caprieval` `capri_ss.tsv` files with the `extract-capri-stats.sh` script:
+
+<a class="prompt prompt-cmd">
+./scripts/extract-capri-stats.sh ./run2-mdref-full
+</a>
+
+<details style="background-color:#DAE4E7">
+<summary>
+<i>View the output of the script</i> <i class="material-icons">expand_more</i>
+ </summary>
+<pre>
+==============================================
+== run2-full/02_caprieval/capri_ss.tsv
+==============================================
+Total number of acceptable or better models:  351  out of  1000
+Total number of medium or better models:      201  out of  1000
+Total number of high quality models:          0  out of  1000
+
+First acceptable model - rank:  15  i-RMSD:  1.677  Fnat:  0.472  DockQ:  0.568
+First medium model     - rank:  15  i-RMSD:  1.677  Fnat:  0.472  DockQ:  0.568       
+Best model             - rank:  48  i-RMSD:  1.146  Fnat:  0.556  DockQ:  0.713       
+==============================================
+== run2-full/04_caprieval/capri_ss.tsv
+==============================================
+Total number of acceptable or better models:  138  out of  200
+Total number of medium or better models:      131  out of  200
+Total number of high quality models:          0  out of  200
+
+First acceptable model - rank:  15  i-RMSD:  1.677  Fnat:  0.472  DockQ:  0.568
+First medium model     - rank:  15  i-RMSD:  1.677  Fnat:  0.472  DockQ:  0.568       
+Best model             - rank:  48  i-RMSD:  1.146  Fnat:  0.556  DockQ:  0.713       
+==============================================
+== run2-full/06_caprieval/capri_ss.tsv
+==============================================
+Total number of acceptable or better models:  141  out of  200
+Total number of medium or better models:      123  out of  200
+Total number of high quality models:          29  out of  200
+
+First acceptable model - rank:  3  i-RMSD:  0.966  Fnat:  0.833  DockQ:  0.828
+First medium model     - rank:  3  i-RMSD:  0.966  Fnat:  0.833  DockQ:  0.828        
+Best model             - rank:  92  i-RMSD:  0.890  Fnat:  0.806  DockQ:  0.840       
+==============================================
+== run2-full/08_caprieval/capri_ss.tsv
+==============================================
+Total number of acceptable or better models:  140  out of  200
+Total number of medium or better models:      123  out of  200
+Total number of high quality models:          16  out of  200
+
+First acceptable model - rank:  1  i-RMSD:  1.096  Fnat:  0.917  DockQ:  0.836
+First medium model     - rank:  1  i-RMSD:  1.096  Fnat:  0.917  DockQ:  0.836        
+Best model             - rank:  105  i-RMSD:  0.909  Fnat:  0.806  DockQ:  0.836      
+==============================================
+== run2-full/11_caprieval/capri_ss.tsv
+==============================================
+Total number of acceptable or better models:  10  out of  20
+Total number of medium or better models:      5  out of  20
+Total number of high quality models:          1  out of  20
+
+First acceptable model - rank:  1  i-RMSD:  1.096  Fnat:  0.917  DockQ:  0.836
+First medium model     - rank:  1  i-RMSD:  1.096  Fnat:  0.917  DockQ:  0.836        
+Best model             - rank:  4  i-RMSD:  0.929  Fnat:  0.917  DockQ:  0.870        
+</pre>
+</details>
+<br>
+
+_**Note**_ that this kind of analysis only makes sense when we know the reference complex and for benchmarking / performance analysis purposes.
+
+<a class="prompt prompt-info">Look at the single structure statistics provided by the script</a>
+
+<a class="prompt prompt-question">How does the quality of the model changes after flexible refinement? Consider here the various metrics.</a>
+
+<details style="background-color:#DAE4E7">
+  <summary style="bold">
+    <i>Answer</i> <i class="material-icons">expand_more</i>
+  </summary>
+  <p>
+    In terms of iRMSD values we only observe very small differences with a slight increase.
+    The fraction of native contacts and the DockQ scores are however improving much more after flexible refinement.
+    All this will of course depend on how different are the bound and unbound conformations and the amount of data
+    used to drive the docking process. In general, from our experience, the more and better data at hand,
+    the larger the conformational changes that can be induced.
+  </p>
+</details>
+<br>
+
+<a class="prompt prompt-question">Is the best model always rank as first?</a>
+
+<details style="background-color:#DAE4E7">
+  <summary style="bold">
+    <i>Answer</i> <i class="material-icons">expand_more</i>
+  </summary>
+  <p>
+    This is clearly not the case. The scoring function is not perfect, but does a reasonable job in ranking models of acceptable or better quality on top in this case.
+  </p>
+</details>
+
+<br>
+
+<hr>
+
+### Analysis scenario 3: Paratope - NMR-epitope as active
+
+Let us now analyse the docking results for this scenario. Use for that either your own run or a pre-calculated run provided in the `runs` directory.
+Go into the _analysis/9_caprieval_analysis_  directory of the respective run directory and
+
+<a class="prompt prompt-info">Inspect the final cluster statistics in _capri_clt.tsv_ file </a>
+
+<details style="background-color:#DAE4E7">
+<summary>
+<i>View the pre-calculated 9_caprieval/capri_clt.tsv file</i> <i class="material-icons">expand_more</i>
+ </summary>
+<pre>
+cluster_rank    cluster_id      n       under_eval      score   score_std       irmsd   irmsd_std       fnat    fnat_std        lrmsd   lrmsd_std       dockq   dockq_std       air     air_std bsa     bsa_std desolv  desolv_std      elec    elec_std        total   total_std       vdw     vdw_std caprieval_rank
+1       1       4       -       -124.146        3.141   1.022   0.113   0.785   0.074   1.812   0.477   0.808   0.045   19.088  8.507   1493.348        101.980 -14.374 2.868   -390.668        44.141  -405.127        39.003  -33.547 7.112   1
+2       2       4       -       -109.733        4.447   8.384   0.538   0.153   0.063   15.962  0.969   0.135   0.029   64.065  29.996  1461.225        113.842 -13.164 2.827   -394.903        13.092  -354.834        34.074  -23.996 4.254   2
+3       6       4       -       -105.989        3.889   4.022   0.232   0.243   0.050   6.572   0.337   0.331   0.025   38.555  17.146  1385.205        39.561  -6.273  3.174   -425.420        56.558  -405.353        38.939  -18.487 5.586   3
+4       15      4       -       -104.603        12.655  2.611   0.503   0.528   0.059   6.425   1.299   0.477   0.067   85.213  45.158  1356.690        121.841 -8.303  1.220   -373.493        63.970  -318.403        31.234  -30.123 7.143   4
+5       4       4       -       -104.598        8.865   10.825  0.034   0.132   0.012   18.111  0.347   0.110   0.004   30.421  16.727  1598.340        70.785  -14.415 3.302   -271.984        24.921  -280.391        29.442  -38.828 10.416  5
+6       5       4       -       -101.889        2.338   9.402   0.193   0.104   0.030   16.057  0.690   0.116   0.009   64.991  18.462  1450.140        29.114  -5.857  3.187   -315.007        37.319  -289.547        33.841  -39.530 5.579   6
+7       11      4       -       -99.698 1.987   10.559  0.095   0.056   0.019   18.576  0.518   0.083   0.006   54.563  22.794  1569.310        56.647  -8.234  1.467   -295.063        14.691  -278.408        32.855  -37.908 3.927   7
+8       3       4       -       -96.631 7.773   7.819   1.263   0.083   0.019   13.394  2.088   0.140   0.028   56.501  8.286   1374.160        140.711 -12.846 3.790   -310.567        43.021  -281.388        40.284  -27.321 13.323  8
+9       16      4       -       -92.523 3.297   3.663   0.302   0.292   0.024   11.379  1.400   0.267   0.027   54.758  29.513  1501.015        38.489  -11.325 1.019   -272.691        16.590  -250.069        27.109  -32.135 4.675   9
+...
+</pre>
+</details>
+
+<a class="prompt prompt-question">How many clusters of acceptable or better quality have been generate according to CAPRI criteria?</a>
+
+<a class="prompt prompt-question">What is the rank of the best cluster generated?</a>
+
+<a class="prompt prompt-question">What is the rank of the first acceptable of better cluster generated?</a>
+
+
+In this run we also had a `caprieval` after the clustering of the rigid body models (step 5 of our workflow).
+
+<a class="prompt prompt-info">Inspect the corresponding _capri_clt.tsv_ file</a>
+
+<details style="background-color:#DAE4E7">
+<summary>
+<i>View the pre-calculated 5_caprieval/capri_clt.tsv file</i> <i class="material-icons">expand_more</i>
+ </summary>
+<pre>
+luster_rank    cluster_id      n       under_eval      score   score_std       irmsd   irmsd_std       fnat    fnat_std        lrmsd   lrmsd_std       dockq   dockq_std       air     air_std bsa     bsa_std desolv  desolv_std      elec    elec_std        total   total_std       vdw     vdw_std caprieval_rank
+1       4       20      -       -32.856 0.533   8.410   0.035   0.056   0.000   16.588  0.218   0.098   0.002   124.256 24.532  1065.697        38.144  -16.689 0.709   -6.744  0.291   116.646 27.428  -0.865  3.459   1
+2       18      7       -       -32.414 0.110   4.179   0.038   0.306   0.019   7.728   0.105   0.323   0.005   202.292 47.410  1058.705        25.612  -16.835 0.581   -6.903  0.482   184.302 52.002  -11.087 4.822   2
+3       11      15      -       -31.515 0.233   1.350   0.190   0.514   0.024   3.046   0.805   0.651   0.048   78.437  33.653  1145.055        41.997  -12.978 0.175   -7.768  1.150   60.382  39.543  -10.287 7.148   3
+4       1       20      -       -31.236 0.236   1.284   0.040   0.556   0.000   2.864   0.281   0.677   0.011   131.928 45.555  1219.428        39.426  -13.313 0.388   -6.916  0.117   111.705 48.278  -13.307 3.521   4
+5       21      7       -       -31.142 1.942   8.839   0.522   0.049   0.012   16.884  0.430   0.093   0.007   95.692  53.937  893.915 67.400  -15.663 3.094   -7.449  1.286   83.407  60.857  -4.835  6.370   5
+6       5       20      -       -29.475 0.296   7.493   0.661   0.076   0.012   12.982  1.199   0.140   0.013   106.772 69.264  981.186 49.018  -12.258 0.686   -8.331  0.482   84.322  76.530  -14.119 7.722   6
+7       26      5       -       -28.704 0.755   3.229   0.706   0.327   0.041   6.579   0.763   0.383   0.055   83.999  32.448  1017.403        165.642 -13.274 2.333   -5.973  1.267   65.694  38.740  -12.333 6.890   7
+8       2       20      -       -28.470 0.706   4.247   0.060   0.118   0.012   7.243   0.105   0.270   0.007   153.547 91.575  1000.080        27.610  -11.114 0.595   -8.809  0.321   136.608 96.503  -8.130  5.374   8
+9       33      4       -       -26.485 0.685   10.716  0.057   0.076   0.012   18.370  0.215   0.091   0.004   186.693 110.722 996.022 75.935  -13.582 1.714   -4.750  0.313   175.985 115.674 -5.959  5.733   9
+...
+</pre>
+</details>
+<br>
+
+<a class="prompt prompt-question">How many clusters are generated?</a>
+
+<a class="prompt prompt-question">Is this the same number that after refinement (see above)?</a>
+
+<a class="prompt prompt-question">If not what could be the reason?</a>
+
+<a class="prompt prompt-question">Consider again the rank of the first acceptable cluster based on iRMSD values. How does this compare with the refined clusters (see above)?</a>
+
+<details style="background-color:#DAE4E7">
+  <summary style="bold">
+    <i>Answer </i> <i class="material-icons">expand_more</i>
+  </summary>
+  <p>
+    After rigid body docking the first acceptable cluster is at rank 3 and the same is true after refinement, but the iRMSD values have improved.
+  </p>
+</details>
+
+<br>
+
+Use the `extract-capri-stats-clt.sh` script to extract some simple cluster statistics for this run.
+
+<a class="prompt prompt-cmd">
+   ./scripts/extract-capri-stats-clt.sh runs/scenario2b-NMR-epitope-act/
+</a>
+
+
+<details style="background-color:#DAE4E7">
+<summary>
+  <i>View the output of the script </i> <i class="material-icons">expand_more</i>
+ </summary>
+<pre>
+==============================================
+== run3-full/02_caprieval/capri_clt.tsv
+==============================================
+Total number of acceptable or better clusters:  0  out of  1
+Total number of medium or better clusters:      0  out of  1
+Total number of high quality clusters:          0  out of  1
+
+First acceptable cluster - rank:   i-RMSD:   Fnat:   DockQ: 
+First medium cluster     - rank:   i-RMSD:   Fnat:   DockQ:
+Best cluster             - rank:  -  i-RMSD:  7.330  Fnat:  0.125  DockQ:  0.155      
+==============================================
+== run3-full/05_caprieval/capri_clt.tsv
+==============================================
+Total number of acceptable or better clusters:  7  out of  33
+Total number of medium or better clusters:      2  out of  33
+Total number of high quality clusters:          0  out of  33
+
+First acceptable cluster - rank:  3  i-RMSD:  1.350  Fnat:  0.514  DockQ:  0.651
+First medium cluster     - rank:  3  i-RMSD:  1.350  Fnat:  0.514  DockQ:  0.651      
+Best cluster             - rank:  4  i-RMSD:  1.284  Fnat:  0.556  DockQ:  0.677      
+==============================================
+== run3-full/07_caprieval/capri_clt.tsv
+==============================================
+Total number of acceptable or better clusters:  0  out of  1
+Total number of medium or better clusters:      0  out of  1
+Total number of high quality clusters:          0  out of  1
+
+First acceptable cluster - rank:   i-RMSD:   Fnat:   DockQ: 
+First medium cluster     - rank:   i-RMSD:   Fnat:   DockQ:
+Best cluster             - rank:  -  i-RMSD:  7.761  Fnat:  0.118  DockQ:  0.135      
+==============================================
+== run3-full/09_caprieval/capri_clt.tsv
+==============================================
+Total number of acceptable or better clusters:  1  out of  1
+Total number of medium or better clusters:      1  out of  1
+Total number of high quality clusters:          0  out of  1
+
+First acceptable cluster - rank:  -  i-RMSD:  1.022  Fnat:  0.785  DockQ:  0.808
+First medium cluster     - rank:  -  i-RMSD:  1.022  Fnat:  0.785  DockQ:  0.808      
+Best cluster             - rank:  -  i-RMSD:  1.022  Fnat:  0.785  DockQ:  0.808      
+==============================================
+== run3-full/12_caprieval/capri_clt.tsv
+==============================================
+Total number of acceptable or better clusters:  6  out of  23
+Total number of medium or better clusters:      2  out of  23
+Total number of high quality clusters:          0  out of  23
+
+First acceptable cluster - rank:  1  i-RMSD:  1.022  Fnat:  0.785  DockQ:  0.808
+First medium cluster     - rank:  1  i-RMSD:  1.022  Fnat:  0.785  DockQ:  0.808      
+Best cluster             - rank:  1  i-RMSD:  1.022  Fnat:  0.785  DockQ:  0.808      
+</pre>
+</details>
+
+<br>
+
+Similarly some simple statistics can be extracted from the single model `caprieval` `capri_ss.tsv` files with the `extract-capri-stats.sh` script:
+
+<a class="prompt prompt-cmd">
+./scripts/extract-capri-stats.sh ./runs/scenario2b-NMR-epitope-pass
+</a>
+
+<details style="background-color:#DAE4E7">
+<summary>
+<i>View the output of the script</i> <i class="material-icons">expand_more</i>
+ </summary>
+<pre>
+==============================================
+== run3-full/02_caprieval/capri_ss.tsv
+==============================================
+Total number of acceptable or better models:  351  out of  1000
+Total number of medium or better models:      201  out of  1000
+Total number of high quality models:          0  out of  1000
+
+First acceptable model - rank:  15  i-RMSD:  1.677  Fnat:  0.472  DockQ:  0.568
+First medium model     - rank:  15  i-RMSD:  1.677  Fnat:  0.472  DockQ:  0.568       
+Best model             - rank:  48  i-RMSD:  1.146  Fnat:  0.556  DockQ:  0.713       
+==============================================
+== run3-full/05_caprieval/capri_ss.tsv
+==============================================
+Total number of acceptable or better models:  64  out of  351
+Total number of medium or better models:      30  out of  351
+Total number of high quality models:          0  out of  351
+
+First acceptable model - rank:  15  i-RMSD:  1.677  Fnat:  0.472  DockQ:  0.568
+First medium model     - rank:  15  i-RMSD:  1.677  Fnat:  0.472  DockQ:  0.568       
+Best model             - rank:  47  i-RMSD:  1.146  Fnat:  0.556  DockQ:  0.713       
+==============================================
+== run3-full/07_caprieval/capri_ss.tsv
+==============================================
+Total number of acceptable or better models:  79  out of  351
+Total number of medium or better models:      36  out of  351
+Total number of high quality models:          5  out of  351
+
+First acceptable model - rank:  5  i-RMSD:  1.009  Fnat:  0.778  DockQ:  0.808
+First medium model     - rank:  5  i-RMSD:  1.009  Fnat:  0.778  DockQ:  0.808        
+Best model             - rank:  15  i-RMSD:  0.948  Fnat:  0.722  DockQ:  0.801       
+==============================================
+== run3-full/09_caprieval/capri_ss.tsv
+==============================================
+Total number of acceptable or better models:  81  out of  351
+Total number of medium or better models:      37  out of  351
+Total number of high quality models:          7  out of  351
+
+First acceptable model - rank:  1  i-RMSD:  1.011  Fnat:  0.861  DockQ:  0.835
+First medium model     - rank:  1  i-RMSD:  1.011  Fnat:  0.861  DockQ:  0.835        
+Best model             - rank:  4  i-RMSD:  0.927  Fnat:  0.778  DockQ:  0.827        
+==============================================
+== run3-full/12_caprieval/capri_ss.tsv
+==============================================
+Total number of acceptable or better models:  25  out of  92
+Total number of medium or better models:      8  out of  92
+Total number of high quality models:          2  out of  92
+
+First acceptable model - rank:  1  i-RMSD:  1.011  Fnat:  0.861  DockQ:  0.835
+First medium model     - rank:  1  i-RMSD:  1.011  Fnat:  0.861  DockQ:  0.835        
+Best model             - rank:  4  i-RMSD:  0.927  Fnat:  0.778  DockQ:  0.827        
+</pre>
+</details>
+
+<br>
+
+_**Note**_ that this kind of analysis only makes sense when we know the reference complex and for benchmarking / performance analysis purposes.
+
+<a class="prompt prompt-info">Look at the single structure statistics provided by the script</a>
+
+<a class="prompt prompt-question">How does the quality of the model changes after flexible refinement? Consider here the various metrics.</a>
+
+<details style="background-color:#DAE4E7">
+  <summary style="bold">
+    <i>Answer</i> <i class="material-icons">expand_more</i>
+  </summary>
+  <p>
+    In this case we observe a small improvement in terms of iRMSD values as well as in the fraction of native contacts and the DockQ scores. Also the single model rankings have improved, but the top ranked model is not the best one.
+  </p>
+</details>
+
+<br>
+
+<a class="prompt prompt-question">Is the best model always rank as first?</a>
+
+<details style="background-color:#DAE4E7">
+  <summary style="bold">
+    <i>Answer</i> <i class="material-icons">expand_more</i>
+  </summary>
+  <p>
+  This is clearly not the case. The scoring function is not perfect, but does a reasonable job in ranking models of acceptable or better quality on top in this case.
+  </p>
+</details>
+
+<br>
+
+<hr>
+
+### Comparing the performance of the three scenarios
+
+Clearly all three scenarios give good results with an acceptable cluster in all three cases ranked at the top:
+
+{% highlight shell %}
+==============================================
+== run1-full/11_caprieval/capri_ss.tsv
+==============================================
+Total number of acceptable or better models:  8  out of  16
+Total number of medium or better models:      5  out of  16
+Total number of high quality models:          2  out of  16
+
+First acceptable model - rank:  1  i-RMSD:  0.894  Fnat:  0.889  DockQ:  0.866
+First medium model     - rank:  1  i-RMSD:  0.894  Fnat:  0.889  DockQ:  0.866        
+Best model             - rank:  1  i-RMSD:  0.894  Fnat:  0.889  DockQ:  0.866        
+
+==============================================
+== run2-full/11_caprieval/capri_ss.tsv
+==============================================
+Total number of acceptable or better models:  10  out of  20
+Total number of medium or better models:      5  out of  20
+Total number of high quality models:          1  out of  20
+
+First acceptable model - rank:  1  i-RMSD:  1.096  Fnat:  0.917  DockQ:  0.836
+First medium model     - rank:  1  i-RMSD:  1.096  Fnat:  0.917  DockQ:  0.836        
+Best model             - rank:  4  i-RMSD:  0.929  Fnat:  0.917  DockQ:  0.870        
+
+==============================================
+== run3-full/12_caprieval/capri_ss.tsv
+==============================================
+Total number of acceptable or better models:  25  out of  92
+Total number of medium or better models:      8  out of  92
+Total number of high quality models:          2  out of  92
+
+First acceptable model - rank:  1  i-RMSD:  1.011  Fnat:  0.861  DockQ:  0.835
+First medium model     - rank:  1  i-RMSD:  1.011  Fnat:  0.861  DockQ:  0.835        
+Best model             - rank:  4  i-RMSD:  0.927  Fnat:  0.778  DockQ:  0.827        
+
+{% endhighlight %}
+
+While the three scerios show similar results, the combination of the flexible and energy minimization refinment has the most acceptable or better models.
+
+<hr>
+<hr>
+
+## Biological insights
+
+The E2A-HPR complex is involved in phosphate-transfer, in which a phosphate group attached to histidine 90 of E2A (which we named NEP) is transferred to a histidine of HPR. As such, the docking models should make sense according to this information, meaning that two histidines should be in close proximity at the interface. Using PyMOL, check the various cluster representatives (we are assuming here you have performed all PyMOL commands of the previous section):
+
+<a class="prompt prompt-pymol">
+select histidines, resn HIS+NEP<br>
+show spheres, histidines<br>
+util.cnc<br>
+</a>
+
+<a class="prompt prompt-question">First of all, has the phosphate group been properly generated?</a>
+
+**Note:** You can zoom on the phosphorylated histidine using the following PyMOL command:
+
+<a class="prompt prompt-pymol">
+zoom resn NEP<br>
+</a>
+
+<figure align="center">
+<img src="/education/HADDOCK24/HADDOCK24-protein-protein-basic/phosphorylated-histidine.png">
+</figure>
+
+Zoom back to all visible molecules with
+
+<a class="prompt prompt-pymol">
+zoom vis<br>
+</a>
+
+Now inspect each cluster in turn and check if histidine 90 of E2A is in close proximity to another histidine of HPR.
+To facilitate this analysis, view each cluster in turn (use the right panel to activate/desactivate the various clusters by clicking on their name).
+
+<a class="prompt prompt-question">Based on this analysis, which cluster does satisfy best the biolocal information?</a>
+
+<a class="prompt prompt-question">Is this cluster also the best ranked one?</a>
+
+<hr>
+
+## Comparison with the reference structure
+
+As explained in the introduction, the structure of the native complex has been determined by NMR (PDB ID [1GGR](https://www.ebi.ac.uk/pdbe/entry/pdb/1ggr){:target="_blank"}) using a combination of intermolecular NOEs and dipolar coupling restraints. We will now compare the docking models with this structure.
+
+If you still have all cluster representative open in PyMOL you can proceed with the sub-sequent analysis, otherwise load again each cluster representative as described above. Then, fetch the reference complex by typing in PyMOL:
+
+<a class="prompt prompt-pymol">
+fetch 1GGR<br>
+show cartoon<br>
+color yellow, 1GGR and chain A<br>
+color orange, 1GGR and chain B<br>
+</a>
+
+The number of chain B in this structure is however different from the HPR numbering in the structure we used: It starts at 301 while in our models chain B starts at 1. We can change the residue numbering easily in PyMol with the following command:
+
+<a class="prompt prompt-pymol">
+alter (chain B and 1GGR), resv -=300<br>
+</a>
+
+Then superimpose all cluster representatives on the reference structure, using the entire chain A (E2A):
+
+<a class="prompt prompt-pymol">
+select 1GGR and chain A<br>
+alignto sele<br>
+</a>
+
+<a class="prompt prompt-question">
+Does any of the cluster representatives ressemble the reference NMR structure?
+</a>
+<a class="prompt prompt-question">
+In case you found a reasonable prediction, what is its cluster rank?
+</a>
+
+<hr>
+<hr>
+
+## Congratulations! 🎉
+
+You have completed this tutorial. If you have any questions or suggestions, feel free to contact us via email or asking a question through our [support center](https://ask.bioexcel.eu){:target="_blank"}.
+
+And check also our [education](/education) web page where you will find more tutorials!
+
+<hr>
+<hr>
+
+## A look into the future Virtual Research Environment for HADDOCK3
+
+In the context of a project with the [Netherlands e-Science Center](https://www.esciencecenter.nl){:target="_blank"} we are working on
+building a Virtual Research Environment (VRE) for HADDOCK3 that will allow you to build and edit custom workflows,
+execute those on a variety of infrastructures (grid, cloud, local, HPC) and provide an interactive analysis
+platform for analyzing your HADDOCK3 results. This is _work in progress_ but you can already take a glimpse of the
+first component, the workflow builder, [here](https://github.com/i-VRESSE/workflow-builder){:target="_blank"}.
+
+All the HADDOCK3 VRE software development is open and can be followed from our [GitHub i-VRESSE](https://github.com/i-VRESSE){:target="_blank"} repository.
+
+So stay tuned!
+
+<!-- Links -->
+[air-help]: https://www.bonvinlab.org/software/haddock2.4/airs/ "AIRs help"
+[gentbl]: https://wenmr.science.uu.nl/gentbl/ "GenTBL"
+[haddock24protein]: /education/HADDOCK24/HADDOCK24-protein-protein-basic/
+[haddock-repo]: https://github.com/haddocking/haddock3 "HADDOCK3 GitHub"
+[haddock-tools]: https://github.com/haddocking/haddock-tools "HADDOCK tools GitHub"
+[installation]: https://www.bonvinlab.org/haddock3/INSTALL.html "Installation"
+[link-cns]: https://cns-online.org "CNS online"
+[link-forum]: https://ask.bioexcel.eu/c/haddock "HADDOCK Forum"
+[link-freesasa]: https://freesasa.github.io "FreeSASA"
+[link-pdbtools]:http://www.bonvinlab.org/pdb-tools/ "PDB-Tools"
+[link-pymol]: https://www.pymol.org/ "PyMOL"
+[nat-pro]: https://www.nature.com/nprot/journal/v5/n5/abs/nprot.2010.32.html "Nature protocol"
+[tbl-examples]: https://github.com/haddocking/haddock-tools/tree/master/haddock_tbl_validation "tbl examples"

From 6b9aa88f53c36f79da2572509e8a28d79fc6a04c Mon Sep 17 00:00:00 2001
From: Raphaelle Versini <rapha.versini@gmail.com>
Date: Mon, 26 Aug 2024 20:37:21 +0200
Subject: [PATCH 02/15] tutorial images

---
 .../HADDOCK2-stages.png                        | Bin 0 -> 128392 bytes
 .../HADDOCK3-workflow-scheme.png               | Bin 0 -> 583020 bytes
 .../e2a-surface-airs.png                       | Bin 0 -> 28052 bytes
 .../hpr-ensemble.png                           | Bin 0 -> 25189 bytes
 .../phosphorylated-histidine.png               | Bin 0 -> 5414 bytes
 5 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 education/HADDOCK3/HADDOCK3-protein-protein-basic/HADDOCK2-stages.png
 create mode 100644 education/HADDOCK3/HADDOCK3-protein-protein-basic/HADDOCK3-workflow-scheme.png
 create mode 100644 education/HADDOCK3/HADDOCK3-protein-protein-basic/e2a-surface-airs.png
 create mode 100644 education/HADDOCK3/HADDOCK3-protein-protein-basic/hpr-ensemble.png
 create mode 100644 education/HADDOCK3/HADDOCK3-protein-protein-basic/phosphorylated-histidine.png

diff --git a/education/HADDOCK3/HADDOCK3-protein-protein-basic/HADDOCK2-stages.png b/education/HADDOCK3/HADDOCK3-protein-protein-basic/HADDOCK2-stages.png
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HcmV?d00001


From fbaf22b283a02b531cb357fb215477c6ee8cc65c Mon Sep 17 00:00:00 2001
From: Raphaelle Versini <rapha.versini@gmail.com>
Date: Tue, 27 Aug 2024 00:49:10 +0200
Subject: [PATCH 03/15] correction of the scenarios description

---
 .../HADDOCK3-protein-protein-basic/index.md   | 21 +++++++------------
 1 file changed, 7 insertions(+), 14 deletions(-)

diff --git a/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md b/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md
index cdba565bf..a07ca49b8 100644
--- a/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md
+++ b/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md
@@ -700,9 +700,9 @@ In this execution mode the HADDOCK3 job should be submitted to the batch system
 
 ### Scenario 1: 1000 rigidbody docking models, selection of top200 and flexible refinement + EM of those
 
-Now that we have all data ready, and know about execution modes of HADDOCK3 it is time to setup the docking for the first scenario in which we will use the paratope on the antibody to guide the docking, targeting the entire surface of the antibody. The restraint file to use for this is `e2a-hpr_air.tbl`. We proceed to produce 1000 rigidbody docking models, from which 200 will be selected and refined through flexible refinment and energy minimization. [DESCRIBE BETTER]. 
+Now that we have all data ready, and know about execution modes of HADDOCK3 it is time to setup the docking for the first scenario in which we will use the paratope on the antibody to guide the docking, targeting the entire surface of the antibody. The restraint file to use for this is `e2a-hpr_air.tbl`. We proceed to produce 1000 rigidbody docking models, from which 200 will be selected and refined through flexible refinment and energy minimization. 
 For the analysis following the docking results, we are using the solved complex [1GGR](https://www.rcsb.org/structure/1GGR), named e2a-hpr_1GGR.pdb.
-The configuration file for this scenario (assuming a local running mode, eventually submitted to the batch system requesting a full node) is:
+The configuration file for this scenario (assuming a batch running mode) is:
 
 {% highlight toml %}
 # ====================================================================
@@ -785,16 +785,12 @@ This configuration file is provided in the `haddock3` directory of the downloade
 If you have everything ready, you can launch haddock3 either from the command line, or, better,
 submitting it to the batch system requesting in this local run mode a full node (see local execution mode above).
 
-_**Note**_ that this scenario is computationally more expensive because of the increased sampling.
-On our own cluster, running in batch mode took 12 minutes and 37 seconds.
-Running in MPI mode with 250 cores on AMD EPYC 7451 processors took [TO BE TESTED].
-The same run on a single node using all 96 threads took on the same architecture [TO BE TESTED].
-
 <hr>
 
 ### Scenario 2: 1000 rigidbody docking models, selection of top200 and flexible refinement + final refinement in explicit solvent (water) of those 
 
-In scenario 2 we are setting up the docking in which [DESCRIBE BETTER]. 
+In scenario 2, we proceed to produce 1000 rigidbody docking models, from which 200 will be selected and refined through flexible refinment then water refinment. 
+For the analysis following the docking results, we are using the solved complex [1GGR](https://www.rcsb.org/structure/1GGR), named e2a-hpr_1GGR.pdb.
 The configuration file for this scenario (assuming a batch running mode) is:
 
 {% highlight toml %}
@@ -879,15 +875,12 @@ This configuration is provided in the `haddock3` directory of the downloaded dat
 
 If you have everything ready, you can launch haddock3 either from the command line, or, better, submitting it to the batch system requesting in this local run mode a full node (see local execution mode above).
 
-_**Note**_ On our own cluster, running in MPI mode with 250 cores on AMD EPYC 7451 processors the run completed in about [TO BE COMPLETED]. The same run on a single node using all 96 threads took on the same architecture about [TO BE COMPLETED]. In `batch` mode, using 100 queue slots and 10 models per job, the same run completed in about [TO BE COMPLETED].
-
-On the Fugaku supercomputer used for the EU ASEAN HPC school, running on a single node (48 [armv8 A64FX](https://github.com/fujitsu/A64FX){:target="_blank"} processors), this run completed in about [TO BE COMPLETED]. 
-
 <hr>
 
 ### Scenario 3: 1000 rigidbody docking models, FCC clustering and selection of max 20 models per cluster followed by flexible refinement and EM 
 
-Scenario 3 is [TO BE COMPETED]
+In scenario 3, we proceed to produce 1000 rigidbody docking models, from which we proceed to do a first clustering analysis. From the top clusters a flexible refinment then energy minization is done. 
+For the analysis following the docking results, we are using the solved complex [1GGR](https://www.rcsb.org/structure/1GGR), named e2a-hpr_1GGR.pdb.
 The configuration file for this scenario (assuming a batch running mode) is:
 
 {% highlight toml %}
@@ -1781,7 +1774,7 @@ zoom resn NEP<br>
 </a>
 
 <figure align="center">
-<img src="/education/HADDOCK24/HADDOCK24-protein-protein-basic/phosphorylated-histidine.png">
+<img src="/education/HADDOCK3/HADDOCK3-protein-protein-basic/phosphorylated-histidine.png">
 </figure>
 
 Zoom back to all visible molecules with

From 0ec811f476183f171a9b0e7d77e385d6137e1fb6 Mon Sep 17 00:00:00 2001
From: Raphaelle Versini <rapha.versini@gmail.com>
Date: Tue, 3 Sep 2024 20:15:34 +0200
Subject: [PATCH 04/15] analysis plots

---
 .../plots/scenario1/dockq_clt.html            | 23 +++++++++++++++++++
 .../plots/scenario1/dockq_score.html          | 23 +++++++++++++++++++
 .../plots/scenario1/irmsd_clt.html            | 23 +++++++++++++++++++
 .../plots/scenario1/irmsd_score.html          | 23 +++++++++++++++++++
 .../plots/scenario1/score_clt.html            | 23 +++++++++++++++++++
 .../plots/scenario2/dockq_clt.html            | 23 +++++++++++++++++++
 .../plots/scenario2/dockq_score.html          | 23 +++++++++++++++++++
 .../plots/scenario2/irmsd_clt.html            | 23 +++++++++++++++++++
 .../plots/scenario2/irmsd_score.html          | 23 +++++++++++++++++++
 .../plots/scenario2/score_clt.html            | 23 +++++++++++++++++++
 .../plots/scenario3/dockq_clt.html            | 23 +++++++++++++++++++
 .../plots/scenario3/dockq_score.html          | 23 +++++++++++++++++++
 .../plots/scenario3/irmsd_clt.html            | 23 +++++++++++++++++++
 .../plots/scenario3/irmsd_score.html          | 23 +++++++++++++++++++
 .../plots/scenario3/score_clt.html            | 23 +++++++++++++++++++
 15 files changed, 345 insertions(+)
 create mode 100644 education/HADDOCK3/HADDOCK3-protein-protein-basic/plots/scenario1/dockq_clt.html
 create mode 100644 education/HADDOCK3/HADDOCK3-protein-protein-basic/plots/scenario1/dockq_score.html
 create mode 100644 education/HADDOCK3/HADDOCK3-protein-protein-basic/plots/scenario1/irmsd_clt.html
 create mode 100644 education/HADDOCK3/HADDOCK3-protein-protein-basic/plots/scenario1/irmsd_score.html
 create mode 100644 education/HADDOCK3/HADDOCK3-protein-protein-basic/plots/scenario1/score_clt.html
 create mode 100644 education/HADDOCK3/HADDOCK3-protein-protein-basic/plots/scenario2/dockq_clt.html
 create mode 100644 education/HADDOCK3/HADDOCK3-protein-protein-basic/plots/scenario2/dockq_score.html
 create mode 100644 education/HADDOCK3/HADDOCK3-protein-protein-basic/plots/scenario2/irmsd_clt.html
 create mode 100644 education/HADDOCK3/HADDOCK3-protein-protein-basic/plots/scenario2/irmsd_score.html
 create mode 100644 education/HADDOCK3/HADDOCK3-protein-protein-basic/plots/scenario2/score_clt.html
 create mode 100644 education/HADDOCK3/HADDOCK3-protein-protein-basic/plots/scenario3/dockq_clt.html
 create mode 100644 education/HADDOCK3/HADDOCK3-protein-protein-basic/plots/scenario3/dockq_score.html
 create mode 100644 education/HADDOCK3/HADDOCK3-protein-protein-basic/plots/scenario3/irmsd_clt.html
 create mode 100644 education/HADDOCK3/HADDOCK3-protein-protein-basic/plots/scenario3/irmsd_score.html
 create mode 100644 education/HADDOCK3/HADDOCK3-protein-protein-basic/plots/scenario3/score_clt.html

diff --git a/education/HADDOCK3/HADDOCK3-protein-protein-basic/plots/scenario1/dockq_clt.html b/education/HADDOCK3/HADDOCK3-protein-protein-basic/plots/scenario1/dockq_clt.html
new file mode 100644
index 000000000..8f89b692d
--- /dev/null
+++ b/education/HADDOCK3/HADDOCK3-protein-protein-basic/plots/scenario1/dockq_clt.html
@@ -0,0 +1,23 @@
+
+    <div>
+    <script type="text/javascript">window.PlotlyConfig = { MathJaxConfig: 'local' };</script>
+    <script src="https://cdn.plot.ly/plotly-2.16.1.min.js"></script>
+    <div id="plot1" class="plotly-graph-div" style="height:800px; width:1000px;">
+    </div>
+    <script id="data1" type="application/json">
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-111.279<br>Caprieval rank: 18","Model: cluster_9_model_2.pdb<br>Score: -108.805<br>Caprieval rank: 26","Model: cluster_9_model_3.pdb<br>Score: -93.602<br>Caprieval rank: 95","Model: cluster_9_model_4.pdb<br>Score: -90.041<br>Caprieval rank: 119","Model: cluster_9_model_5.pdb<br>Score: -88.144<br>Caprieval rank: 136","Model: cluster_9_model_6.pdb<br>Score: -86.93<br>Caprieval rank: 143","Model: cluster_9_model_7.pdb<br>Score: -79.594<br>Caprieval rank: 193","Model: cluster_9_model_8.pdb<br>Score: -76.698<br>Caprieval rank: 216","Model: cluster_9_model_9.pdb<br>Score: -72.423<br>Caprieval rank: 246","Model: cluster_9_model_10.pdb<br>Score: -62.457<br>Caprieval rank: 287","Model: cluster_9_model_11.pdb<br>Score: -62.115<br>Caprieval rank: 288"],"x":[0.109,0.11,0.111,0.102,0.099,0.101,0.096,0.103,0.092,0.102,0.084],"y":[-111.279,-108.805,-93.602,-90.041,-88.144,-86.93,-79.594,-76.698,-72.423,-62.457,-62.115],"type":"scatter"},{"error_x":{"array":[0.0035355339059327407],"type":"data","visible":true},"error_y":{"array":[9.23833478974972],"type":"data","visible":true},"hoverlabel":{"bgcolor":"#EB663B","font":{"family":"Helvetica","size":16}},"hovertemplate":"<b>Cluster 9<br>dockq: 0.108<br>score: -100.932</b><extra></extra>","legendgroup":"Cluster 9","marker":{"color":"#EB663B","size":10,"symbol":"square-dot"},"mode":"markers","showlegend":false,"text":["Cluster 9<br>dockq: 0.108<br>score: -100.932"],"x":[0.108],"y":[-100.93175000000001],"type":"scatter"},{"hoverlabel":{"bgcolor":"#511CFB","font":{"family":"Helvetica","size":16}},"legendgroup":"Cluster 10","marker":{"color":"#511CFB"},"mode":"markers","name":"Cluster 10","text":["Model: cluster_10_model_1.pdb<br>Score: -104.573<br>Caprieval rank: 39","Model: cluster_10_model_2.pdb<br>Score: -98.436<br>Caprieval rank: 65","Model: cluster_10_model_3.pdb<br>Score: -97.928<br>Caprieval rank: 69","Model: cluster_10_model_4.pdb<br>Score: -95.797<br>Caprieval rank: 82","Model: cluster_10_model_5.pdb<br>Score: -88.311<br>Caprieval rank: 134","Model: cluster_10_model_6.pdb<br>Score: -85.64<br>Caprieval rank: 147","Model: cluster_10_model_7.pdb<br>Score: -80.717<br>Caprieval rank: 184"],"x":[0.106,0.115,0.104,0.096,0.083,0.108,0.096],"y":[-104.573,-98.436,-97.928,-95.797,-88.311,-85.64,-80.717],"type":"scatter"},{"error_x":{"array":[0.006759252917297888],"type":"data","visible":true},"error_y":{"array":[3.2653533116647555],"type":"data","visible":true},"hoverlabel":{"bgcolor":"#511CFB","font":{"family":"Helvetica","size":16}},"hovertemplate":"<b>Cluster 10<br>dockq: 0.105<br>score: -99.184</b><extra></extra>","legendgroup":"Cluster 10","marker":{"color":"#511CFB","size":10,"symbol":"square-dot"},"mode":"markers","showlegend":false,"text":["Cluster 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cluster_12_model_10.pdb<br>Score: -80.182<br>Caprieval rank: 189","Model: cluster_12_model_11.pdb<br>Score: -79.353<br>Caprieval rank: 195","Model: cluster_12_model_12.pdb<br>Score: -75.997<br>Caprieval rank: 220","Model: cluster_12_model_13.pdb<br>Score: -74.666<br>Caprieval rank: 227","Model: cluster_12_model_14.pdb<br>Score: -73.689<br>Caprieval rank: 232","Model: cluster_12_model_15.pdb<br>Score: -72.673<br>Caprieval rank: 241","Model: cluster_12_model_16.pdb<br>Score: -67.604<br>Caprieval rank: 264","Model: cluster_12_model_17.pdb<br>Score: -66.097<br>Caprieval rank: 276","Model: cluster_12_model_18.pdb<br>Score: -63.674<br>Caprieval rank: 282","Model: cluster_12_model_19.pdb<br>Score: -63.397<br>Caprieval rank: 284","Model: cluster_12_model_20.pdb<br>Score: -56.403<br>Caprieval rank: 301","Model: cluster_12_model_21.pdb<br>Score: -55.159<br>Caprieval rank: 302","Model: cluster_24_model_1.pdb<br>Score: -77.077<br>Caprieval rank: 212","Model: cluster_24_model_2.pdb<br>Score: -66.705<br>Caprieval rank: 269","Model: cluster_24_model_3.pdb<br>Score: -65.064<br>Caprieval rank: 278","Model: cluster_24_model_4.pdb<br>Score: -62.533<br>Caprieval rank: 286","Model: cluster_24_model_5.pdb<br>Score: -60.727<br>Caprieval rank: 291","Model: cluster_24_model_6.pdb<br>Score: -59.979<br>Caprieval rank: 292","Model: cluster_24_model_7.pdb<br>Score: -58.73<br>Caprieval rank: 297","Model: cluster_24_model_8.pdb<br>Score: -58.724<br>Caprieval rank: 298","Model: cluster_24_model_9.pdb<br>Score: -58.508<br>Caprieval rank: 299","Model: cluster_24_model_10.pdb<br>Score: -57.175<br>Caprieval rank: 300","Model: cluster_24_model_11.pdb<br>Score: -54.571<br>Caprieval rank: 304","Model: cluster_24_model_12.pdb<br>Score: -54.399<br>Caprieval rank: 305","Model: cluster_24_model_13.pdb<br>Score: -51.266<br>Caprieval rank: 309","Model: cluster_24_model_14.pdb<br>Score: -48.009<br>Caprieval rank: 311","Model: cluster_24_model_15.pdb<br>Score: -46.219<br>Caprieval rank: 314","Model: cluster_24_model_16.pdb<br>Score: -41.991<br>Caprieval rank: 315","Model: cluster_24_model_17.pdb<br>Score: -41.146<br>Caprieval rank: 316","Model: cluster_24_model_18.pdb<br>Score: -30.591<br>Caprieval rank: 317","Model: cluster_14_model_1.pdb<br>Score: -94.95<br>Caprieval rank: 88","Model: cluster_14_model_2.pdb<br>Score: -91.574<br>Caprieval rank: 107","Model: cluster_14_model_3.pdb<br>Score: -91.488<br>Caprieval rank: 108","Model: cluster_14_model_4.pdb<br>Score: -89.213<br>Caprieval rank: 128","Model: cluster_14_model_5.pdb<br>Score: -88.42<br>Caprieval rank: 133","Model: cluster_14_model_6.pdb<br>Score: -85.999<br>Caprieval rank: 144","Model: cluster_14_model_7.pdb<br>Score: -84.262<br>Caprieval rank: 159","Model: cluster_14_model_8.pdb<br>Score: -80.023<br>Caprieval rank: 190","Model: cluster_14_model_9.pdb<br>Score: -78.893<br>Caprieval rank: 199","Model: cluster_14_model_10.pdb<br>Score: -78.359<br>Caprieval rank: 202","Model: cluster_14_model_11.pdb<br>Score: -77.604<br>Caprieval rank: 206","Model: cluster_14_model_12.pdb<br>Score: -77.155<br>Caprieval rank: 210","Model: cluster_14_model_13.pdb<br>Score: -76.755<br>Caprieval rank: 213","Model: cluster_14_model_14.pdb<br>Score: -74.029<br>Caprieval rank: 230","Model: cluster_14_model_15.pdb<br>Score: -73.345<br>Caprieval rank: 234","Model: cluster_14_model_16.pdb<br>Score: -72.568<br>Caprieval rank: 244","Model: cluster_14_model_17.pdb<br>Score: -68.563<br>Caprieval rank: 259","Model: cluster_14_model_18.pdb<br>Score: -63.611<br>Caprieval rank: 283","Model: cluster_23_model_1.pdb<br>Score: -77.675<br>Caprieval rank: 205","Model: cluster_23_model_2.pdb<br>Score: -71.182<br>Caprieval rank: 252","Model: cluster_23_model_3.pdb<br>Score: -68.734<br>Caprieval rank: 257","Model: cluster_23_model_4.pdb<br>Score: -66.454<br>Caprieval rank: 272","Model: cluster_23_model_5.pdb<br>Score: -65.464<br>Caprieval rank: 277","Model: cluster_23_model_6.pdb<br>Score: -62.779<br>Caprieval rank: 285","Model: cluster_23_model_7.pdb<br>Score: -61.798<br>Caprieval rank: 290","Model: cluster_23_model_8.pdb<br>Score: -54.247<br>Caprieval rank: 306","Model: cluster_23_model_9.pdb<br>Score: -51.304<br>Caprieval rank: 308","Model: cluster_23_model_10.pdb<br>Score: -50.01<br>Caprieval rank: 310","Model: cluster_23_model_11.pdb<br>Score: -46.698<br>Caprieval rank: 313","Model: cluster_18_model_1.pdb<br>Score: -92.201<br>Caprieval rank: 104","Model: cluster_18_model_2.pdb<br>Score: -85.73<br>Caprieval rank: 146","Model: cluster_18_model_3.pdb<br>Score: -78.566<br>Caprieval rank: 201","Model: cluster_18_model_4.pdb<br>Score: -76.707<br>Caprieval rank: 215","Model: cluster_18_model_5.pdb<br>Score: -68.327<br>Caprieval rank: 260","Model: cluster_18_model_6.pdb<br>Score: -64.011<br>Caprieval rank: 280","Model: cluster_18_model_7.pdb<br>Score: -59.735<br>Caprieval rank: 293","Model: cluster_18_model_8.pdb<br>Score: -59.34<br>Caprieval rank: 295","Model: cluster_18_model_9.pdb<br>Score: -54.63<br>Caprieval rank: 303","Model: cluster_18_model_10.pdb<br>Score: -47.625<br>Caprieval rank: 312","Model: cluster_11_model_1.pdb<br>Score: -105.224<br>Caprieval rank: 37","Model: cluster_11_model_2.pdb<br>Score: -97.556<br>Caprieval rank: 71","Model: cluster_11_model_3.pdb<br>Score: -91.197<br>Caprieval rank: 111","Model: cluster_11_model_4.pdb<br>Score: -90.076<br>Caprieval rank: 117","Model: cluster_11_model_5.pdb<br>Score: -89.905<br>Caprieval rank: 121","Model: cluster_11_model_6.pdb<br>Score: -88.71<br>Caprieval rank: 132","Model: cluster_11_model_7.pdb<br>Score: -78.642<br>Caprieval rank: 200","Model: cluster_11_model_8.pdb<br>Score: -70.48<br>Caprieval rank: 255","Model: cluster_11_model_9.pdb<br>Score: -68.003<br>Caprieval rank: 262","Model: cluster_13_model_1.pdb<br>Score: -101.564<br>Caprieval rank: 51","Model: cluster_13_model_2.pdb<br>Score: -99.31<br>Caprieval rank: 60","Model: cluster_13_model_3.pdb<br>Score: -87.884<br>Caprieval rank: 137","Model: cluster_13_model_4.pdb<br>Score: -81.192<br>Caprieval rank: 179","Model: cluster_13_model_5.pdb<br>Score: -74.428<br>Caprieval rank: 229","Model: cluster_13_model_6.pdb<br>Score: -72.548<br>Caprieval rank: 245","Model: cluster_13_model_7.pdb<br>Score: -70.677<br>Caprieval rank: 254","Model: cluster_13_model_8.pdb<br>Score: -67.48<br>Caprieval rank: 265","Model: cluster_13_model_9.pdb<br>Score: -66.939<br>Caprieval rank: 267","Model: cluster_22_model_1.pdb<br>Score: -76.745<br>Caprieval rank: 214","Model: cluster_22_model_2.pdb<br>Score: -75.683<br>Caprieval rank: 222","Model: cluster_22_model_3.pdb<br>Score: -69.316<br>Caprieval rank: 256","Model: cluster_22_model_4.pdb<br>Score: -67.128<br>Caprieval rank: 266","Model: cluster_22_model_5.pdb<br>Score: -66.855<br>Caprieval rank: 268","Model: cluster_22_model_6.pdb<br>Score: -61.8<br>Caprieval rank: 289","Model: cluster_15_model_1.pdb<br>Score: -96.594<br>Caprieval rank: 76","Model: cluster_15_model_2.pdb<br>Score: -94.253<br>Caprieval rank: 91","Model: cluster_15_model_3.pdb<br>Score: -89.303<br>Caprieval rank: 127","Model: cluster_15_model_4.pdb<br>Score: -84.777<br>Caprieval rank: 152","Model: cluster_15_model_5.pdb<br>Score: -68.655<br>Caprieval rank: 258","Model: cluster_17_model_1.pdb<br>Score: -91.794<br>Caprieval rank: 106","Model: cluster_17_model_2.pdb<br>Score: -82.243<br>Caprieval rank: 172","Model: cluster_17_model_3.pdb<br>Score: -81.669<br>Caprieval rank: 176","Model: cluster_17_model_4.pdb<br>Score: -80.697<br>Caprieval rank: 185","Model: cluster_17_model_5.pdb<br>Score: -77.16<br>Caprieval rank: 209","Model: cluster_16_model_1.pdb<br>Score: -97.596<br>Caprieval rank: 70","Model: cluster_16_model_2.pdb<br>Score: -92.619<br>Caprieval rank: 102","Model: cluster_16_model_3.pdb<br>Score: -84.232<br>Caprieval rank: 160","Model: cluster_16_model_4.pdb<br>Score: -80.223<br>Caprieval rank: 188","Model: cluster_16_model_5.pdb<br>Score: -79.04<br>Caprieval rank: 197","Model: cluster_25_model_1.pdb<br>Score: -72.613<br>Caprieval 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174","Model: cluster_12_model_9.pdb<br>Score: -81.106<br>Caprieval rank: 180","Model: cluster_12_model_10.pdb<br>Score: -80.182<br>Caprieval rank: 189","Model: cluster_12_model_11.pdb<br>Score: -79.353<br>Caprieval rank: 195","Model: cluster_12_model_12.pdb<br>Score: -75.997<br>Caprieval rank: 220","Model: cluster_12_model_13.pdb<br>Score: -74.666<br>Caprieval rank: 227","Model: cluster_12_model_14.pdb<br>Score: -73.689<br>Caprieval rank: 232","Model: cluster_12_model_15.pdb<br>Score: -72.673<br>Caprieval rank: 241","Model: cluster_12_model_16.pdb<br>Score: -67.604<br>Caprieval rank: 264","Model: cluster_12_model_17.pdb<br>Score: -66.097<br>Caprieval rank: 276","Model: cluster_12_model_18.pdb<br>Score: -63.674<br>Caprieval rank: 282","Model: cluster_12_model_19.pdb<br>Score: -63.397<br>Caprieval rank: 284","Model: cluster_12_model_20.pdb<br>Score: -56.403<br>Caprieval rank: 301","Model: cluster_12_model_21.pdb<br>Score: -55.159<br>Caprieval rank: 302","Model: 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+    </script>
+    <script type="text/javascript">
+        const dat1 = JSON.parse(document.getElementById("data1").text)
+        window.PLOTLYENV = window.PLOTLYENV || {};
+        if (document.getElementById("plot1")) {
+            Plotly.newPlot(
+                "plot1",
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+                dat1.layout,
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+    
\ No newline at end of file

From 4b7211a3e26d36110e541db801c08ff5e2e0b3d2 Mon Sep 17 00:00:00 2001
From: Raphaelle Versini <rapha.versini@gmail.com>
Date: Tue, 3 Sep 2024 22:02:05 +0200
Subject: [PATCH 05/15] correction of results

---
 .../HADDOCK3-protein-protein-basic/index.md   | 568 +++++++++---------
 1 file changed, 297 insertions(+), 271 deletions(-)

diff --git a/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md b/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md
index a07ca49b8..10b8b542a 100644
--- a/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md
+++ b/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md
@@ -158,8 +158,8 @@ combining a multitude of independent modules that perform specialized tasks.
 
 <hr>
 <hr>
-## Software requirements
 
+## Software requirements
 
 ### Installing CNS
 The other required piece of software to run HADDOCK is its computational engine,
@@ -201,11 +201,9 @@ already installed on your system, download and install PyMol.
 
 ## Preparing PDB files for docking
 
-In this section we will prepare the PDB files of the antibody and antigen for docking.
-Crystal structures of both the antibody and the antigen in their free forms are available from the
-[PDBe database](https://www.pdbe.org){:target="_blank"}. In the case of the antibody which consists
-of two chains (L+H) we will have to prepare it for use in HADDOCK such as it can be treated as
-a single chain with non-overlapping residue numbering. For this we will be making use of `pdb-tools` from the command line.
+In this section we will prepare the PDB files of the two proteins for docking.
+Crystal structures are available from the [PDBe database](https://www.pdbe.org){:target="_blank"}. 
+Throughout this step, we will use `pdb-tools` from the command line.
 
 _**Note**_ that `pdb-tools` is also available as a [web service](https://wenmr.science.uu.nl/pdbtools/){:target="_blank"}.
 
@@ -520,9 +518,9 @@ HADDOCK3 also provides an analysis module (`caprieval`) that allows
 to compare models to either the best scoring model (if no reference is given) or a reference structure, which in our case
 we have at hand. This will directly allow us to assess the performance of the protocol for the following three scenarios:
 
-1. Scenario 1: Docking using the paratope information only and the surface of the antigen
-2. Scenario 2a: Docking using the paratope and the NMR-identified epitope as passive
-3. Scenario 2b: Docking using the paratope and the NMR-identified epitope as active
+1. Scenario 1: 1000 rigidbody docking models, selection of top200 and flexible refinement + EM 
+2. Scenario 2a: 1000 rigidbody docking models, selection of top200 and flexible refinement + final refinement in explicit solvent (water)
+3. Scenario 2b: 1000 rigidbody docking models, FCC clustering and selection of max 20 models per cluster followed by flexible refinement and EM
 
 The basic workflow for all three scenarios will consists of the following modules, with some differences in the restraints used and some parameter settings (see below):
 
@@ -592,39 +590,14 @@ _**Note**_: This is also the execution mode that should be used for example when
   ##conda activate haddock3
 
   # go to the run directory
-  cd $HOME/HADDOCK3-antibody-antigen
+  cd $HOME/HADDOCK3-protein-protein-basic
 
   # execute
-  haddock3 scenario1-surface-node.cfg
+  haddock3 docking-protein-protein-full.cfg
   {% endhighlight %}
   <br>
 </details>
 
-<details>
-<summary style="bold">
-View an EU-ASEAN HPC school example script for submitting to the Fugaku batch system <i class="material-icons">expand_more</i>
- </summary>
-{% highlight shell %}
-#!/bin/bash
-#PJM -L  "node=1"                            # Assign 1 node
-#PJM -L  "elapse=02:00:00"                   # Elapsed time limit 2 hour
-#PJM -x PJM_LLIO_GFSCACHE=/vol0003:/vol0006  # volume names that job uses
-#PJM -s                                      # Statistical information output
-
-# active the haddock3 conda environment
-source /vol0601/share/ra020021/LifeScience/20221208_Bonvin/miniconda3-arm8/etc/profile.d/conda.sh
-conda activate haddock3
-
-# go to the tutorial directory in your home directory
-# edit if needed to specify the correct location
-cd $HOME/HADDOCK3-antibody-antigen
-
-# execute haddock3
-haddock3 scenario2a-NMR-epitope-pass-node.cfg
-{% endhighlight %}
-<br>
-</details>
-
 <br>
 
 #### 2. batch mode
@@ -664,8 +637,8 @@ An example of the relevant parameters to be defined in the first section of the
 {% highlight toml %}
 # compute mode
 mode = "mpi"
-#  5 nodes x 50 tasks = ncores = 250
-ncores = 250
+#  1 nodes x 50 tasks = ncores = 50
+ncores = 50
 {% endhighlight %}
 
 In this execution mode the HADDOCK3 job should be submitted to the batch system requesting the corresponding number of nodes and cores per node.
@@ -688,19 +661,19 @@ In this execution mode the HADDOCK3 job should be submitted to the batch system
 
   # go to the run directory
   # edit if needed to specify the correct location
-  cd $HOME/HADDOCK3-antibody-antigen
+  cd $HOME/HADDOCK3-protein-protein-basic
 
   # execute
-  haddock3 scenario2a-NMR-epitope-pass-mpi.cfg
+  haddock3 docking-protein-protein-full.cfg
   {% endhighlight %}
   <br>
 </details>
 
 <hr>
 
-### Scenario 1: 1000 rigidbody docking models, selection of top200 and flexible refinement + EM of those
+### Scenario 1: 1000 rigidbody docking models, selection of top200 and flexible refinement + EM 
 
-Now that we have all data ready, and know about execution modes of HADDOCK3 it is time to setup the docking for the first scenario in which we will use the paratope on the antibody to guide the docking, targeting the entire surface of the antibody. The restraint file to use for this is `e2a-hpr_air.tbl`. We proceed to produce 1000 rigidbody docking models, from which 200 will be selected and refined through flexible refinment and energy minimization. 
+Now that we have all data ready, and know about execution modes of HADDOCK3 it is time to setup the docking for the first scenario. The restraint file to use for this is `e2a-hpr_air.tbl`. We proceed to produce 1000 rigidbody docking models, from which 200 will be selected and refined through flexible refinment and energy minimization. 
 For the analysis following the docking results, we are using the solved complex [1GGR](https://www.rcsb.org/structure/1GGR), named e2a-hpr_1GGR.pdb.
 The configuration file for this scenario (assuming a batch running mode) is:
 
@@ -709,7 +682,7 @@ The configuration file for this scenario (assuming a batch running mode) is:
 # Protein-protein docking example with NMR-derived ambiguous interaction restraints
 
 # directory in which the scoring will be done
-run_dir = "run1-full"
+run_dir = "scenario1-full"
 
 # execution mode
 mode = "batch"
@@ -772,7 +745,7 @@ reference_fname = "data/e2a-hpr_1GGR.pdb"
 [clustfcc]
 
 [seletopclusts]
-top_models = 4
+top_models = 500
 
 [caprieval]
 reference_fname = "data/e2a-hpr_1GGR.pdb"
@@ -798,7 +771,7 @@ The configuration file for this scenario (assuming a batch running mode) is:
 # Protein-protein docking example with NMR-derived ambiguous interaction restraints
 
 # directory in which the scoring will be done
-run_dir = "run2-mdref-full"
+run_dir = "scenario2-mdref-full"
 
 # execution mode
 mode = "batch"
@@ -863,7 +836,7 @@ reference_fname = "data/e2a-hpr_1GGR.pdb"
 [clustfcc]
 
 [seletopclusts]
-top_models = 4
+top_models = 500
 
 [caprieval]
 reference_fname = "data/e2a-hpr_1GGR.pdb"
@@ -889,7 +862,7 @@ The configuration file for this scenario (assuming a batch running mode) is:
 # ====================================================================
 
 # directory in which the scoring will be done
-run_dir = "run3-cltsel-full"
+run_dir = "scenario3-cltsel-full"
 
 # execution mode
 mode = "batch"
@@ -930,7 +903,7 @@ reference_fname = "data/e2a-hpr_1GGR.pdb"
 [clustfcc]
 
 [seletopclusts]
-## select the best 4 models of each cluster
+## select the best 20 models of each cluster
 top_models = 20
 
 [caprieval]
@@ -953,7 +926,7 @@ reference_fname = "data/e2a-hpr_1GGR.pdb"
 [clustfcc]
 
 [seletopclusts]
-top_models = 4
+top_models = 500
 
 [caprieval]
 reference_fname = "data/e2a-hpr_1GGR.pdb"
@@ -965,9 +938,6 @@ This configuration file is provided in the `haddock3` directory of the downloade
 
 If you have everything ready, you can launch haddock3 either from the command line, or, better, submitting it to the batch system requesting in this local run mode a full node (see local execution mode above).
 
-_**Note**_ The running time for this scenario is similar to that of scenario 2a (see above).
-
-
 <hr>
 <hr>
 
@@ -1071,11 +1041,11 @@ First of all let us check the final cluster statistics.
 <i>View the pre-calculated 11_caprieval/capri_clt.tsv file</i> <i class="material-icons">expand_more</i>
  </summary>
 <pre>
-cluster_rank    cluster_id      n       under_eval      score   score_std       irmsd   irmsd_std       fnat    fnat_std        lrmsd   lrmsd_std       dockq   dockq_std       air     air_std bsa     bsa_std desolv  desolv_std      elec    elec_std        total   total_std       vdw     vdw_std caprieval_rank
-1       1       4       -       -144.093        2.488   0.982   0.066   0.868   0.053   1.808   0.400   0.841   0.030   7.948   2.867   1552.338        29.521  -9.815  0.581   -516.505        19.903  -540.329        19.324  -31.772 4.032   1
-2       2       4       -       -114.725        2.332   7.857   0.537   0.160   0.060   15.260  1.442   0.145   0.031   20.796  13.446  1463.065        62.919  -15.198 1.933   -366.827        51.294  -374.272        36.604  -28.241 6.257   2
-3       3       4       -       -94.068 5.808   3.275   0.819   0.438   0.069   6.634   0.746   0.421   0.078   39.979  8.006   1355.070        46.720  -15.369 2.124   -277.523        45.985  -264.737        43.408  -27.193 3.176   3
-4       4       4       -       -90.571 9.613   7.949   0.272   0.139   0.020   13.948  0.422   0.148   0.007   11.023  13.529  1180.695        135.973 -12.646 2.946   -267.354        21.668  -281.889        19.831  -25.557 6.065   4
+cluster_rank    cluster_id      n       under_eval      score   score_std       irmsdirmsd_std        fnat    fnat_std        lrmsd   lrmsd_std       dockq   dockq_std    ilrmsd   ilrmsd_std      air     air_std bsa     bsa_std desolv  desolv_std      elec elec_std total   total_std       vdw     vdw_std caprieval_rank
+1       1       132     -       -136.315        2.459   0.922   0.050   0.847   0.0501.497    0.158   0.848   0.022   1.577   0.100   17.510  10.499  1592.155        26.85-11.290  2.460   -477.868        20.524  -491.561        13.390  -31.203 1.856   1     
+2       2       41      -       -118.410        9.418   7.843   0.237   0.194   0.00014.976   0.870   0.158   0.008   14.161  0.256   33.123  27.142  1525.405        19.48-11.788  2.649   -396.013        33.391  -393.621        55.889  -30.732 8.145   2     
+3       3       8       -       -87.144 5.206   3.741   0.418   0.333   0.039   7.4090.410    0.348   0.025   7.643   0.422   41.435  13.967  1290.872        72.223  -15.930       4.468   -245.765        38.007  -230.535        31.740  -26.204 3.205   3     
+4       4       4       -       -55.138 9.488   2.340   0.218   0.292   0.031   5.7850.727    0.424   0.019   5.334   0.773   42.306  19.922  960.189 142.370 -13.059 3.913-158.379 14.190  -130.707        26.432  -14.634 3.528   4
 </pre>
 </details>
 <br>
@@ -1098,7 +1068,7 @@ We are providing in the `scripts` directory a simple script that extract some cl
 To use is simply call the script with as argument the run directory you want to analyze, e.g.:
 
 <a class="prompt prompt-cmd">
-   ./scripts/extract-capri-stats-clt.sh ./scenario1-surface
+   ./scripts/extract-capri-stats-clt.sh ./scenario1
 </a>
 
 <details style="background-color:#DAE4E7">
@@ -1107,8 +1077,7 @@ To use is simply call the script with as argument the run directory you want to
  </summary>
 <pre>
 ==============================================
-==============================================
-== run1-full/02_caprieval/capri_clt.tsv
+== scenario1-full/02_caprieval/capri_clt.tsv
 ==============================================
 Total number of acceptable or better clusters:  0  out of  1
 Total number of medium or better clusters:      0  out of  1
@@ -1116,9 +1085,9 @@ Total number of high quality clusters:          0  out of  1
 
 First acceptable cluster - rank:   i-RMSD:   Fnat:   DockQ: 
 First medium cluster     - rank:   i-RMSD:   Fnat:   DockQ:
-Best cluster             - rank:  -  i-RMSD:  7.330  Fnat:  0.125  DockQ:  0.155      
+Best cluster             - rank:  -  i-RMSD:  6.407  Fnat:  0.202  DockQ:  0.264      
 ==============================================
-== run1-full/04_caprieval/capri_clt.tsv
+== scenario1-full/04_caprieval/capri_clt.tsv
 ==============================================
 Total number of acceptable or better clusters:  0  out of  1
 Total number of medium or better clusters:      0  out of  1
@@ -1126,37 +1095,37 @@ Total number of high quality clusters:          0  out of  1
 
 First acceptable cluster - rank:   i-RMSD:   Fnat:   DockQ: 
 First medium cluster     - rank:   i-RMSD:   Fnat:   DockQ:
-Best cluster             - rank:  -  i-RMSD:  7.330  Fnat:  0.125  DockQ:  0.155      
+Best cluster             - rank:  -  i-RMSD:  6.407  Fnat:  0.202  DockQ:  0.264      
 ==============================================
-== run1-full/06_caprieval/capri_clt.tsv
+== scenario1-full/06_caprieval/capri_clt.tsv
 ==============================================
-Total number of acceptable or better clusters:  0  out of  1
+Total number of acceptable or better clusters:  1  out of  1
 Total number of medium or better clusters:      0  out of  1
 Total number of high quality clusters:          0  out of  1
 
-First acceptable cluster - rank:   i-RMSD:   Fnat:   DockQ: 
+First acceptable cluster - rank:  -  i-RMSD:  2.976  Fnat:  0.611  DockQ:  0.601
 First medium cluster     - rank:   i-RMSD:   Fnat:   DockQ:
-Best cluster             - rank:  -  i-RMSD:  4.563  Fnat:  0.424  DockQ:  0.455      
+Best cluster             - rank:  -  i-RMSD:  2.976  Fnat:  0.611  DockQ:  0.601      
 ==============================================
-== run1-full/08_caprieval/capri_clt.tsv
+== scenario1-full/08_caprieval/capri_clt.tsv
 ==============================================
 Total number of acceptable or better clusters:  1  out of  1
 Total number of medium or better clusters:      1  out of  1
-Total number of high quality clusters:          1  out of  1
+Total number of high quality clusters:          0  out of  1
 
-First acceptable cluster - rank:  -  i-RMSD:  0.982  Fnat:  0.868  DockQ:  0.841
-First medium cluster     - rank:  -  i-RMSD:  0.982  Fnat:  0.868  DockQ:  0.841      
-Best cluster             - rank:  -  i-RMSD:  0.982  Fnat:  0.868  DockQ:  0.841      
+First acceptable cluster - rank:  -  i-RMSD:  1.673  Fnat:  0.736  DockQ:  0.727
+First medium cluster     - rank:  -  i-RMSD:  1.673  Fnat:  0.736  DockQ:  0.727      
+Best cluster             - rank:  -  i-RMSD:  1.673  Fnat:  0.736  DockQ:  0.727      
 ==============================================
-== run1-full/11_caprieval/capri_clt.tsv
+== scenario1-full/11_caprieval/capri_clt.tsv
 ==============================================
-Total number of acceptable or better clusters:  2  out of  4
+Total number of acceptable or better clusters:  3  out of  4
 Total number of medium or better clusters:      1  out of  4
 Total number of high quality clusters:          1  out of  4
 
-First acceptable cluster - rank:  1  i-RMSD:  0.982  Fnat:  0.868  DockQ:  0.841
-First medium cluster     - rank:  1  i-RMSD:  0.982  Fnat:  0.868  DockQ:  0.841      
-Best cluster             - rank:  1  i-RMSD:  0.982  Fnat:  0.868  DockQ:  0.841      
+First acceptable cluster - rank:  1  i-RMSD:  0.922  Fnat:  0.847  DockQ:  0.848
+First medium cluster     - rank:  1  i-RMSD:  0.922  Fnat:  0.847  DockQ:  0.848      
+Best cluster             - rank:  1  i-RMSD:  0.922  Fnat:  0.847  DockQ:  0.848      
 </pre>
 </details>
 
@@ -1175,55 +1144,55 @@ Similarly some simple statistics can be extracted from the single model `capriev
  </summary>
 <pre>
 ==============================================
-== run1-full/02_caprieval/capri_ss.tsv
+== scenario1-full/02_caprieval/capri_ss.tsv
 ==============================================
-Total number of acceptable or better models:  351  out of  1000
-Total number of medium or better models:      201  out of  1000
+Total number of acceptable or better models:  365  out of  1000
+Total number of medium or better models:      199  out of  1000
 Total number of high quality models:          0  out of  1000
 
-First acceptable model - rank:  15  i-RMSD:  1.677  Fnat:  0.472  DockQ:  0.568
-First medium model     - rank:  15  i-RMSD:  1.677  Fnat:  0.472  DockQ:  0.568       
-Best model             - rank:  48  i-RMSD:  1.146  Fnat:  0.556  DockQ:  0.713       
+First acceptable model - rank:  3  i-RMSD:  1.153  Fnat:  0.556  DockQ:  0.711
+First medium model     - rank:  3  i-RMSD:  1.153  Fnat:  0.556  DockQ:  0.711        
+Best model             - rank:  46  i-RMSD:  1.145  Fnat:  0.556  DockQ:  0.713       
 ==============================================
-== run1-full/04_caprieval/capri_ss.tsv
+== scenario1-full/04_caprieval/capri_ss.tsv
 ==============================================
-Total number of acceptable or better models:  138  out of  200
-Total number of medium or better models:      131  out of  200
+Total number of acceptable or better models:  144  out of  200
+Total number of medium or better models:      137  out of  200
 Total number of high quality models:          0  out of  200
 
-First acceptable model - rank:  15  i-RMSD:  1.677  Fnat:  0.472  DockQ:  0.568
-First medium model     - rank:  15  i-RMSD:  1.677  Fnat:  0.472  DockQ:  0.568       
-Best model             - rank:  48  i-RMSD:  1.146  Fnat:  0.556  DockQ:  0.713       
+First acceptable model - rank:  3  i-RMSD:  1.153  Fnat:  0.556  DockQ:  0.711
+First medium model     - rank:  3  i-RMSD:  1.153  Fnat:  0.556  DockQ:  0.711        
+Best model             - rank:  46  i-RMSD:  1.145  Fnat:  0.556  DockQ:  0.713       
 ==============================================
-== run1-full/06_caprieval/capri_ss.tsv
+== scenario1-full/06_caprieval/capri_ss.tsv
 ==============================================
-Total number of acceptable or better models:  141  out of  200
-Total number of medium or better models:      123  out of  200
-Total number of high quality models:          29  out of  200
+Total number of acceptable or better models:  147  out of  200
+Total number of medium or better models:      118  out of  200
+Total number of high quality models:          20  out of  200
 
-First acceptable model - rank:  3  i-RMSD:  0.966  Fnat:  0.833  DockQ:  0.828
-First medium model     - rank:  3  i-RMSD:  0.966  Fnat:  0.833  DockQ:  0.828        
-Best model             - rank:  92  i-RMSD:  0.890  Fnat:  0.806  DockQ:  0.840       
+First acceptable model - rank:  2  i-RMSD:  1.221  Fnat:  0.694  DockQ:  0.727
+First medium model     - rank:  2  i-RMSD:  1.221  Fnat:  0.694  DockQ:  0.727        
+Best model             - rank:  30  i-RMSD:  0.883  Fnat:  0.750  DockQ:  0.823       
 ==============================================
-== run1-full/08_caprieval/capri_ss.tsv
+== scenario1-full/08_caprieval/capri_ss.tsv
 ==============================================
-Total number of acceptable or better models:  140  out of  200
-Total number of medium or better models:      124  out of  200
-Total number of high quality models:          45  out of  200
+Total number of acceptable or better models:  147  out of  200
+Total number of medium or better models:      118  out of  200
+Total number of high quality models:          34  out of  200
 
-First acceptable model - rank:  1  i-RMSD:  0.894  Fnat:  0.889  DockQ:  0.866
-First medium model     - rank:  1  i-RMSD:  0.894  Fnat:  0.889  DockQ:  0.866        
-Best model             - rank:  52  i-RMSD:  0.838  Fnat:  0.861  DockQ:  0.866       
+First acceptable model - rank:  1  i-RMSD:  1.219  Fnat:  0.833  DockQ:  0.787
+First medium model     - rank:  1  i-RMSD:  1.219  Fnat:  0.833  DockQ:  0.787        
+Best model             - rank:  39  i-RMSD:  0.807  Fnat:  0.833  DockQ:  0.862       
 ==============================================
-== run1-full/11_caprieval/capri_ss.tsv
+== scenario1-full/11_caprieval/capri_ss.tsv
 ==============================================
-Total number of acceptable or better models:  8  out of  16
-Total number of medium or better models:      5  out of  16
-Total number of high quality models:          2  out of  16
+Total number of acceptable or better models:  141  out of  185
+Total number of medium or better models:      116  out of  185
+Total number of high quality models:          34  out of  185
 
-First acceptable model - rank:  1  i-RMSD:  0.894  Fnat:  0.889  DockQ:  0.866
-First medium model     - rank:  1  i-RMSD:  0.894  Fnat:  0.889  DockQ:  0.866        
-Best model             - rank:  1  i-RMSD:  0.894  Fnat:  0.889  DockQ:  0.866        
+First acceptable model - rank:  1  i-RMSD:  0.907  Fnat:  0.917  DockQ:  0.871
+First medium model     - rank:  1  i-RMSD:  0.907  Fnat:  0.917  DockQ:  0.871        
+Best model             - rank:  36  i-RMSD:  0.807  Fnat:  0.833  DockQ:  0.862          
 </pre>
 </details>
 
@@ -1263,26 +1232,43 @@ _**Note**_ that this kind of analysis only makes sense when we know the referenc
 
 <br>
 
+#### Analysis scenario 1: visualising the scores and their components
+
+We have precalculated a number of interactive plots to visualize the scores and their components versus ranks and model quality.
+
+<a class="prompt prompt-info">
+Examine the plots (remember here that higher DockQ values and lower i-RMSD values correspond to better models)
+</a>
+
+Models statistics:
+
+* [iRMSD versus HADDOCK score](plots/scenario1/irmsd_score.html){:target="_blank"}
+* [DockQ versus HADDOCK score](plots/scenario1/dockq_score.html){:target="_blank"}
+
+Cluster statistics (distributions of values per cluster ordered according to their HADDOCK rank):
+
+* [HADDOCK scores](plots/scenario1/score_clt.html){:target="_blank"}
+* [iRMSD](plots/scenario1/irmsd_clt.html){:target="_blank"}
+* [DockQ](plots/scenario1/dockq_clt.html){:target="_blank"}
+
 <hr>
 
 ### Analysis scenario 2: 
 
 Let us now analyse the docking results for this scenario. Use for that either your own run or a pre-calculated run provided in the `runs` directory.
-Go into the _analysis/9_caprieval_analysis_  directory of the respective run directory and
+Go into the _analysis/_caprieval_analysis_  directory of the respective run directory and
 
 <a class="prompt prompt-info">Inspect the final cluster statistics in _capri_clt.tsv_ file </a>
 
 <details style="background-color:#DAE4E7">
 <summary>
-<i>View the pre-calculated 9_caprieval/capri_clt.tsv file</i> <i class="material-icons">expand_more</i>
+<i>View the pre-calculated 09_caprieval/capri_clt.tsv file</i> <i class="material-icons">expand_more</i>
  </summary>
 <pre>
-cluster_rank    cluster_id      n       under_eval      score   score_std       irmsd   irmsd_std       fnat    fnat_std        lrmsd   lrmsd_std       dockq   dockq_std       air     air_std bsa     bsa_std desolv  desolv_std      elec    elec_std        total   total_std       vdw     vdw_std caprieval_rank
-1       1       4       -       -152.266        6.827   1.063   0.088   0.875   0.046   1.919   0.354   0.831   0.030   9.789   4.782   1666.013        73.943  -14.186 2.889   -451.427        36.126  -490.412        39.908  -48.774 6.566   1
-2       2       4       -       -118.927        4.544   8.218   0.226   0.084   0.034   16.077  0.481   0.111   0.014   15.749  9.293   1446.352        88.238  -11.992 2.298   -382.010        16.477  -398.369        16.682  -32.108 1.977   2
-3       3       4       -       -92.965 7.490   3.203   0.522   0.416   0.027   6.760   0.336   0.406   0.024   18.864  13.757  1293.035        117.159 -18.657 4.710   -234.961        19.534  -245.298        24.082  -29.202 6.667   3
-4       4       4       -       -88.999 5.805   7.961   0.279   0.146   0.023   13.985  0.473   0.150   0.003   5.901   6.214   1224.918        78.277  -15.469 3.235   -212.758        22.313  -238.425        21.362  -31.568 4.338   4
-5       5       4       -       -79.140 9.087   3.194   1.314   0.312   0.135   6.154   1.904   0.406   0.135   24.820  15.208  1180.793        111.786 -14.802 4.462   -230.060        40.784  -226.049        33.401  -20.809 3.137   5
+cluster_rank    cluster_id      n       under_eval      score   score_std       irmsdirmsd_std        fnat    fnat_std        lrmsd   lrmsd_std       dockq   dockq_std    ilrmsd   ilrmsd_std      air     air_std bsa     bsa_std desolv  desolv_std      elec elec_std total   total_std       vdw     vdw_std caprieval_rank
+1       1       135     -       -150.688        4.693   1.179   0.175   0.792   0.1091.950    0.688   0.786   0.071   1.944   0.456   19.494  12.609  1690.822        47.60-18.212  3.894   -419.064        25.202  -450.182        33.243  -50.613 2.503   1     
+2       2       43      -       -127.775        6.046   8.432   0.520   0.139   0.05916.534   1.229   0.127   0.029   15.098  1.051   23.416  26.105  1511.128        127.638       -13.659 2.707   -383.687        32.493  -399.990        32.684  -39.720 4.6852
+3       3       8       -       -88.830 6.416   3.701   0.393   0.354   0.012   7.6140.634    0.352   0.024   7.684   0.663   14.781  8.117   1263.165        122.190 -22.622       4.743   -174.373        32.041  -192.404        36.319  -32.812 3.443   3     
 </pre>
 </details>
 
@@ -1315,7 +1301,8 @@ To use is simply call the script with as argument the run directory you want to
  </summary>
 <pre>
 ==============================================
-== run2-full/02_caprieval/capri_clt.tsv
+==============================================
+== scenario2-mdref-full/02_caprieval/capri_clt.tsv
 ==============================================
 Total number of acceptable or better clusters:  0  out of  1
 Total number of medium or better clusters:      0  out of  1
@@ -1323,9 +1310,9 @@ Total number of high quality clusters:          0  out of  1
 
 First acceptable cluster - rank:   i-RMSD:   Fnat:   DockQ: 
 First medium cluster     - rank:   i-RMSD:   Fnat:   DockQ:
-Best cluster             - rank:  -  i-RMSD:  7.330  Fnat:  0.125  DockQ:  0.155      
+Best cluster             - rank:  -  i-RMSD:  6.407  Fnat:  0.202  DockQ:  0.264      
 ==============================================
-== run2-full/04_caprieval/capri_clt.tsv
+== scenario2-mdref-full/04_caprieval/capri_clt.tsv
 ==============================================
 Total number of acceptable or better clusters:  0  out of  1
 Total number of medium or better clusters:      0  out of  1
@@ -1333,37 +1320,37 @@ Total number of high quality clusters:          0  out of  1
 
 First acceptable cluster - rank:   i-RMSD:   Fnat:   DockQ: 
 First medium cluster     - rank:   i-RMSD:   Fnat:   DockQ:
-Best cluster             - rank:  -  i-RMSD:  7.330  Fnat:  0.125  DockQ:  0.155      
+Best cluster             - rank:  -  i-RMSD:  6.407  Fnat:  0.202  DockQ:  0.264      
 ==============================================
-== run2-full/06_caprieval/capri_clt.tsv
+== scenario2-mdref-full/06_caprieval/capri_clt.tsv
 ==============================================
-Total number of acceptable or better clusters:  0  out of  1
+Total number of acceptable or better clusters:  1  out of  1
 Total number of medium or better clusters:      0  out of  1
 Total number of high quality clusters:          0  out of  1
 
-First acceptable cluster - rank:   i-RMSD:   Fnat:   DockQ: 
+First acceptable cluster - rank:  -  i-RMSD:  2.976  Fnat:  0.611  DockQ:  0.601
 First medium cluster     - rank:   i-RMSD:   Fnat:   DockQ:
-Best cluster             - rank:  -  i-RMSD:  4.563  Fnat:  0.424  DockQ:  0.455      
+Best cluster             - rank:  -  i-RMSD:  2.976  Fnat:  0.611  DockQ:  0.601      
 ==============================================
-== run2-full/08_caprieval/capri_clt.tsv
+== scenario2-mdref-full/08_caprieval/capri_clt.tsv
 ==============================================
 Total number of acceptable or better clusters:  1  out of  1
 Total number of medium or better clusters:      1  out of  1
 Total number of high quality clusters:          0  out of  1
 
-First acceptable cluster - rank:  -  i-RMSD:  1.063  Fnat:  0.875  DockQ:  0.831
-First medium cluster     - rank:  -  i-RMSD:  1.063  Fnat:  0.875  DockQ:  0.831      
-Best cluster             - rank:  -  i-RMSD:  1.063  Fnat:  0.875  DockQ:  0.831      
+First acceptable cluster - rank:  -  i-RMSD:  1.179  Fnat:  0.792  DockQ:  0.786
+First medium cluster     - rank:  -  i-RMSD:  1.179  Fnat:  0.792  DockQ:  0.786      
+Best cluster             - rank:  -  i-RMSD:  1.179  Fnat:  0.792  DockQ:  0.786      
 ==============================================
-== run2-full/11_caprieval/capri_clt.tsv
+== scenario2-mdref-full/11_caprieval/capri_clt.tsv
 ==============================================
-Total number of acceptable or better clusters:  3  out of  5
-Total number of medium or better clusters:      1  out of  5
-Total number of high quality clusters:          0  out of  5
+Total number of acceptable or better clusters:  2  out of  3
+Total number of medium or better clusters:      1  out of  3
+Total number of high quality clusters:          0  out of  3
 
-First acceptable cluster - rank:  1  i-RMSD:  1.063  Fnat:  0.875  DockQ:  0.831
-First medium cluster     - rank:  1  i-RMSD:  1.063  Fnat:  0.875  DockQ:  0.831      
-Best cluster             - rank:  1  i-RMSD:  1.063  Fnat:  0.875  DockQ:  0.831      
+First acceptable cluster - rank:  1  i-RMSD:  1.179  Fnat:  0.792  DockQ:  0.786
+First medium cluster     - rank:  1  i-RMSD:  1.179  Fnat:  0.792  DockQ:  0.786      
+Best cluster             - rank:  1  i-RMSD:  1.179  Fnat:  0.792  DockQ:  0.786
 </pre>
 </details>
 
@@ -1381,55 +1368,55 @@ Similarly some simple statistics can be extracted from the single model `capriev
  </summary>
 <pre>
 ==============================================
-== run2-full/02_caprieval/capri_ss.tsv
+== scenario2-mdref-full/02_caprieval/capri_ss.tsv
 ==============================================
-Total number of acceptable or better models:  351  out of  1000
-Total number of medium or better models:      201  out of  1000
+Total number of acceptable or better models:  365  out of  1000
+Total number of medium or better models:      199  out of  1000
 Total number of high quality models:          0  out of  1000
 
-First acceptable model - rank:  15  i-RMSD:  1.677  Fnat:  0.472  DockQ:  0.568
-First medium model     - rank:  15  i-RMSD:  1.677  Fnat:  0.472  DockQ:  0.568       
-Best model             - rank:  48  i-RMSD:  1.146  Fnat:  0.556  DockQ:  0.713       
+First acceptable model - rank:  3  i-RMSD:  1.153  Fnat:  0.556  DockQ:  0.711
+First medium model     - rank:  3  i-RMSD:  1.153  Fnat:  0.556  DockQ:  0.711        
+Best model             - rank:  46  i-RMSD:  1.145  Fnat:  0.556  DockQ:  0.713       
 ==============================================
-== run2-full/04_caprieval/capri_ss.tsv
+== scenario2-mdref-full/04_caprieval/capri_ss.tsv
 ==============================================
-Total number of acceptable or better models:  138  out of  200
-Total number of medium or better models:      131  out of  200
+Total number of acceptable or better models:  144  out of  200
+Total number of medium or better models:      137  out of  200
 Total number of high quality models:          0  out of  200
 
-First acceptable model - rank:  15  i-RMSD:  1.677  Fnat:  0.472  DockQ:  0.568
-First medium model     - rank:  15  i-RMSD:  1.677  Fnat:  0.472  DockQ:  0.568       
-Best model             - rank:  48  i-RMSD:  1.146  Fnat:  0.556  DockQ:  0.713       
+First acceptable model - rank:  3  i-RMSD:  1.153  Fnat:  0.556  DockQ:  0.711
+First medium model     - rank:  3  i-RMSD:  1.153  Fnat:  0.556  DockQ:  0.711        
+Best model             - rank:  46  i-RMSD:  1.145  Fnat:  0.556  DockQ:  0.713       
 ==============================================
-== run2-full/06_caprieval/capri_ss.tsv
+== scenario2-mdref-full/06_caprieval/capri_ss.tsv
 ==============================================
-Total number of acceptable or better models:  141  out of  200
-Total number of medium or better models:      123  out of  200
-Total number of high quality models:          29  out of  200
+Total number of acceptable or better models:  147  out of  200
+Total number of medium or better models:      118  out of  200
+Total number of high quality models:          20  out of  200
 
-First acceptable model - rank:  3  i-RMSD:  0.966  Fnat:  0.833  DockQ:  0.828
-First medium model     - rank:  3  i-RMSD:  0.966  Fnat:  0.833  DockQ:  0.828        
-Best model             - rank:  92  i-RMSD:  0.890  Fnat:  0.806  DockQ:  0.840       
+First acceptable model - rank:  2  i-RMSD:  1.221  Fnat:  0.694  DockQ:  0.727
+First medium model     - rank:  2  i-RMSD:  1.221  Fnat:  0.694  DockQ:  0.727        
+Best model             - rank:  30  i-RMSD:  0.883  Fnat:  0.750  DockQ:  0.823       
 ==============================================
-== run2-full/08_caprieval/capri_ss.tsv
+== scenario2-mdref-full/08_caprieval/capri_ss.tsv
 ==============================================
-Total number of acceptable or better models:  140  out of  200
-Total number of medium or better models:      123  out of  200
-Total number of high quality models:          16  out of  200
+Total number of acceptable or better models:  146  out of  200
+Total number of medium or better models:      121  out of  200
+Total number of high quality models:          15  out of  200
 
-First acceptable model - rank:  1  i-RMSD:  1.096  Fnat:  0.917  DockQ:  0.836
-First medium model     - rank:  1  i-RMSD:  1.096  Fnat:  0.917  DockQ:  0.836        
-Best model             - rank:  105  i-RMSD:  0.909  Fnat:  0.806  DockQ:  0.836      
+First acceptable model - rank:  1  i-RMSD:  1.129  Fnat:  0.806  DockQ:  0.804
+First medium model     - rank:  1  i-RMSD:  1.129  Fnat:  0.806  DockQ:  0.804        
+Best model             - rank:  51  i-RMSD:  0.891  Fnat:  0.861  DockQ:  0.855       
 ==============================================
-== run2-full/11_caprieval/capri_ss.tsv
+== scenario2-mdref-full/11_caprieval/capri_ss.tsv
 ==============================================
-Total number of acceptable or better models:  10  out of  20
-Total number of medium or better models:      5  out of  20
-Total number of high quality models:          1  out of  20
+Total number of acceptable or better models:  141  out of  186
+Total number of medium or better models:      118  out of  186
+Total number of high quality models:          15  out of  186
 
-First acceptable model - rank:  1  i-RMSD:  1.096  Fnat:  0.917  DockQ:  0.836
-First medium model     - rank:  1  i-RMSD:  1.096  Fnat:  0.917  DockQ:  0.836        
-Best model             - rank:  4  i-RMSD:  0.929  Fnat:  0.917  DockQ:  0.870        
+First acceptable model - rank:  1  i-RMSD:  1.129  Fnat:  0.806  DockQ:  0.804
+First medium model     - rank:  1  i-RMSD:  1.129  Fnat:  0.806  DockQ:  0.804        
+Best model             - rank:  50  i-RMSD:  0.891  Fnat:  0.861  DockQ:  0.855
 </pre>
 </details>
 <br>
@@ -1467,30 +1454,49 @@ _**Note**_ that this kind of analysis only makes sense when we know the referenc
 
 <br>
 
+#### Analysis scenario 2: visualising the scores and their components
+
+We have precalculated a number of interactive plots to visualize the scores and their components versus ranks and model quality.
+
+<a class="prompt prompt-info">
+Examine the plots (remember here that higher DockQ values and lower i-RMSD values correspond to better models)
+</a>
+
+Models statistics:
+
+* [iRMSD versus HADDOCK score](plots/scenario2/irmsd_score.html){:target="_blank"}
+* [DockQ versus HADDOCK score](plots/scenario2/dockq_score.html){:target="_blank"}
+
+Cluster statistics (distributions of values per cluster ordered according to their HADDOCK rank):
+
+* [HADDOCK scores](plots/scenario2/score_clt.html){:target="_blank"}
+* [iRMSD](plots/scenario2/irmsd_clt.html){:target="_blank"}
+* [DockQ](plots/scenario2/dockq_clt.html){:target="_blank"}
+
 <hr>
 
 ### Analysis scenario 3: Paratope - NMR-epitope as active
 
 Let us now analyse the docking results for this scenario. Use for that either your own run or a pre-calculated run provided in the `runs` directory.
-Go into the _analysis/9_caprieval_analysis_  directory of the respective run directory and
+Go into the _analysis/_caprieval_analysis_  directory of the respective run directory and
 
 <a class="prompt prompt-info">Inspect the final cluster statistics in _capri_clt.tsv_ file </a>
 
 <details style="background-color:#DAE4E7">
 <summary>
-<i>View the pre-calculated 9_caprieval/capri_clt.tsv file</i> <i class="material-icons">expand_more</i>
+<i>View the pre-calculated _caprieval/capri_clt.tsv file</i> <i class="material-icons">expand_more</i>
  </summary>
 <pre>
-cluster_rank    cluster_id      n       under_eval      score   score_std       irmsd   irmsd_std       fnat    fnat_std        lrmsd   lrmsd_std       dockq   dockq_std       air     air_std bsa     bsa_std desolv  desolv_std      elec    elec_std        total   total_std       vdw     vdw_std caprieval_rank
-1       1       4       -       -124.146        3.141   1.022   0.113   0.785   0.074   1.812   0.477   0.808   0.045   19.088  8.507   1493.348        101.980 -14.374 2.868   -390.668        44.141  -405.127        39.003  -33.547 7.112   1
-2       2       4       -       -109.733        4.447   8.384   0.538   0.153   0.063   15.962  0.969   0.135   0.029   64.065  29.996  1461.225        113.842 -13.164 2.827   -394.903        13.092  -354.834        34.074  -23.996 4.254   2
-3       6       4       -       -105.989        3.889   4.022   0.232   0.243   0.050   6.572   0.337   0.331   0.025   38.555  17.146  1385.205        39.561  -6.273  3.174   -425.420        56.558  -405.353        38.939  -18.487 5.586   3
-4       15      4       -       -104.603        12.655  2.611   0.503   0.528   0.059   6.425   1.299   0.477   0.067   85.213  45.158  1356.690        121.841 -8.303  1.220   -373.493        63.970  -318.403        31.234  -30.123 7.143   4
-5       4       4       -       -104.598        8.865   10.825  0.034   0.132   0.012   18.111  0.347   0.110   0.004   30.421  16.727  1598.340        70.785  -14.415 3.302   -271.984        24.921  -280.391        29.442  -38.828 10.416  5
-6       5       4       -       -101.889        2.338   9.402   0.193   0.104   0.030   16.057  0.690   0.116   0.009   64.991  18.462  1450.140        29.114  -5.857  3.187   -315.007        37.319  -289.547        33.841  -39.530 5.579   6
-7       11      4       -       -99.698 1.987   10.559  0.095   0.056   0.019   18.576  0.518   0.083   0.006   54.563  22.794  1569.310        56.647  -8.234  1.467   -295.063        14.691  -278.408        32.855  -37.908 3.927   7
-8       3       4       -       -96.631 7.773   7.819   1.263   0.083   0.019   13.394  2.088   0.140   0.028   56.501  8.286   1374.160        140.711 -12.846 3.790   -310.567        43.021  -281.388        40.284  -27.321 13.323  8
-9       16      4       -       -92.523 3.297   3.663   0.302   0.292   0.024   11.379  1.400   0.267   0.027   54.758  29.513  1501.015        38.489  -11.325 1.019   -272.691        16.590  -250.069        27.109  -32.135 4.675   9
+cluster_rank    cluster_id      n       under_eval      score   score_std       irmsdirmsd_std        fnat    fnat_std        lrmsd   lrmsd_std       dockq   dockq_std    ilrmsd   ilrmsd_std      air     air_std bsa     bsa_std desolv  desolv_std      elec elec_std total   total_std       vdw     vdw_std caprieval_rank
+1       1       37      -       -124.658        10.252  3.857   0.922   0.319   0.0976.660    1.321   0.365   0.085   6.780   1.681   31.615  22.188  1616.990        119.623       -6.778  6.101   -437.724        75.263  -439.605        69.689  -33.496 10.191
+2       3       26      -       -119.435        4.949   0.982   0.026   0.805   0.0522.058    0.408   0.816   0.026   1.781   0.120   29.748  11.990  1522.275        58.81-14.346  5.089   -383.147        87.371  -384.832        89.623  -31.434 9.369   2     
+3       8       15      -       -117.501        8.381   10.507  0.012   0.049   0.02318.245   0.253   0.082   0.008   17.406  0.097   16.941  13.148  1695.840        65.32-11.683  2.440   -305.048        31.227  -334.610        34.942  -46.502 3.538   3     
+4       10      12      -       -115.472        5.836   0.980   0.038   0.812   0.0532.062    0.443   0.819   0.020   1.762   0.114   19.993  10.271  1488.888        64.59-14.848  1.764   -351.208        32.914  -363.598        30.092  -32.382 8.809   4     
+5       2       27      -       -106.389        2.683   9.379   0.146   0.125   0.01416.285   0.693   0.122   0.004   16.768  0.405   20.260  12.423  1359.715        23.92-10.242  1.447   -272.409        33.608  -295.839        37.474  -43.691 4.786   5     
+6       4       25      -       -106.037        2.709   7.852   0.619   0.132   0.07715.047   1.787   0.139   0.042   14.277  0.588   43.187  10.734  1403.977        70.15-13.256  1.092   -361.058        57.590  -342.759        55.416  -24.888 11.965  6     
+7       9       13      -       -105.524        8.380   10.273  0.355   0.076   0.01217.160   0.297   0.098   0.005   16.986  0.601   52.965  34.487  1493.557        88.840.241    1.661   -433.424        58.594  -404.836        36.296  -24.376 10.058  7     
+8       13      11      -       -104.016        12.736  6.651   1.287   0.215   0.04112.319   2.028   0.201   0.046   11.777  2.166   67.269  34.762  1452.928        53.36-7.209   2.522   -367.069        36.068  -329.921        62.280  -30.121 8.255   8     
+9       12      11      -       -100.932        9.238   10.829  0.016   0.132   0.01218.562   0.153   0.108   0.004   17.755  0.101   32.367  14.729  1645.305        104.797       -18.030 2.335   -232.574        32.271  -239.830        42.371  -39.624 7.4289
 ...
 </pre>
 </details>
@@ -1511,16 +1517,16 @@ In this run we also had a `caprieval` after the clustering of the rigid body mod
 <i>View the pre-calculated 5_caprieval/capri_clt.tsv file</i> <i class="material-icons">expand_more</i>
  </summary>
 <pre>
-luster_rank    cluster_id      n       under_eval      score   score_std       irmsd   irmsd_std       fnat    fnat_std        lrmsd   lrmsd_std       dockq   dockq_std       air     air_std bsa     bsa_std desolv  desolv_std      elec    elec_std        total   total_std       vdw     vdw_std caprieval_rank
-1       4       20      -       -32.856 0.533   8.410   0.035   0.056   0.000   16.588  0.218   0.098   0.002   124.256 24.532  1065.697        38.144  -16.689 0.709   -6.744  0.291   116.646 27.428  -0.865  3.459   1
-2       18      7       -       -32.414 0.110   4.179   0.038   0.306   0.019   7.728   0.105   0.323   0.005   202.292 47.410  1058.705        25.612  -16.835 0.581   -6.903  0.482   184.302 52.002  -11.087 4.822   2
-3       11      15      -       -31.515 0.233   1.350   0.190   0.514   0.024   3.046   0.805   0.651   0.048   78.437  33.653  1145.055        41.997  -12.978 0.175   -7.768  1.150   60.382  39.543  -10.287 7.148   3
-4       1       20      -       -31.236 0.236   1.284   0.040   0.556   0.000   2.864   0.281   0.677   0.011   131.928 45.555  1219.428        39.426  -13.313 0.388   -6.916  0.117   111.705 48.278  -13.307 3.521   4
-5       21      7       -       -31.142 1.942   8.839   0.522   0.049   0.012   16.884  0.430   0.093   0.007   95.692  53.937  893.915 67.400  -15.663 3.094   -7.449  1.286   83.407  60.857  -4.835  6.370   5
-6       5       20      -       -29.475 0.296   7.493   0.661   0.076   0.012   12.982  1.199   0.140   0.013   106.772 69.264  981.186 49.018  -12.258 0.686   -8.331  0.482   84.322  76.530  -14.119 7.722   6
-7       26      5       -       -28.704 0.755   3.229   0.706   0.327   0.041   6.579   0.763   0.383   0.055   83.999  32.448  1017.403        165.642 -13.274 2.333   -5.973  1.267   65.694  38.740  -12.333 6.890   7
-8       2       20      -       -28.470 0.706   4.247   0.060   0.118   0.012   7.243   0.105   0.270   0.007   153.547 91.575  1000.080        27.610  -11.114 0.595   -8.809  0.321   136.608 96.503  -8.130  5.374   8
-9       33      4       -       -26.485 0.685   10.716  0.057   0.076   0.012   18.370  0.215   0.091   0.004   186.693 110.722 996.022 75.935  -13.582 1.714   -4.750  0.313   175.985 115.674 -5.959  5.733   9
+cluster_rank    cluster_id      n       under_eval      score   score_std       irmsdirmsd_std        fnat    fnat_std        lrmsd   lrmsd_std       dockq   dockq_std    ilrmsd   ilrmsd_std      air     air_std bsa     bsa_std desolv  desolv_std      elec elec_std total   total_std       vdw     vdw_std caprieval_rank
+1       4       20      -       -32.647 0.718   8.443   0.050   0.056   0.000   16.670.440    0.098   0.003   15.142  0.029   103.171 30.153  1037.440        40.574  -16.600       0.384   -6.642  0.367   90.292  34.825  -6.237  4.862   1
+2       1       20      -       -32.078 0.309   1.193   0.052   0.563   0.012   2.3440.382    0.701   0.015   2.241   0.176   144.927 37.448  1185.507        24.515  -14.154       0.495   -7.458  0.197   131.527 41.553  -5.942  4.909   2
+3       11      15      -       -31.524 0.512   2.591   0.043   0.306   0.000   5.8830.150    0.411   0.006   5.951   0.125   238.270 90.904  838.533 10.610  -17.621 0.383-7.971   0.168   237.269 95.233  6.969   4.900   3
+4       23      6       -       -31.175 0.237   4.180   0.009   0.285   0.012   7.7030.015    0.316   0.004   8.171   0.036   217.839 78.900  1071.035        16.129  -15.642       0.348   -6.892  0.257   199.998 83.806  -10.949 5.140   4
+5       32      4       -       -30.152 1.356   7.126   0.074   0.069   0.024   16.690.938    0.106   0.014   12.952  0.455   286.907 150.515 1041.192        37.687  -13.618       0.880   -8.950  0.629   273.851 150.190 -4.106  4.566   5
+6       33      4       -       -29.431 2.824   2.660   0.894   0.326   0.121   7.1353.087    0.407   0.141   6.418   2.586   124.179 48.395  917.899 78.204  -13.272 2.489-8.230   0.566   116.856 51.814  0.907   4.084   6
+7       2       20      -       -27.915 0.952   4.133   0.017   0.139   0.020   7.0510.018    0.282   0.007   7.455   0.023   264.450 31.588  1014.276        17.755  -11.711       0.867   -8.673  0.226   252.511 36.667  -3.266  5.371   7
+8       17      11      -       -27.474 1.291   6.676   0.703   0.063   0.012   11.461.246    0.157   0.014   12.049  1.207   303.023 57.328  963.135 62.068  -12.556 1.856-8.338   0.587   296.748 55.220  2.063   6.790   8
+9       13      14      -       -27.374 0.754   10.733  0.011   0.083   0.000   18.250.037    0.094   0.000   17.522  0.031   134.468 43.797  1039.598        13.308  -13.613       0.558   -4.687  0.149   127.422 45.173  -2.360  2.548   9
 ...
 </pre>
 </details>
@@ -1558,7 +1564,7 @@ Use the `extract-capri-stats-clt.sh` script to extract some simple cluster stati
  </summary>
 <pre>
 ==============================================
-== run3-full/02_caprieval/capri_clt.tsv
+== scenario3-cltsel-full/02_caprieval/capri_clt.tsv
 ==============================================
 Total number of acceptable or better clusters:  0  out of  1
 Total number of medium or better clusters:      0  out of  1
@@ -1566,19 +1572,19 @@ Total number of high quality clusters:          0  out of  1
 
 First acceptable cluster - rank:   i-RMSD:   Fnat:   DockQ: 
 First medium cluster     - rank:   i-RMSD:   Fnat:   DockQ:
-Best cluster             - rank:  -  i-RMSD:  7.330  Fnat:  0.125  DockQ:  0.155      
+Best cluster             - rank:  -  i-RMSD:  6.407  Fnat:  0.202  DockQ:  0.264      
 ==============================================
-== run3-full/05_caprieval/capri_clt.tsv
+== scenario3-cltsel-full/05_caprieval/capri_clt.tsv
 ==============================================
-Total number of acceptable or better clusters:  7  out of  33
-Total number of medium or better clusters:      2  out of  33
+Total number of acceptable or better clusters:  6  out of  33
+Total number of medium or better clusters:      1  out of  33
 Total number of high quality clusters:          0  out of  33
 
-First acceptable cluster - rank:  3  i-RMSD:  1.350  Fnat:  0.514  DockQ:  0.651
-First medium cluster     - rank:  3  i-RMSD:  1.350  Fnat:  0.514  DockQ:  0.651      
-Best cluster             - rank:  4  i-RMSD:  1.284  Fnat:  0.556  DockQ:  0.677      
+First acceptable cluster - rank:  2  i-RMSD:  1.193  Fnat:  0.563  DockQ:  0.701
+First medium cluster     - rank:  2  i-RMSD:  1.193  Fnat:  0.563  DockQ:  0.701      
+Best cluster             - rank:  2  i-RMSD:  1.193  Fnat:  0.563  DockQ:  0.701      
 ==============================================
-== run3-full/07_caprieval/capri_clt.tsv
+== scenario3-cltsel-full/07_caprieval/capri_clt.tsv
 ==============================================
 Total number of acceptable or better clusters:  0  out of  1
 Total number of medium or better clusters:      0  out of  1
@@ -1586,27 +1592,27 @@ Total number of high quality clusters:          0  out of  1
 
 First acceptable cluster - rank:   i-RMSD:   Fnat:   DockQ: 
 First medium cluster     - rank:   i-RMSD:   Fnat:   DockQ:
-Best cluster             - rank:  -  i-RMSD:  7.761  Fnat:  0.118  DockQ:  0.135      
+Best cluster             - rank:  -  i-RMSD:  8.237  Fnat:  0.104  DockQ:  0.121      
 ==============================================
-== run3-full/09_caprieval/capri_clt.tsv
+== scenario3-cltsel-full/09_caprieval/capri_clt.tsv
 ==============================================
-Total number of acceptable or better clusters:  1  out of  1
-Total number of medium or better clusters:      1  out of  1
+Total number of acceptable or better clusters:  0  out of  1
+Total number of medium or better clusters:      0  out of  1
 Total number of high quality clusters:          0  out of  1
 
-First acceptable cluster - rank:  -  i-RMSD:  1.022  Fnat:  0.785  DockQ:  0.808
-First medium cluster     - rank:  -  i-RMSD:  1.022  Fnat:  0.785  DockQ:  0.808      
-Best cluster             - rank:  -  i-RMSD:  1.022  Fnat:  0.785  DockQ:  0.808      
+First acceptable cluster - rank:   i-RMSD:   Fnat:   DockQ: 
+First medium cluster     - rank:   i-RMSD:   Fnat:   DockQ:
+Best cluster             - rank:  -  i-RMSD:  4.840  Fnat:  0.361  DockQ:  0.400      
 ==============================================
-== run3-full/12_caprieval/capri_clt.tsv
+== scenario3-cltsel-full/12_caprieval/capri_clt.tsv
 ==============================================
-Total number of acceptable or better clusters:  6  out of  23
-Total number of medium or better clusters:      2  out of  23
-Total number of high quality clusters:          0  out of  23
+Total number of acceptable or better clusters:  4  out of  25
+Total number of medium or better clusters:      2  out of  25
+Total number of high quality clusters:          2  out of  25
 
-First acceptable cluster - rank:  1  i-RMSD:  1.022  Fnat:  0.785  DockQ:  0.808
-First medium cluster     - rank:  1  i-RMSD:  1.022  Fnat:  0.785  DockQ:  0.808      
-Best cluster             - rank:  1  i-RMSD:  1.022  Fnat:  0.785  DockQ:  0.808      
+First acceptable cluster - rank:  1  i-RMSD:  3.857  Fnat:  0.319  DockQ:  0.365
+First medium cluster     - rank:  2  i-RMSD:  0.982  Fnat:  0.805  DockQ:  0.816      
+Best cluster             - rank:  4  i-RMSD:  0.980  Fnat:  0.812  DockQ:  0.819      
 </pre>
 </details>
 
@@ -1624,55 +1630,55 @@ Similarly some simple statistics can be extracted from the single model `capriev
  </summary>
 <pre>
 ==============================================
-== run3-full/02_caprieval/capri_ss.tsv
+== scenario3-cltsel-full/02_caprieval/capri_ss.tsv
 ==============================================
-Total number of acceptable or better models:  351  out of  1000
-Total number of medium or better models:      201  out of  1000
+Total number of acceptable or better models:  365  out of  1000
+Total number of medium or better models:      199  out of  1000
 Total number of high quality models:          0  out of  1000
 
-First acceptable model - rank:  15  i-RMSD:  1.677  Fnat:  0.472  DockQ:  0.568
-First medium model     - rank:  15  i-RMSD:  1.677  Fnat:  0.472  DockQ:  0.568       
-Best model             - rank:  48  i-RMSD:  1.146  Fnat:  0.556  DockQ:  0.713       
+First acceptable model - rank:  3  i-RMSD:  1.153  Fnat:  0.556  DockQ:  0.711
+First medium model     - rank:  3  i-RMSD:  1.153  Fnat:  0.556  DockQ:  0.711        
+Best model             - rank:  46  i-RMSD:  1.145  Fnat:  0.556  DockQ:  0.713       
 ==============================================
-== run3-full/05_caprieval/capri_ss.tsv
+== scenario3-cltsel-full/05_caprieval/capri_ss.tsv
 ==============================================
-Total number of acceptable or better models:  64  out of  351
-Total number of medium or better models:      30  out of  351
-Total number of high quality models:          0  out of  351
+Total number of acceptable or better models:  62  out of  375
+Total number of medium or better models:      22  out of  375
+Total number of high quality models:          0  out of  375
 
-First acceptable model - rank:  15  i-RMSD:  1.677  Fnat:  0.472  DockQ:  0.568
-First medium model     - rank:  15  i-RMSD:  1.677  Fnat:  0.472  DockQ:  0.568       
-Best model             - rank:  47  i-RMSD:  1.146  Fnat:  0.556  DockQ:  0.713       
+First acceptable model - rank:  3  i-RMSD:  1.153  Fnat:  0.556  DockQ:  0.711
+First medium model     - rank:  3  i-RMSD:  1.153  Fnat:  0.556  DockQ:  0.711        
+Best model             - rank:  46  i-RMSD:  1.145  Fnat:  0.556  DockQ:  0.713       
 ==============================================
-== run3-full/07_caprieval/capri_ss.tsv
+== scenario3-cltsel-full/07_caprieval/capri_ss.tsv
 ==============================================
-Total number of acceptable or better models:  79  out of  351
-Total number of medium or better models:      36  out of  351
-Total number of high quality models:          5  out of  351
+Total number of acceptable or better models:  74  out of  375
+Total number of medium or better models:      27  out of  375
+Total number of high quality models:          1  out of  375
 
-First acceptable model - rank:  5  i-RMSD:  1.009  Fnat:  0.778  DockQ:  0.808
-First medium model     - rank:  5  i-RMSD:  1.009  Fnat:  0.778  DockQ:  0.808        
-Best model             - rank:  15  i-RMSD:  0.948  Fnat:  0.722  DockQ:  0.801       
+First acceptable model - rank:  6  i-RMSD:  1.081  Fnat:  0.750  DockQ:  0.771
+First medium model     - rank:  6  i-RMSD:  1.081  Fnat:  0.750  DockQ:  0.771        
+Best model             - rank:  36  i-RMSD:  0.930  Fnat:  0.778  DockQ:  0.822       
 ==============================================
-== run3-full/09_caprieval/capri_ss.tsv
+== scenario3-cltsel-full/09_caprieval/capri_ss.tsv
 ==============================================
-Total number of acceptable or better models:  81  out of  351
-Total number of medium or better models:      37  out of  351
-Total number of high quality models:          7  out of  351
+Total number of acceptable or better models:  74  out of  375
+Total number of medium or better models:      27  out of  375
+Total number of high quality models:          7  out of  375
 
-First acceptable model - rank:  1  i-RMSD:  1.011  Fnat:  0.861  DockQ:  0.835
-First medium model     - rank:  1  i-RMSD:  1.011  Fnat:  0.861  DockQ:  0.835        
-Best model             - rank:  4  i-RMSD:  0.927  Fnat:  0.778  DockQ:  0.827        
+First acceptable model - rank:  1  i-RMSD:  3.718  Fnat:  0.333  DockQ:  0.382
+First medium model     - rank:  3  i-RMSD:  0.991  Fnat:  0.806  DockQ:  0.821        
+Best model             - rank:  60  i-RMSD:  0.896  Fnat:  0.778  DockQ:  0.828       
 ==============================================
-== run3-full/12_caprieval/capri_ss.tsv
+== scenario3-cltsel-full/12_caprieval/capri_ss.tsv
 ==============================================
-Total number of acceptable or better models:  25  out of  92
-Total number of medium or better models:      8  out of  92
-Total number of high quality models:          2  out of  92
+Total number of acceptable or better models:  65  out of  317
+Total number of medium or better models:      27  out of  317
+Total number of high quality models:          7  out of  317
 
-First acceptable model - rank:  1  i-RMSD:  1.011  Fnat:  0.861  DockQ:  0.835
-First medium model     - rank:  1  i-RMSD:  1.011  Fnat:  0.861  DockQ:  0.835        
-Best model             - rank:  4  i-RMSD:  0.927  Fnat:  0.778  DockQ:  0.827        
+First acceptable model - rank:  1  i-RMSD:  3.718  Fnat:  0.333  DockQ:  0.382
+First medium model     - rank:  3  i-RMSD:  0.991  Fnat:  0.806  DockQ:  0.821        
+Best model             - rank:  54  i-RMSD:  0.896  Fnat:  0.778  DockQ:  0.828       
 </pre>
 </details>
 
@@ -1708,6 +1714,25 @@ _**Note**_ that this kind of analysis only makes sense when we know the referenc
 
 <br>
 
+#### Analysis scenario 3: visualising the scores and their components
+
+We have precalculated a number of interactive plots to visualize the scores and their components versus ranks and model quality.
+
+<a class="prompt prompt-info">
+Examine the plots (remember here that higher DockQ values and lower i-RMSD values correspond to better models)
+</a>
+
+Models statistics:
+
+* [iRMSD versus HADDOCK score](plots/scenario3/irmsd_score.html){:target="_blank"}
+* [DockQ versus HADDOCK score](plots/scenario3/dockq_score.html){:target="_blank"}
+
+Cluster statistics (distributions of values per cluster ordered according to their HADDOCK rank):
+
+* [HADDOCK scores](plots/scenario3/score_clt.html){:target="_blank"}
+* [iRMSD](plots/scenario3/irmsd_clt.html){:target="_blank"}
+* [DockQ](plots/scenario3/dockq_clt.html){:target="_blank"}
+
 <hr>
 
 ### Comparing the performance of the three scenarios
@@ -1716,41 +1741,42 @@ Clearly all three scenarios give good results with an acceptable cluster in all
 
 {% highlight shell %}
 ==============================================
-== run1-full/11_caprieval/capri_ss.tsv
+== scenario1-full/11_caprieval/capri_ss.tsv
 ==============================================
-Total number of acceptable or better models:  8  out of  16
-Total number of medium or better models:      5  out of  16
-Total number of high quality models:          2  out of  16
+Total number of acceptable or better models:  141  out of  185
+Total number of medium or better models:      116  out of  185
+Total number of high quality models:          34  out of  185
 
-First acceptable model - rank:  1  i-RMSD:  0.894  Fnat:  0.889  DockQ:  0.866
-First medium model     - rank:  1  i-RMSD:  0.894  Fnat:  0.889  DockQ:  0.866        
-Best model             - rank:  1  i-RMSD:  0.894  Fnat:  0.889  DockQ:  0.866        
+First acceptable model - rank:  1  i-RMSD:  0.907  Fnat:  0.917  DockQ:  0.871
+First medium model     - rank:  1  i-RMSD:  0.907  Fnat:  0.917  DockQ:  0.871        
+Best model             - rank:  36  i-RMSD:  0.807  Fnat:  0.833  DockQ:  0.862       
 
 ==============================================
-== run2-full/11_caprieval/capri_ss.tsv
+== scenario2-mdref-full/11_caprieval/capri_ss.tsv
 ==============================================
-Total number of acceptable or better models:  10  out of  20
-Total number of medium or better models:      5  out of  20
-Total number of high quality models:          1  out of  20
+Total number of acceptable or better models:  141  out of  186
+Total number of medium or better models:      118  out of  186
+Total number of high quality models:          15  out of  186
 
-First acceptable model - rank:  1  i-RMSD:  1.096  Fnat:  0.917  DockQ:  0.836
-First medium model     - rank:  1  i-RMSD:  1.096  Fnat:  0.917  DockQ:  0.836        
-Best model             - rank:  4  i-RMSD:  0.929  Fnat:  0.917  DockQ:  0.870        
+First acceptable model - rank:  1  i-RMSD:  1.129  Fnat:  0.806  DockQ:  0.804
+First medium model     - rank:  1  i-RMSD:  1.129  Fnat:  0.806  DockQ:  0.804        
+Best model             - rank:  50  i-RMSD:  0.891  Fnat:  0.861  DockQ:  0.855        
 
 ==============================================
-== run3-full/12_caprieval/capri_ss.tsv
+== scenario3-cltsel-full/12_caprieval/capri_ss.tsv
 ==============================================
-Total number of acceptable or better models:  25  out of  92
-Total number of medium or better models:      8  out of  92
-Total number of high quality models:          2  out of  92
+Total number of acceptable or better models:  65  out of  317
+Total number of medium or better models:      27  out of  317
+Total number of high quality models:          7  out of  317
 
-First acceptable model - rank:  1  i-RMSD:  1.011  Fnat:  0.861  DockQ:  0.835
-First medium model     - rank:  1  i-RMSD:  1.011  Fnat:  0.861  DockQ:  0.835        
-Best model             - rank:  4  i-RMSD:  0.927  Fnat:  0.778  DockQ:  0.827        
+First acceptable model - rank:  1  i-RMSD:  3.718  Fnat:  0.333  DockQ:  0.382
+First medium model     - rank:  3  i-RMSD:  0.991  Fnat:  0.806  DockQ:  0.821        
+Best model             - rank:  54  i-RMSD:  0.896  Fnat:  0.778  DockQ:  0.828        
 
 {% endhighlight %}
 
-While the three scerios show similar results, the combination of the flexible and energy minimization refinment has the most acceptable or better models.
+While the first two scenarios show similar results, we can observe that scenario 3 produces a higher count of clusters, i.e. a higher conformational diversity than the other scenarios. 
+This difference is most probably a consequence of the clustering step carried out after the rigidbody docking. In fact, this additional step allowed us to select the best models of each clusters, retaining the diversity produced in the riigid body step, while selecting the overall best ranked models in the first two scenarios showed lower diversity.
 
 <hr>
 <hr>

From 7b2efa1397c5d9542171a4f274819bf6fdb97e4c Mon Sep 17 00:00:00 2001
From: Raphaelle Versini <rapha.versini@gmail.com>
Date: Fri, 6 Sep 2024 20:36:48 +0200
Subject: [PATCH 06/15] archive to be added

---
 education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md b/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md
index 10b8b542a..b44ed5be4 100644
--- a/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md
+++ b/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md
@@ -44,7 +44,7 @@ Further we are providing pre-processed PDB files for docking and analysis (but t
 preprocessing of those files will also be explained in this tutorial). The files have been processed
 to facilitate their use in HADDOCK and for allowing comparison with the known reference
 structure of the complex. For this _download and unzip the following_
-[zip archive](https://surfdrive.surf.nl/files/index.php/s/HvXxgxCTY1DiPsV){:target="_blank"}
+[zip archive](){:target="_blank"}
 _and note the location of the extracted PDB files in your system_. In it you should find the following directories:
 
 * `haddock3`: Contains HADDOCK3 configuration and job files for the various scenarios in this tutorial

From 64dde94407d416a227859b8ee13517c12356413f Mon Sep 17 00:00:00 2001
From: Raphaelle Versini <rapha.versini@gmail.com>
Date: Wed, 18 Sep 2024 14:49:03 +0200
Subject: [PATCH 07/15] paratope/epitope corrected

---
 education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md b/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md
index b44ed5be4..00ae7059f 100644
--- a/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md
+++ b/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md
@@ -1475,7 +1475,7 @@ Cluster statistics (distributions of values per cluster ordered according to the
 
 <hr>
 
-### Analysis scenario 3: Paratope - NMR-epitope as active
+### Analysis scenario 3: 
 
 Let us now analyse the docking results for this scenario. Use for that either your own run or a pre-calculated run provided in the `runs` directory.
 Go into the _analysis/_caprieval_analysis_  directory of the respective run directory and
@@ -1554,7 +1554,7 @@ cluster_rank    cluster_id      n       under_eval      score   score_std
 Use the `extract-capri-stats-clt.sh` script to extract some simple cluster statistics for this run.
 
 <a class="prompt prompt-cmd">
-   ./scripts/extract-capri-stats-clt.sh runs/scenario2b-NMR-epitope-act/
+   ./scripts/extract-capri-stats-clt.sh runs/scenario3/
 </a>
 
 
@@ -1621,7 +1621,7 @@ Best cluster             - rank:  4  i-RMSD:  0.980  Fnat:  0.812  DockQ:  0.819
 Similarly some simple statistics can be extracted from the single model `caprieval` `capri_ss.tsv` files with the `extract-capri-stats.sh` script:
 
 <a class="prompt prompt-cmd">
-./scripts/extract-capri-stats.sh ./runs/scenario2b-NMR-epitope-pass
+./scripts/extract-capri-stats.sh ./runs/scenario3
 </a>
 
 <details style="background-color:#DAE4E7">

From 1adbc7e971aacc14b0fad5ffeaaee8c9fd754f96 Mon Sep 17 00:00:00 2001
From: Raphaelle Versini <rapha.versini@gmail.com>
Date: Wed, 18 Sep 2024 15:25:16 +0200
Subject: [PATCH 08/15] removal of freesasa

---
 education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md b/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md
index 00ae7059f..094ba7959 100644
--- a/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md
+++ b/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md
@@ -183,9 +183,6 @@ registration form, and then follow the [installation instructions](https://www.b
 
 ### Auxiliary software
 
-**[FreeSASA][link-freesasa]**: FreeSASA will be used to identify surface-accessible residues
-(pre-calculated data are provided).
-
 **[PDB-tools][link-pdbtools]**: A useful collection of Python scripts for the
 manipulation (renumbering, changing chain and segIDs...) of PDB files is freely
 available from our GitHub repository. `pdb-tools` is automatically installed
@@ -1884,7 +1881,6 @@ So stay tuned!
 [installation]: https://www.bonvinlab.org/haddock3/INSTALL.html "Installation"
 [link-cns]: https://cns-online.org "CNS online"
 [link-forum]: https://ask.bioexcel.eu/c/haddock "HADDOCK Forum"
-[link-freesasa]: https://freesasa.github.io "FreeSASA"
 [link-pdbtools]:http://www.bonvinlab.org/pdb-tools/ "PDB-Tools"
 [link-pymol]: https://www.pymol.org/ "PyMOL"
 [nat-pro]: https://www.nature.com/nprot/journal/v5/n5/abs/nprot.2010.32.html "Nature protocol"

From 8de7d9d83d4a1565449037713e2df09baa662d2b Mon Sep 17 00:00:00 2001
From: Raphaelle Versini <rapha.versini@gmail.com>
Date: Wed, 18 Sep 2024 18:30:28 +0200
Subject: [PATCH 09/15] scenario2 removed

---
 .../HADDOCK3-protein-protein-basic/index.md   | 380 ++----------------
 .../plots/GOGO_scenario2/dockq_clt.html       |  23 ++
 .../plots/GOGO_scenario2/dockq_score.html     |  23 ++
 .../plots/GOGO_scenario2/irmsd_clt.html       |  23 ++
 .../plots/GOGO_scenario2/irmsd_score.html     |  23 ++
 .../plots/GOGO_scenario2/score_clt.html       |  23 ++
 .../plots/scenario2/dockq_clt.html            |   2 +-
 .../plots/scenario2/dockq_score.html          |   2 +-
 .../plots/scenario2/irmsd_clt.html            |   2 +-
 .../plots/scenario2/irmsd_score.html          |   2 +-
 .../plots/scenario2/score_clt.html            |   2 +-
 .../plots/scenario3/dockq_clt.html            |  23 --
 .../plots/scenario3/dockq_score.html          |  23 --
 .../plots/scenario3/irmsd_clt.html            |  23 --
 .../plots/scenario3/irmsd_score.html          |  23 --
 .../plots/scenario3/score_clt.html            |  23 --
 16 files changed, 148 insertions(+), 472 deletions(-)
 create mode 100644 education/HADDOCK3/HADDOCK3-protein-protein-basic/plots/GOGO_scenario2/dockq_clt.html
 create mode 100644 education/HADDOCK3/HADDOCK3-protein-protein-basic/plots/GOGO_scenario2/dockq_score.html
 create mode 100644 education/HADDOCK3/HADDOCK3-protein-protein-basic/plots/GOGO_scenario2/irmsd_clt.html
 create mode 100644 education/HADDOCK3/HADDOCK3-protein-protein-basic/plots/GOGO_scenario2/irmsd_score.html
 create mode 100644 education/HADDOCK3/HADDOCK3-protein-protein-basic/plots/GOGO_scenario2/score_clt.html
 delete mode 100644 education/HADDOCK3/HADDOCK3-protein-protein-basic/plots/scenario3/dockq_clt.html
 delete mode 100644 education/HADDOCK3/HADDOCK3-protein-protein-basic/plots/scenario3/dockq_score.html
 delete mode 100644 education/HADDOCK3/HADDOCK3-protein-protein-basic/plots/scenario3/irmsd_clt.html
 delete mode 100644 education/HADDOCK3/HADDOCK3-protein-protein-basic/plots/scenario3/irmsd_score.html
 delete mode 100644 education/HADDOCK3/HADDOCK3-protein-protein-basic/plots/scenario3/score_clt.html

diff --git a/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md b/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md
index 094ba7959..f91c7657c 100644
--- a/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md
+++ b/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md
@@ -516,8 +516,7 @@ to compare models to either the best scoring model (if no reference is given) or
 we have at hand. This will directly allow us to assess the performance of the protocol for the following three scenarios:
 
 1. Scenario 1: 1000 rigidbody docking models, selection of top200 and flexible refinement + EM 
-2. Scenario 2a: 1000 rigidbody docking models, selection of top200 and flexible refinement + final refinement in explicit solvent (water)
-3. Scenario 2b: 1000 rigidbody docking models, FCC clustering and selection of max 20 models per cluster followed by flexible refinement and EM
+3. Scenario 2: 1000 rigidbody docking models, FCC clustering and selection of max 20 models per cluster followed by flexible refinement and EM
 
 The basic workflow for all three scenarios will consists of the following modules, with some differences in the restraints used and some parameter settings (see below):
 
@@ -530,7 +529,7 @@ The basic workflow for all three scenarios will consists of the following module
 7. **`clustfcc`**: *Clustering of models based on the fraction of common contacts (FCC)*
 8. **`caprieval`**: *Calculates CAPRI metrics (i-RMSD, l-RMSD, Fnat, DockQ) with respect to the top scoring model or reference structure if provided*
 
-The input PDB files are the same for all three scenarios. The differences are in the ambiguous interaction restraint files used and the sampling at the rigid body stage in the case of scenario1.
+The input PDB files are the same for all two scenarios. The differences are in the sampling at the rigid body stage.
 
 
 <hr>
@@ -757,99 +756,9 @@ submitting it to the batch system requesting in this local run mode a full node
 
 <hr>
 
-### Scenario 2: 1000 rigidbody docking models, selection of top200 and flexible refinement + final refinement in explicit solvent (water) of those 
+### Scenario 2: 1000 rigidbody docking models, FCC clustering and selection of max 20 models per cluster followed by flexible refinement and EM 
 
-In scenario 2, we proceed to produce 1000 rigidbody docking models, from which 200 will be selected and refined through flexible refinment then water refinment. 
-For the analysis following the docking results, we are using the solved complex [1GGR](https://www.rcsb.org/structure/1GGR), named e2a-hpr_1GGR.pdb.
-The configuration file for this scenario (assuming a batch running mode) is:
-
-{% highlight toml %}
-# ====================================================================
-# Protein-protein docking example with NMR-derived ambiguous interaction restraints
-
-# directory in which the scoring will be done
-run_dir = "scenario2-mdref-full"
-
-# execution mode
-mode = "batch"
-# in which queue the jobs should run, if nothing is defined
-#  it will take the system's default
-# queue = "short"
-# concatenate models inside each job, concat = 5 each .job will produce 5 models
-concat = 5
-#  Limit the number of concurrent submissions to the queue
-queue_limit = 100
-# cns_exec = "path/to/bin/cns" # optional
-
-# molecules to be docked
-molecules =  [
-    "data/e2aP_1F3G.pdb",
-    "data/hpr_ensemble.pdb"
-    ]
-
-# ====================================================================
-# Parameters for each stage are defined below, prefer full paths
-# ====================================================================
-[topoaa]
-autohis = false
-[topoaa.mol1]
-nhisd = 0
-nhise = 1
-hise_1 = 75
-[topoaa.mol2]
-nhisd = 1
-hisd_1 = 76
-nhise = 1
-hise_1 = 15
-
-[rigidbody]
-tolerance = 5
-ambig_fname = "data/e2a-hpr_air.tbl"
-sampling = 1000
-
-[caprieval]
-reference_fname = "data/e2a-hpr_1GGR.pdb"
-
-[seletop]
-select = 200
-
-[caprieval]
-reference_fname = "data/e2a-hpr_1GGR.pdb"
-
-[flexref]
-tolerance = 5
-ambig_fname = "data/e2a-hpr_air.tbl"
-
-[caprieval]
-reference_fname = "data/e2a-hpr_1GGR.pdb"
-
-[mdref]
-tolerance = 5
-ambig_fname = "data/e2a-hpr_air.tbl"
-
-[caprieval]
-reference_fname = "data/e2a-hpr_1GGR.pdb"
-
-[clustfcc]
-
-[seletopclusts]
-top_models = 500
-
-[caprieval]
-reference_fname = "data/e2a-hpr_1GGR.pdb"
-
-# ====================================================================
-{% endhighlight %}
-
-This configuration is provided in the `haddock3` directory of the downloaded data set for this tutorial as `docking-protein-protein-mdref-full.cfg`. 
-
-If you have everything ready, you can launch haddock3 either from the command line, or, better, submitting it to the batch system requesting in this local run mode a full node (see local execution mode above).
-
-<hr>
-
-### Scenario 3: 1000 rigidbody docking models, FCC clustering and selection of max 20 models per cluster followed by flexible refinement and EM 
-
-In scenario 3, we proceed to produce 1000 rigidbody docking models, from which we proceed to do a first clustering analysis. From the top clusters a flexible refinment then energy minization is done. 
+In scenario 2, we proceed to produce 1000 rigidbody docking models, from which we proceed to do a first clustering analysis. From the top clusters a flexible refinment then energy minization is done. 
 For the analysis following the docking results, we are using the solved complex [1GGR](https://www.rcsb.org/structure/1GGR), named e2a-hpr_1GGR.pdb.
 The configuration file for this scenario (assuming a batch running mode) is:
 
@@ -859,7 +768,7 @@ The configuration file for this scenario (assuming a batch running mode) is:
 # ====================================================================
 
 # directory in which the scoring will be done
-run_dir = "scenario3-cltsel-full"
+run_dir = "scenario2-cltsel-full"
 
 # execution mode
 mode = "batch"
@@ -945,7 +854,7 @@ If you have everything ready, you can launch haddock3 either from the command li
 Once your run has completed inspect the content of the resulting directory. You will find the various steps (modules) of the defined workflow numbered sequentially, e.g.:
 
 {% highlight shell %}
-> ls scenario3/
+> ls scenario2/
     00_topoaa/
     01_rigidbody/
     02_caprieval/
@@ -1257,228 +1166,6 @@ Go into the _analysis/_caprieval_analysis_  directory of the respective run dire
 
 <a class="prompt prompt-info">Inspect the final cluster statistics in _capri_clt.tsv_ file </a>
 
-<details style="background-color:#DAE4E7">
-<summary>
-<i>View the pre-calculated 09_caprieval/capri_clt.tsv file</i> <i class="material-icons">expand_more</i>
- </summary>
-<pre>
-cluster_rank    cluster_id      n       under_eval      score   score_std       irmsdirmsd_std        fnat    fnat_std        lrmsd   lrmsd_std       dockq   dockq_std    ilrmsd   ilrmsd_std      air     air_std bsa     bsa_std desolv  desolv_std      elec elec_std total   total_std       vdw     vdw_std caprieval_rank
-1       1       135     -       -150.688        4.693   1.179   0.175   0.792   0.1091.950    0.688   0.786   0.071   1.944   0.456   19.494  12.609  1690.822        47.60-18.212  3.894   -419.064        25.202  -450.182        33.243  -50.613 2.503   1     
-2       2       43      -       -127.775        6.046   8.432   0.520   0.139   0.05916.534   1.229   0.127   0.029   15.098  1.051   23.416  26.105  1511.128        127.638       -13.659 2.707   -383.687        32.493  -399.990        32.684  -39.720 4.6852
-3       3       8       -       -88.830 6.416   3.701   0.393   0.354   0.012   7.6140.634    0.352   0.024   7.684   0.663   14.781  8.117   1263.165        122.190 -22.622       4.743   -174.373        32.041  -192.404        36.319  -32.812 3.443   3     
-</pre>
-</details>
-
-<br>
-
-<a class="prompt prompt-question">How many clusters are generated?</a>
-
-<a class="prompt prompt-question">Look at the score of the first few clusters: Are they significantly different if you consider their average scores and standard deviations?</a>
-
-Since for this tutorial we have at hand the crystal structure of the complex, we provided it as reference to the `caprieval` modules.
-This means that the iRMSD, lRMSD, Fnat and DockQ statistics report on the quality of the docked model compared to the reference crystal structure.
-
-<a class="prompt prompt-question">How many clusters of acceptable or better quality have been generate according to CAPRI criteria?</a>
-
-<a class="prompt prompt-question">What is the rank of the best cluster generated?</a>
-
-<a class="prompt prompt-question">What is the rank of the first acceptable of better cluster generated?</a>
-
-
-We are providing in the `scripts` a simple script that extract some cluster statistics for acceptable or better clusters from the `caprieval` steps.
-To use is simply call the script with as argument the run directory you want to analyze, e.g.:
-
-<a class="prompt prompt-cmd">
-./scripts/extract-capri-stats-clt.sh ./run2-mdref-full
-</a>
-
-<details style="background-color:#DAE4E7">
-<summary>
-<i>View the output of the script</i> <i class="material-icons">expand_more</i>
- </summary>
-<pre>
-==============================================
-==============================================
-== scenario2-mdref-full/02_caprieval/capri_clt.tsv
-==============================================
-Total number of acceptable or better clusters:  0  out of  1
-Total number of medium or better clusters:      0  out of  1
-Total number of high quality clusters:          0  out of  1
-
-First acceptable cluster - rank:   i-RMSD:   Fnat:   DockQ: 
-First medium cluster     - rank:   i-RMSD:   Fnat:   DockQ:
-Best cluster             - rank:  -  i-RMSD:  6.407  Fnat:  0.202  DockQ:  0.264      
-==============================================
-== scenario2-mdref-full/04_caprieval/capri_clt.tsv
-==============================================
-Total number of acceptable or better clusters:  0  out of  1
-Total number of medium or better clusters:      0  out of  1
-Total number of high quality clusters:          0  out of  1
-
-First acceptable cluster - rank:   i-RMSD:   Fnat:   DockQ: 
-First medium cluster     - rank:   i-RMSD:   Fnat:   DockQ:
-Best cluster             - rank:  -  i-RMSD:  6.407  Fnat:  0.202  DockQ:  0.264      
-==============================================
-== scenario2-mdref-full/06_caprieval/capri_clt.tsv
-==============================================
-Total number of acceptable or better clusters:  1  out of  1
-Total number of medium or better clusters:      0  out of  1
-Total number of high quality clusters:          0  out of  1
-
-First acceptable cluster - rank:  -  i-RMSD:  2.976  Fnat:  0.611  DockQ:  0.601
-First medium cluster     - rank:   i-RMSD:   Fnat:   DockQ:
-Best cluster             - rank:  -  i-RMSD:  2.976  Fnat:  0.611  DockQ:  0.601      
-==============================================
-== scenario2-mdref-full/08_caprieval/capri_clt.tsv
-==============================================
-Total number of acceptable or better clusters:  1  out of  1
-Total number of medium or better clusters:      1  out of  1
-Total number of high quality clusters:          0  out of  1
-
-First acceptable cluster - rank:  -  i-RMSD:  1.179  Fnat:  0.792  DockQ:  0.786
-First medium cluster     - rank:  -  i-RMSD:  1.179  Fnat:  0.792  DockQ:  0.786      
-Best cluster             - rank:  -  i-RMSD:  1.179  Fnat:  0.792  DockQ:  0.786      
-==============================================
-== scenario2-mdref-full/11_caprieval/capri_clt.tsv
-==============================================
-Total number of acceptable or better clusters:  2  out of  3
-Total number of medium or better clusters:      1  out of  3
-Total number of high quality clusters:          0  out of  3
-
-First acceptable cluster - rank:  1  i-RMSD:  1.179  Fnat:  0.792  DockQ:  0.786
-First medium cluster     - rank:  1  i-RMSD:  1.179  Fnat:  0.792  DockQ:  0.786      
-Best cluster             - rank:  1  i-RMSD:  1.179  Fnat:  0.792  DockQ:  0.786
-</pre>
-</details>
-
-<br>
-
-Similarly some simple statistics can be extracted from the single model `caprieval` `capri_ss.tsv` files with the `extract-capri-stats.sh` script:
-
-<a class="prompt prompt-cmd">
-./scripts/extract-capri-stats.sh ./run2-mdref-full
-</a>
-
-<details style="background-color:#DAE4E7">
-<summary>
-<i>View the output of the script</i> <i class="material-icons">expand_more</i>
- </summary>
-<pre>
-==============================================
-== scenario2-mdref-full/02_caprieval/capri_ss.tsv
-==============================================
-Total number of acceptable or better models:  365  out of  1000
-Total number of medium or better models:      199  out of  1000
-Total number of high quality models:          0  out of  1000
-
-First acceptable model - rank:  3  i-RMSD:  1.153  Fnat:  0.556  DockQ:  0.711
-First medium model     - rank:  3  i-RMSD:  1.153  Fnat:  0.556  DockQ:  0.711        
-Best model             - rank:  46  i-RMSD:  1.145  Fnat:  0.556  DockQ:  0.713       
-==============================================
-== scenario2-mdref-full/04_caprieval/capri_ss.tsv
-==============================================
-Total number of acceptable or better models:  144  out of  200
-Total number of medium or better models:      137  out of  200
-Total number of high quality models:          0  out of  200
-
-First acceptable model - rank:  3  i-RMSD:  1.153  Fnat:  0.556  DockQ:  0.711
-First medium model     - rank:  3  i-RMSD:  1.153  Fnat:  0.556  DockQ:  0.711        
-Best model             - rank:  46  i-RMSD:  1.145  Fnat:  0.556  DockQ:  0.713       
-==============================================
-== scenario2-mdref-full/06_caprieval/capri_ss.tsv
-==============================================
-Total number of acceptable or better models:  147  out of  200
-Total number of medium or better models:      118  out of  200
-Total number of high quality models:          20  out of  200
-
-First acceptable model - rank:  2  i-RMSD:  1.221  Fnat:  0.694  DockQ:  0.727
-First medium model     - rank:  2  i-RMSD:  1.221  Fnat:  0.694  DockQ:  0.727        
-Best model             - rank:  30  i-RMSD:  0.883  Fnat:  0.750  DockQ:  0.823       
-==============================================
-== scenario2-mdref-full/08_caprieval/capri_ss.tsv
-==============================================
-Total number of acceptable or better models:  146  out of  200
-Total number of medium or better models:      121  out of  200
-Total number of high quality models:          15  out of  200
-
-First acceptable model - rank:  1  i-RMSD:  1.129  Fnat:  0.806  DockQ:  0.804
-First medium model     - rank:  1  i-RMSD:  1.129  Fnat:  0.806  DockQ:  0.804        
-Best model             - rank:  51  i-RMSD:  0.891  Fnat:  0.861  DockQ:  0.855       
-==============================================
-== scenario2-mdref-full/11_caprieval/capri_ss.tsv
-==============================================
-Total number of acceptable or better models:  141  out of  186
-Total number of medium or better models:      118  out of  186
-Total number of high quality models:          15  out of  186
-
-First acceptable model - rank:  1  i-RMSD:  1.129  Fnat:  0.806  DockQ:  0.804
-First medium model     - rank:  1  i-RMSD:  1.129  Fnat:  0.806  DockQ:  0.804        
-Best model             - rank:  50  i-RMSD:  0.891  Fnat:  0.861  DockQ:  0.855
-</pre>
-</details>
-<br>
-
-_**Note**_ that this kind of analysis only makes sense when we know the reference complex and for benchmarking / performance analysis purposes.
-
-<a class="prompt prompt-info">Look at the single structure statistics provided by the script</a>
-
-<a class="prompt prompt-question">How does the quality of the model changes after flexible refinement? Consider here the various metrics.</a>
-
-<details style="background-color:#DAE4E7">
-  <summary style="bold">
-    <i>Answer</i> <i class="material-icons">expand_more</i>
-  </summary>
-  <p>
-    In terms of iRMSD values we only observe very small differences with a slight increase.
-    The fraction of native contacts and the DockQ scores are however improving much more after flexible refinement.
-    All this will of course depend on how different are the bound and unbound conformations and the amount of data
-    used to drive the docking process. In general, from our experience, the more and better data at hand,
-    the larger the conformational changes that can be induced.
-  </p>
-</details>
-<br>
-
-<a class="prompt prompt-question">Is the best model always rank as first?</a>
-
-<details style="background-color:#DAE4E7">
-  <summary style="bold">
-    <i>Answer</i> <i class="material-icons">expand_more</i>
-  </summary>
-  <p>
-    This is clearly not the case. The scoring function is not perfect, but does a reasonable job in ranking models of acceptable or better quality on top in this case.
-  </p>
-</details>
-
-<br>
-
-#### Analysis scenario 2: visualising the scores and their components
-
-We have precalculated a number of interactive plots to visualize the scores and their components versus ranks and model quality.
-
-<a class="prompt prompt-info">
-Examine the plots (remember here that higher DockQ values and lower i-RMSD values correspond to better models)
-</a>
-
-Models statistics:
-
-* [iRMSD versus HADDOCK score](plots/scenario2/irmsd_score.html){:target="_blank"}
-* [DockQ versus HADDOCK score](plots/scenario2/dockq_score.html){:target="_blank"}
-
-Cluster statistics (distributions of values per cluster ordered according to their HADDOCK rank):
-
-* [HADDOCK scores](plots/scenario2/score_clt.html){:target="_blank"}
-* [iRMSD](plots/scenario2/irmsd_clt.html){:target="_blank"}
-* [DockQ](plots/scenario2/dockq_clt.html){:target="_blank"}
-
-<hr>
-
-### Analysis scenario 3: 
-
-Let us now analyse the docking results for this scenario. Use for that either your own run or a pre-calculated run provided in the `runs` directory.
-Go into the _analysis/_caprieval_analysis_  directory of the respective run directory and
-
-<a class="prompt prompt-info">Inspect the final cluster statistics in _capri_clt.tsv_ file </a>
-
 <details style="background-color:#DAE4E7">
 <summary>
 <i>View the pre-calculated _caprieval/capri_clt.tsv file</i> <i class="material-icons">expand_more</i>
@@ -1551,7 +1238,7 @@ cluster_rank    cluster_id      n       under_eval      score   score_std
 Use the `extract-capri-stats-clt.sh` script to extract some simple cluster statistics for this run.
 
 <a class="prompt prompt-cmd">
-   ./scripts/extract-capri-stats-clt.sh runs/scenario3/
+   ./scripts/extract-capri-stats-clt.sh runs/scenario2/
 </a>
 
 
@@ -1561,7 +1248,7 @@ Use the `extract-capri-stats-clt.sh` script to extract some simple cluster stati
  </summary>
 <pre>
 ==============================================
-== scenario3-cltsel-full/02_caprieval/capri_clt.tsv
+== scenario2-cltsel-full/02_caprieval/capri_clt.tsv
 ==============================================
 Total number of acceptable or better clusters:  0  out of  1
 Total number of medium or better clusters:      0  out of  1
@@ -1571,7 +1258,7 @@ First acceptable cluster - rank:   i-RMSD:   Fnat:   DockQ:
 First medium cluster     - rank:   i-RMSD:   Fnat:   DockQ:
 Best cluster             - rank:  -  i-RMSD:  6.407  Fnat:  0.202  DockQ:  0.264      
 ==============================================
-== scenario3-cltsel-full/05_caprieval/capri_clt.tsv
+== scenario2-cltsel-full/05_caprieval/capri_clt.tsv
 ==============================================
 Total number of acceptable or better clusters:  6  out of  33
 Total number of medium or better clusters:      1  out of  33
@@ -1581,7 +1268,7 @@ First acceptable cluster - rank:  2  i-RMSD:  1.193  Fnat:  0.563  DockQ:  0.701
 First medium cluster     - rank:  2  i-RMSD:  1.193  Fnat:  0.563  DockQ:  0.701      
 Best cluster             - rank:  2  i-RMSD:  1.193  Fnat:  0.563  DockQ:  0.701      
 ==============================================
-== scenario3-cltsel-full/07_caprieval/capri_clt.tsv
+== scenario2-cltsel-full/07_caprieval/capri_clt.tsv
 ==============================================
 Total number of acceptable or better clusters:  0  out of  1
 Total number of medium or better clusters:      0  out of  1
@@ -1591,7 +1278,7 @@ First acceptable cluster - rank:   i-RMSD:   Fnat:   DockQ:
 First medium cluster     - rank:   i-RMSD:   Fnat:   DockQ:
 Best cluster             - rank:  -  i-RMSD:  8.237  Fnat:  0.104  DockQ:  0.121      
 ==============================================
-== scenario3-cltsel-full/09_caprieval/capri_clt.tsv
+== scenario2-cltsel-full/09_caprieval/capri_clt.tsv
 ==============================================
 Total number of acceptable or better clusters:  0  out of  1
 Total number of medium or better clusters:      0  out of  1
@@ -1601,7 +1288,7 @@ First acceptable cluster - rank:   i-RMSD:   Fnat:   DockQ:
 First medium cluster     - rank:   i-RMSD:   Fnat:   DockQ:
 Best cluster             - rank:  -  i-RMSD:  4.840  Fnat:  0.361  DockQ:  0.400      
 ==============================================
-== scenario3-cltsel-full/12_caprieval/capri_clt.tsv
+== scenario2-cltsel-full/12_caprieval/capri_clt.tsv
 ==============================================
 Total number of acceptable or better clusters:  4  out of  25
 Total number of medium or better clusters:      2  out of  25
@@ -1618,7 +1305,7 @@ Best cluster             - rank:  4  i-RMSD:  0.980  Fnat:  0.812  DockQ:  0.819
 Similarly some simple statistics can be extracted from the single model `caprieval` `capri_ss.tsv` files with the `extract-capri-stats.sh` script:
 
 <a class="prompt prompt-cmd">
-./scripts/extract-capri-stats.sh ./runs/scenario3
+./scripts/extract-capri-stats.sh ./runs/scenario2
 </a>
 
 <details style="background-color:#DAE4E7">
@@ -1627,7 +1314,7 @@ Similarly some simple statistics can be extracted from the single model `capriev
  </summary>
 <pre>
 ==============================================
-== scenario3-cltsel-full/02_caprieval/capri_ss.tsv
+== scenario2-cltsel-full/02_caprieval/capri_ss.tsv
 ==============================================
 Total number of acceptable or better models:  365  out of  1000
 Total number of medium or better models:      199  out of  1000
@@ -1637,7 +1324,7 @@ First acceptable model - rank:  3  i-RMSD:  1.153  Fnat:  0.556  DockQ:  0.711
 First medium model     - rank:  3  i-RMSD:  1.153  Fnat:  0.556  DockQ:  0.711        
 Best model             - rank:  46  i-RMSD:  1.145  Fnat:  0.556  DockQ:  0.713       
 ==============================================
-== scenario3-cltsel-full/05_caprieval/capri_ss.tsv
+== scenario2-cltsel-full/05_caprieval/capri_ss.tsv
 ==============================================
 Total number of acceptable or better models:  62  out of  375
 Total number of medium or better models:      22  out of  375
@@ -1647,7 +1334,7 @@ First acceptable model - rank:  3  i-RMSD:  1.153  Fnat:  0.556  DockQ:  0.711
 First medium model     - rank:  3  i-RMSD:  1.153  Fnat:  0.556  DockQ:  0.711        
 Best model             - rank:  46  i-RMSD:  1.145  Fnat:  0.556  DockQ:  0.713       
 ==============================================
-== scenario3-cltsel-full/07_caprieval/capri_ss.tsv
+== scenario2-cltsel-full/07_caprieval/capri_ss.tsv
 ==============================================
 Total number of acceptable or better models:  74  out of  375
 Total number of medium or better models:      27  out of  375
@@ -1657,7 +1344,7 @@ First acceptable model - rank:  6  i-RMSD:  1.081  Fnat:  0.750  DockQ:  0.771
 First medium model     - rank:  6  i-RMSD:  1.081  Fnat:  0.750  DockQ:  0.771        
 Best model             - rank:  36  i-RMSD:  0.930  Fnat:  0.778  DockQ:  0.822       
 ==============================================
-== scenario3-cltsel-full/09_caprieval/capri_ss.tsv
+== scenario2-cltsel-full/09_caprieval/capri_ss.tsv
 ==============================================
 Total number of acceptable or better models:  74  out of  375
 Total number of medium or better models:      27  out of  375
@@ -1667,7 +1354,7 @@ First acceptable model - rank:  1  i-RMSD:  3.718  Fnat:  0.333  DockQ:  0.382
 First medium model     - rank:  3  i-RMSD:  0.991  Fnat:  0.806  DockQ:  0.821        
 Best model             - rank:  60  i-RMSD:  0.896  Fnat:  0.778  DockQ:  0.828       
 ==============================================
-== scenario3-cltsel-full/12_caprieval/capri_ss.tsv
+== scenario2-cltsel-full/12_caprieval/capri_ss.tsv
 ==============================================
 Total number of acceptable or better models:  65  out of  317
 Total number of medium or better models:      27  out of  317
@@ -1711,7 +1398,7 @@ _**Note**_ that this kind of analysis only makes sense when we know the referenc
 
 <br>
 
-#### Analysis scenario 3: visualising the scores and their components
+#### Analysis scenario 2: visualising the scores and their components
 
 We have precalculated a number of interactive plots to visualize the scores and their components versus ranks and model quality.
 
@@ -1721,18 +1408,18 @@ Examine the plots (remember here that higher DockQ values and lower i-RMSD value
 
 Models statistics:
 
-* [iRMSD versus HADDOCK score](plots/scenario3/irmsd_score.html){:target="_blank"}
-* [DockQ versus HADDOCK score](plots/scenario3/dockq_score.html){:target="_blank"}
+* [iRMSD versus HADDOCK score](plots/scenario2/irmsd_score.html){:target="_blank"}
+* [DockQ versus HADDOCK score](plots/scenario2/dockq_score.html){:target="_blank"}
 
 Cluster statistics (distributions of values per cluster ordered according to their HADDOCK rank):
 
-* [HADDOCK scores](plots/scenario3/score_clt.html){:target="_blank"}
-* [iRMSD](plots/scenario3/irmsd_clt.html){:target="_blank"}
-* [DockQ](plots/scenario3/dockq_clt.html){:target="_blank"}
+* [HADDOCK scores](plots/scenario2/score_clt.html){:target="_blank"}
+* [iRMSD](plots/scenario2/irmsd_clt.html){:target="_blank"}
+* [DockQ](plots/scenario2/dockq_clt.html){:target="_blank"}
 
 <hr>
 
-### Comparing the performance of the three scenarios
+### Comparing the performance of the two scenarios
 
 Clearly all three scenarios give good results with an acceptable cluster in all three cases ranked at the top:
 
@@ -1746,21 +1433,10 @@ Total number of high quality models:          34  out of  185
 
 First acceptable model - rank:  1  i-RMSD:  0.907  Fnat:  0.917  DockQ:  0.871
 First medium model     - rank:  1  i-RMSD:  0.907  Fnat:  0.917  DockQ:  0.871        
-Best model             - rank:  36  i-RMSD:  0.807  Fnat:  0.833  DockQ:  0.862       
-
-==============================================
-== scenario2-mdref-full/11_caprieval/capri_ss.tsv
-==============================================
-Total number of acceptable or better models:  141  out of  186
-Total number of medium or better models:      118  out of  186
-Total number of high quality models:          15  out of  186
-
-First acceptable model - rank:  1  i-RMSD:  1.129  Fnat:  0.806  DockQ:  0.804
-First medium model     - rank:  1  i-RMSD:  1.129  Fnat:  0.806  DockQ:  0.804        
-Best model             - rank:  50  i-RMSD:  0.891  Fnat:  0.861  DockQ:  0.855        
+Best model             - rank:  36  i-RMSD:  0.807  Fnat:  0.833  DockQ:  0.862            
 
 ==============================================
-== scenario3-cltsel-full/12_caprieval/capri_ss.tsv
+== scenario2-cltsel-full/12_caprieval/capri_ss.tsv
 ==============================================
 Total number of acceptable or better models:  65  out of  317
 Total number of medium or better models:      27  out of  317
@@ -1772,7 +1448,7 @@ Best model             - rank:  54  i-RMSD:  0.896  Fnat:  0.778  DockQ:  0.828
 
 {% endhighlight %}
 
-While the first two scenarios show similar results, we can observe that scenario 3 produces a higher count of clusters, i.e. a higher conformational diversity than the other scenarios. 
+While the first two scenarios show similar results, we can observe that scenario 2 produces a higher count of clusters, i.e. a higher conformational diversity than the other scenarios. 
 This difference is most probably a consequence of the clustering step carried out after the rigidbody docking. In fact, this additional step allowed us to select the best models of each clusters, retaining the diversity produced in the riigid body step, while selecting the overall best ranked models in the first two scenarios showed lower diversity.
 
 <hr>
diff --git a/education/HADDOCK3/HADDOCK3-protein-protein-basic/plots/GOGO_scenario2/dockq_clt.html b/education/HADDOCK3/HADDOCK3-protein-protein-basic/plots/GOGO_scenario2/dockq_clt.html
new file mode 100644
index 000000000..bd35873ea
--- /dev/null
+++ b/education/HADDOCK3/HADDOCK3-protein-protein-basic/plots/GOGO_scenario2/dockq_clt.html
@@ -0,0 +1,23 @@
+
+    <div>
+    <script type="text/javascript">window.PlotlyConfig = { MathJaxConfig: 'local' };</script>
+    <script src="https://cdn.plot.ly/plotly-2.16.1.min.js"></script>
+    <div id="plot1" class="plotly-graph-div" style="height:800px; width:1000px;">
+    </div>
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288"],"x":[0.109,0.11,0.111,0.102,0.099,0.101,0.096,0.103,0.092,0.102,0.084],"y":[-111.279,-108.805,-93.602,-90.041,-88.144,-86.93,-79.594,-76.698,-72.423,-62.457,-62.115],"type":"scatter"},{"error_x":{"array":[0.0035355339059327407],"type":"data","visible":true},"error_y":{"array":[9.23833478974972],"type":"data","visible":true},"hoverlabel":{"bgcolor":"#EB663B","font":{"family":"Helvetica","size":16}},"hovertemplate":"<b>Cluster 9<br>dockq: 0.108<br>score: -100.932</b><extra></extra>","legendgroup":"Cluster 9","marker":{"color":"#EB663B","size":10,"symbol":"square-dot"},"mode":"markers","showlegend":false,"text":["Cluster 9<br>dockq: 0.108<br>score: -100.932"],"x":[0.108],"y":[-100.93175000000001],"type":"scatter"},{"hoverlabel":{"bgcolor":"#511CFB","font":{"family":"Helvetica","size":16}},"legendgroup":"Cluster 10","marker":{"color":"#511CFB"},"mode":"markers","name":"Cluster 10","text":["Model: cluster_10_model_1.pdb<br>Score: -104.573<br>Caprieval rank: 39","Model: cluster_10_model_2.pdb<br>Score: -98.436<br>Caprieval rank: 65","Model: cluster_10_model_3.pdb<br>Score: -97.928<br>Caprieval rank: 69","Model: cluster_10_model_4.pdb<br>Score: -95.797<br>Caprieval rank: 82","Model: cluster_10_model_5.pdb<br>Score: -88.311<br>Caprieval rank: 134","Model: cluster_10_model_6.pdb<br>Score: -85.64<br>Caprieval rank: 147","Model: cluster_10_model_7.pdb<br>Score: -80.717<br>Caprieval rank: 184"],"x":[0.106,0.115,0.104,0.096,0.083,0.108,0.096],"y":[-104.573,-98.436,-97.928,-95.797,-88.311,-85.64,-80.717],"type":"scatter"},{"error_x":{"array":[0.006759252917297888],"type":"data","visible":true},"error_y":{"array":[3.2653533116647555],"type":"data","visible":true},"hoverlabel":{"bgcolor":"#511CFB","font":{"family":"Helvetica","size":16}},"hovertemplate":"<b>Cluster 10<br>dockq: 0.105<br>score: -99.184</b><extra></extra>","legendgroup":"Cluster 10","marker":{"color":"#511CFB","size":10,"symbol":"square-dot"},"mode":"markers","showlegend":false,"text":["Cluster 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cluster_12_model_10.pdb<br>Score: -80.182<br>Caprieval rank: 189","Model: cluster_12_model_11.pdb<br>Score: -79.353<br>Caprieval rank: 195","Model: cluster_12_model_12.pdb<br>Score: -75.997<br>Caprieval rank: 220","Model: cluster_12_model_13.pdb<br>Score: -74.666<br>Caprieval rank: 227","Model: cluster_12_model_14.pdb<br>Score: -73.689<br>Caprieval rank: 232","Model: cluster_12_model_15.pdb<br>Score: -72.673<br>Caprieval rank: 241","Model: cluster_12_model_16.pdb<br>Score: -67.604<br>Caprieval rank: 264","Model: cluster_12_model_17.pdb<br>Score: -66.097<br>Caprieval rank: 276","Model: cluster_12_model_18.pdb<br>Score: -63.674<br>Caprieval rank: 282","Model: cluster_12_model_19.pdb<br>Score: -63.397<br>Caprieval rank: 284","Model: cluster_12_model_20.pdb<br>Score: -56.403<br>Caprieval rank: 301","Model: cluster_12_model_21.pdb<br>Score: -55.159<br>Caprieval rank: 302","Model: cluster_24_model_1.pdb<br>Score: -77.077<br>Caprieval rank: 212","Model: cluster_24_model_2.pdb<br>Score: -66.705<br>Caprieval rank: 269","Model: cluster_24_model_3.pdb<br>Score: -65.064<br>Caprieval rank: 278","Model: cluster_24_model_4.pdb<br>Score: -62.533<br>Caprieval rank: 286","Model: cluster_24_model_5.pdb<br>Score: -60.727<br>Caprieval rank: 291","Model: cluster_24_model_6.pdb<br>Score: -59.979<br>Caprieval rank: 292","Model: cluster_24_model_7.pdb<br>Score: -58.73<br>Caprieval rank: 297","Model: cluster_24_model_8.pdb<br>Score: -58.724<br>Caprieval rank: 298","Model: cluster_24_model_9.pdb<br>Score: -58.508<br>Caprieval rank: 299","Model: cluster_24_model_10.pdb<br>Score: -57.175<br>Caprieval rank: 300","Model: cluster_24_model_11.pdb<br>Score: -54.571<br>Caprieval rank: 304","Model: cluster_24_model_12.pdb<br>Score: -54.399<br>Caprieval rank: 305","Model: cluster_24_model_13.pdb<br>Score: -51.266<br>Caprieval rank: 309","Model: cluster_24_model_14.pdb<br>Score: -48.009<br>Caprieval rank: 311","Model: cluster_24_model_15.pdb<br>Score: -46.219<br>Caprieval rank: 314","Model: 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index c8e5f32b9..2382496fb 100644
--- a/education/HADDOCK3/HADDOCK3-protein-protein-basic/plots/scenario2/irmsd_score.html
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174","Model: cluster_12_model_9.pdb<br>Score: -81.106<br>Caprieval rank: 180","Model: cluster_12_model_10.pdb<br>Score: -80.182<br>Caprieval rank: 189","Model: cluster_12_model_11.pdb<br>Score: -79.353<br>Caprieval rank: 195","Model: cluster_12_model_12.pdb<br>Score: -75.997<br>Caprieval rank: 220","Model: cluster_12_model_13.pdb<br>Score: -74.666<br>Caprieval rank: 227","Model: cluster_12_model_14.pdb<br>Score: -73.689<br>Caprieval rank: 232","Model: cluster_12_model_15.pdb<br>Score: -72.673<br>Caprieval rank: 241","Model: cluster_12_model_16.pdb<br>Score: -67.604<br>Caprieval rank: 264","Model: cluster_12_model_17.pdb<br>Score: -66.097<br>Caprieval rank: 276","Model: cluster_12_model_18.pdb<br>Score: -63.674<br>Caprieval rank: 282","Model: cluster_12_model_19.pdb<br>Score: -63.397<br>Caprieval rank: 284","Model: cluster_12_model_20.pdb<br>Score: -56.403<br>Caprieval rank: 301","Model: cluster_12_model_21.pdb<br>Score: -55.159<br>Caprieval rank: 302","Model: 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cluster_12_model_10.pdb<br>Score: -80.182<br>Caprieval rank: 189","Model: cluster_12_model_11.pdb<br>Score: -79.353<br>Caprieval rank: 195","Model: cluster_12_model_12.pdb<br>Score: -75.997<br>Caprieval rank: 220","Model: cluster_12_model_13.pdb<br>Score: -74.666<br>Caprieval rank: 227","Model: cluster_12_model_14.pdb<br>Score: -73.689<br>Caprieval rank: 232","Model: cluster_12_model_15.pdb<br>Score: -72.673<br>Caprieval rank: 241","Model: cluster_12_model_16.pdb<br>Score: -67.604<br>Caprieval rank: 264","Model: cluster_12_model_17.pdb<br>Score: -66.097<br>Caprieval rank: 276","Model: cluster_12_model_18.pdb<br>Score: -63.674<br>Caprieval rank: 282","Model: cluster_12_model_19.pdb<br>Score: -63.397<br>Caprieval rank: 284","Model: cluster_12_model_20.pdb<br>Score: -56.403<br>Caprieval rank: 301","Model: cluster_12_model_21.pdb<br>Score: -55.159<br>Caprieval rank: 302","Model: cluster_24_model_1.pdb<br>Score: -77.077<br>Caprieval rank: 212","Model: cluster_24_model_2.pdb<br>Score: 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cluster_24_model_1.pdb<br>Score: -77.077<br>Caprieval rank: 212","Model: cluster_24_model_2.pdb<br>Score: -66.705<br>Caprieval rank: 269","Model: cluster_24_model_3.pdb<br>Score: -65.064<br>Caprieval rank: 278","Model: cluster_24_model_4.pdb<br>Score: -62.533<br>Caprieval rank: 286","Model: cluster_24_model_5.pdb<br>Score: -60.727<br>Caprieval rank: 291","Model: cluster_24_model_6.pdb<br>Score: -59.979<br>Caprieval rank: 292","Model: cluster_24_model_7.pdb<br>Score: -58.73<br>Caprieval rank: 297","Model: cluster_24_model_8.pdb<br>Score: -58.724<br>Caprieval rank: 298","Model: cluster_24_model_9.pdb<br>Score: -58.508<br>Caprieval rank: 299","Model: cluster_24_model_10.pdb<br>Score: -57.175<br>Caprieval rank: 300","Model: cluster_24_model_11.pdb<br>Score: -54.571<br>Caprieval rank: 304","Model: cluster_24_model_12.pdb<br>Score: -54.399<br>Caprieval rank: 305","Model: cluster_24_model_13.pdb<br>Score: -51.266<br>Caprieval rank: 309","Model: cluster_24_model_14.pdb<br>Score: -48.009<br>Caprieval rank: 311","Model: cluster_24_model_15.pdb<br>Score: -46.219<br>Caprieval rank: 314","Model: cluster_24_model_16.pdb<br>Score: -41.991<br>Caprieval rank: 315","Model: cluster_24_model_17.pdb<br>Score: -41.146<br>Caprieval rank: 316","Model: cluster_24_model_18.pdb<br>Score: -30.591<br>Caprieval rank: 317","Model: cluster_14_model_1.pdb<br>Score: -94.95<br>Caprieval rank: 88","Model: cluster_14_model_2.pdb<br>Score: -91.574<br>Caprieval rank: 107","Model: cluster_14_model_3.pdb<br>Score: -91.488<br>Caprieval rank: 108","Model: cluster_14_model_4.pdb<br>Score: -89.213<br>Caprieval rank: 128","Model: cluster_14_model_5.pdb<br>Score: -88.42<br>Caprieval rank: 133","Model: cluster_14_model_6.pdb<br>Score: -85.999<br>Caprieval rank: 144","Model: cluster_14_model_7.pdb<br>Score: -84.262<br>Caprieval rank: 159","Model: cluster_14_model_8.pdb<br>Score: -80.023<br>Caprieval rank: 190","Model: cluster_14_model_9.pdb<br>Score: -78.893<br>Caprieval rank: 199","Model: cluster_14_model_10.pdb<br>Score: -78.359<br>Caprieval rank: 202","Model: cluster_14_model_11.pdb<br>Score: -77.604<br>Caprieval rank: 206","Model: cluster_14_model_12.pdb<br>Score: -77.155<br>Caprieval rank: 210","Model: cluster_14_model_13.pdb<br>Score: -76.755<br>Caprieval rank: 213","Model: cluster_14_model_14.pdb<br>Score: -74.029<br>Caprieval rank: 230","Model: cluster_14_model_15.pdb<br>Score: -73.345<br>Caprieval rank: 234","Model: cluster_14_model_16.pdb<br>Score: -72.568<br>Caprieval rank: 244","Model: cluster_14_model_17.pdb<br>Score: -68.563<br>Caprieval rank: 259","Model: cluster_14_model_18.pdb<br>Score: -63.611<br>Caprieval rank: 283","Model: cluster_23_model_1.pdb<br>Score: -77.675<br>Caprieval rank: 205","Model: cluster_23_model_2.pdb<br>Score: -71.182<br>Caprieval rank: 252","Model: cluster_23_model_3.pdb<br>Score: -68.734<br>Caprieval rank: 257","Model: cluster_23_model_4.pdb<br>Score: -66.454<br>Caprieval rank: 272","Model: cluster_23_model_5.pdb<br>Score: -65.464<br>Caprieval rank: 277","Model: cluster_23_model_6.pdb<br>Score: -62.779<br>Caprieval rank: 285","Model: cluster_23_model_7.pdb<br>Score: -61.798<br>Caprieval rank: 290","Model: cluster_23_model_8.pdb<br>Score: -54.247<br>Caprieval rank: 306","Model: cluster_23_model_9.pdb<br>Score: -51.304<br>Caprieval rank: 308","Model: cluster_23_model_10.pdb<br>Score: -50.01<br>Caprieval rank: 310","Model: cluster_23_model_11.pdb<br>Score: -46.698<br>Caprieval rank: 313","Model: cluster_18_model_1.pdb<br>Score: -92.201<br>Caprieval rank: 104","Model: cluster_18_model_2.pdb<br>Score: -85.73<br>Caprieval rank: 146","Model: cluster_18_model_3.pdb<br>Score: -78.566<br>Caprieval rank: 201","Model: cluster_18_model_4.pdb<br>Score: -76.707<br>Caprieval rank: 215","Model: cluster_18_model_5.pdb<br>Score: -68.327<br>Caprieval rank: 260","Model: cluster_18_model_6.pdb<br>Score: -64.011<br>Caprieval rank: 280","Model: cluster_18_model_7.pdb<br>Score: -59.735<br>Caprieval rank: 293","Model: 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From 1439c79186a2f9056422f05847c3686b2a29a0a5 Mon Sep 17 00:00:00 2001
From: amjjbonvin <alexandre.bonvin@gmail.com>
Date: Tue, 1 Oct 2024 15:18:00 +0200
Subject: [PATCH 10/15] Updated publications

---
 publications/index.md | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/publications/index.md b/publications/index.md
index a1813eb1b..d385d7b3c 100644
--- a/publications/index.md
+++ b/publications/index.md
@@ -5,22 +5,26 @@ image:
   feature: pages/banner_publications-mini.jpg
 ---
 ## 2024
+* V. Reys∗, Ma. Giulini∗, V. Cojocaru, A. Engel, X. Xu, J. Roel-Touris, C. Geng, F. Ambrosetti, B.
+Jimenez-Garcia, Z. Jandova, P.I. Koukos, C. van Noort, J.M. . Teixeira, S.C. van Keulen, M. Reau, R.V. Honorato and **A.M.J.J. Bonvin**. [Integrative modeling in the age of machine learning: a summary of HADDOCK strategies in CAPRI rounds 47-55](https://doi.org/10.1101/2024.09.16.613212) _BioRXiv._ (2024).
 
 * K. Devantier, T.L. Toft-Bertelsen, A. Prestel, V.M.S. Kjaer, C.a Sahin, M. Giulini, S. Louka, K. Spiess, A. Manandhar, K. Qvortrup, T. Ulven, B. Hjorth Bentzen, **A.M.J.J. Bonvin**, N. MacAulay, B.B. Kragelund and M.M. Rosenkilde. [The SH Protein of Mumps Virus is a Druggable Pentameric Viroporin](https://doi.org/10.1101/2024.08.09.60700). _BioRXiv._ 10.1101/2024.08.09.60700 (2024).
 
-* A. Ranaudo, M. Giulini, A. Pelissou Ayuso and **A.M.J.J. Bonvin**. [Modelling Protein-Glycan Interactions with HADDOCK](https://doi.org/10.1101/2024.07.31.605986). _BioRXiv._ 10.1101/2024.07.31.605986 (2024).
-
 * X. Xu and **A.M.J.J. Bonvin** [Ranking protein-protein models with large language models and graph neural networks](https://arxiv.org/abs/2407.16375). _arXiv_:2407.16375 (2024).
 
 * G. Bellinzona, D. Sassera and **A.M.J.J. Bonvin**. [Accelerating Protein-Protein Interaction screens with reduced AlphaFold-Multimer sampling](https://www.biorxiv.org/content/10.1101/2024.06.07.597882v2) _BioRXiv_ 10.1101/2024.06.07.597882 (2024).
 
 * A. Basciu, M. Athar, H. Kurt, C. Neville, G. Malloci, F. Muredda, A. Bosin, P. Ruggerone, **A.M.J.J. Bonvin** and A.V. Vargiu. [Predicting binding events in very flexible, allosteric, multi-domain proteins](https://doi.org/10.1101/2024.06.02.597018). _BioRXiv._ 10.1101/2024.06.02.597018 (2024).
 
+* A. Ranaudo, M. Giulini, A. Pelissou Ayuso and **A.M.J.J. Bonvin**. [Modelling Protein-Glycan Interactions with HADDOCK](https://doi.org/10.1101/2024.07.31.605986). _J. Chem. Inf. Mod._ In press (2024). [BioRXiv](https://doi.org/10.1101/2024.07.31.605986)
+
+* M. Giulini, C. Schneider, D. Cutting, N. Desai, C. Deane and **A.M.J.J. Bonvin**. [Towards the accurate modelling of antibody-antigen complexes from sequence using machine learning and information-driven docking](https://doi.org/10.1093/bioinformatics/btae583). _Bioinformatics_ Advanced Online Publication, 10.1093/bioinformatics/btae583 (2024).[BioRxiv](https://www.biorxiv.org/content/10.1101/2023.11.17.567543v1)
+
 * R.V. Honorato, M.E. Trellet, B. Jiménez-García1, J.J. Schaarschmidt, M. Giulini, V. Reys,  P.I. Koukos, J.P.G.L.M. Rodrigues, E. Karaca, G.C.P. van Zundert, J. Roel-Touris, C.W. van Noort, Z. Jandová, A.S.J. Melquiond and **A.M.J.J. Bonvin**. [The HADDOCK2.4 web server: A leap forward in integrative modelling of biomolecular complexes](https://www.nature.com/articles/s41596-024-01011-0.epdf?sharing_token=UHDrW9bNh3BqijxD2u9Xd9RgN0jAjWel9jnR3ZoTv0O8Cyf_B_3QikVaNIBRHxp9xyFsQ7dSV3t-kBtpCaFZWPfnuUnAtvRG_vkef9o4oWuhrOLGbBXJVlaaA9ALOULn6NjxbiqC2VkmpD2ZR_r-o0sgRZoHVz10JqIYOeus_nM%3D). _Nature Prot._, Advanced Online Publication DOI: 10.1038/s41596-024-01011-0 (2024).
 
-* B. Vallat, B.M. Webb, J.D. Westbrook, T. Goddard, C.A. Hanke, A. Graziadei, E. Peisach, A. Zalevsky, J. Sagendorf, H. Tangmunarunkit, S. Voinea, M. Sekharan, J. Yu, **A.M.J.J. Bonvin**, Fr, DiMaio, G. Hummer, J. Meiler, E. Tajkhorshid, T. Ferrin, C.L. Lawson, A. Leitner, J. Rappsilber, C.A.M. Seidel, C.M. Jeffries, S.K. Burley, J. Hoch, G.i Kurisu, K.e Morris, A. Patwardhan, S. Velankar, T. Schwede, J. Trewhella, C. Kesselman, H.M. Berman, A. Sali. [IHMCIF: An extension of PDBx/mmCIF data standard for integrative structure determination methods](https://doi.org/10.1016/j.jmb.2024.168546). _J. Mol. Biol._, Advanced Online Publication (2024).
+* B. Vallat, B.M. Webb, J.D. Westbrook, T. Goddard, C.A. Hanke, A. Graziadei, E. Peisach, A. Zalevsky, J. Sagendorf, H. Tangmunarunkit, S. Voinea, M. Sekharan, J. Yu, **A.M.J.J. Bonvin**, Fr, DiMaio, G. Hummer, J. Meiler, E. Tajkhorshid, T. Ferrin, C.L. Lawson, A. Leitner, J. Rappsilber, C.A.M. Seidel, C.M. Jeffries, S.K. Burley, J. Hoch, G.i Kurisu, K.e Morris, A. Patwardhan, S. Velankar, T. Schwede, J. Trewhella, C. Kesselman, H.M. Berman, A. Sali. [IHMCIF: An extension of PDBx/mmCIF data standard for integrative structure determination methods](https://doi.org/10.1016/j.jmb.2024.168546). _J. Mol. Biol._, *436*:168546 (2024).
 
-* K.W. Collins, M.M. Copeland,, G. Brysbaert, S.J. Wodak, **A.M.J.J. Bonvin**, P,J, Kundrotas, I.A. Vakser and M.F. Lensink. [CAPRI-Q: The CAPRI resource evaluating the quality of predicted structures of protein complexes](https://doi.org/10.1016/j.jmb.2024.168540). _J. Mol. Biol._, Advanced Online Publication (2024).
+* K.W. Collins, M.M. Copeland,, G. Brysbaert, S.J. Wodak, **A.M.J.J. Bonvin**, P,J, Kundrotas, I.A. Vakser and M.F. Lensink. [CAPRI-Q: The CAPRI resource evaluating the quality of predicted structures of protein complexes](https://doi.org/10.1016/j.jmb.2024.168540). _J. Mol. Biol._, *436*:168540 (2024).
 
 * M. Giulini, R.V. Honorato, J.L. Rivera, **A.M.J.J. Bonvin**. [ARCTIC-3D: Automatic Retrieval and ClusTering of Interfaces in Complexes from 3D structural information](https://doi.org/10.1038/s42003-023-05718-w). _Comm. Biol._  *7*:49, p. 1-9  (2024).
 
@@ -29,8 +33,6 @@ image:
 
 ## 2023
 
-* M. Giulini, C. Schneider, D. Cutting, N. Desai, C. Deane and **A.M.J.J. Bonvin**. [Towards the accurate modelling of antibody-antigen complexes from sequence using machine learning and information-driven docking](https://www.biorxiv.org/content/10.1101/2023.11.17.567543v1). _BioRXiv._ 10.1101/2023.11.17.567543v1 (2023).
-
 * **A.M.J.J. Bonvin**. [Empowering Global Collaboration in Structural Biology and Life Sciences](https://zenodo.org/record/8135315). DOI:10.5281/zenodo.8135315 (2023).
 
 * M. Lensink, G. Brysbaert, N. Raouraoua, P. Bates, M. Giulini, R. Vargas Honorato, C. van Noort, J. Teixeira, **A.M.J.J. Bonvin**, R. Kong, H. Shi, X. Lu, S. Chang, J. Liu, Z. Guo, X. Chen, A. Morehead, R. Roy, T. Wu, N. Giri, F. Quadir, C. Chen, J. Cheng, C. Del Carpio, E. Ichiishi, L. Rodriguez-Lumbreras, J. Fernández-Recio, A. Harmalkar, L. Chu, S.Canner, R. Smanta, J. Gray, H. Li, P. Lin, J.a He, H. Tao, S. Huang, J. Roel, B. Jimenez-Garcia, C. Christoffer, A. Jain J, Y. Kagaya, H. Kannan, T. Nakamura, G. Terashi, J. Verburgt, Y. Zhang, Z. Zhang, H. Fujuta, M. Sekijima, D. Kihara, O. Khan, S. Kotelnikov, U. Ghani, D. Padhorny, D. Beglov, S. Vajda, D. Kozakov, S. Negi S, T. Ricciardelli, D. Barradas-Bautista, Z. Cao, M. Chawla, L. Cavallo, R. Oliva, R. Yin, M. Cheung, J. Guest, J. Lee, B. Pierce, B. Shor, T. Cohen, M. Halfon, D. Schneidman-Duhovny, S. Zhu, R. Yin, Y. Sun, Y. Shen, M. Maszota-Zieleniak, K. Bojarski K, E. Lubecka, M. Marcisz, A. Danielsson, L. Dziadek, M. Gaardlos, A. Giełdoń, J. Liwo, S. Samsonov, R. Slusarz, K. Zieba, A. Sieradzan, C. Czaplewski , S. Kobayashi, Y. Miyakawa, Y. Kiyota, M. Takeda-Shitaka, K. Olechnovič, L. Valančauskas, J. Dapkūnas, C. Venclovas, B. Wallner, L. Yang, C. Hou, X. He, S. Guo, S. Jiang, X. Ma, R. Duan, L. Qiu, X. Xu, X. Zou, S. Velankar, S. Wodak. [Impact of AlphaFold on Structure Prediction of Protein Complexes: The CASP15-CAPRI Experiment](https://doi.org/10.1002/prot.26609) _Proteins: Struc. Funct. &amp; Bioinformatics_ *12*, 1658-1683 (2023).

From 00274de9727f6bae54d53d3ce9af29d05237af09 Mon Sep 17 00:00:00 2001
From: Raphaelle Versini <rapha.versini@gmail.com>
Date: Wed, 9 Apr 2025 14:21:12 +0200
Subject: [PATCH 11/15] haddock3-restraints added

---
 .../HADDOCK3/HADDOCK3-protein-protein-basic/index.md  | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md b/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md
index f91c7657c..47586db4e 100644
--- a/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md
+++ b/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md
@@ -410,12 +410,11 @@ binding site in the final models, while the residues defined as `active` (typica
 site residues) will. When using the HADDOCK server, `passive` residues will be automatically defined. Here since we are
 using a local version, we need to define those manually and create a file in which the active and passive residues will be listed.
 
-This can easily be done using a script from our [haddock-tools][haddock-tools] repository, which is also provided for convenience
-in the `scripts` directly of the archive you downloaded for this tutorial:
+This can easily be done using a haddock3 command line tool in the following way:
 
 <a class="prompt prompt-cmd">
 echo "38 40 45 46 69 71 78 80 94 96 141" > e2a.act-pass
-python ./scripts/passive_from_active.py e2a_1F3G.pdb 38,40,45,46,69,71,78,80,94,96,141 >> e2a.act-pass
+haddock3-restraints passive_from_active e2a_1F3G.pdb 38,40,45,46,69,71,78,80,94,96,141 >> e2a.act-pass
 </a>
 
 The NMR-identified residues and their surface neighbors generated with the above command can be used to define ambiguous interactions restraints, either using the NMR identified residues as active in HADDOCK, or combining those with the surface neighbors and use this combination as passive only. Here we decided to treat the NMR-identified residues as active residues. 
@@ -439,11 +438,11 @@ As stated before, the following residues were identified has having significant
 
 <a class="prompt prompt-info">15,16,17,20,48,49,51,52,54,56</a>
 
-Using the same script provided in the archive of this tutorial: 
+Using the same haddock3 command line tool: 
 
 <a class="prompt prompt-cmd">
 echo "15 16 17 20 48 49 51 52 54 56" > hpr.act-pass
-python ./scripts/passive_from_active.py hpr-ensemble.pdb 15,16,17,20,48,49,51,52,54,56 >> hpr.act-pass
+haddock3-restraints passive_from_active hpr-ensemble.pdb 15,16,17,20,48,49,51,52,54,56 >> hpr.act-pass
 </a>
 
 <hr>
@@ -477,7 +476,7 @@ Using those two files, we can generate the CNS-formatted AIR restraint files
 with the following command:
 
 <a class="prompt prompt-cmd">
-python ./scripts/active-passive-to-ambig.py e2a.act-pass hpr.act-pass > e2a-hpr_air.tbl
+haddock3-restraints active_passive_to_ambig e2a.act-pass hpr.act-pass \-\-segid-one A \-\-segid-two B > e2a-hpr_air.tbl
 </a>
 
 This generates a file called `ambig-prot-prot.tbl` that contains the AIR

From d40f1ae3f561e751ec71170b00237a6e7d9acacd Mon Sep 17 00:00:00 2001
From: Raphaelle Versini <rapha.versini@gmail.com>
Date: Wed, 9 Apr 2025 15:00:30 +0200
Subject: [PATCH 12/15] missing modules added

---
 .../HADDOCK3/HADDOCK3-protein-protein-basic/index.md   | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md b/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md
index 47586db4e..7208590b0 100644
--- a/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md
+++ b/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md
@@ -135,20 +135,30 @@ all categories and modules. Below is a summary of the available modules:
 * **Sampling modules**
     * `rigidbody`: *Rigid body energy minimization with CNS (`it0` in haddock2.x).*
     * `lightdock`: *Third-party glow-worm swam optimization docking software.*
+    * 'gdock': *Gdock integration sampling modulex.*
 * **Model refinement modules**
     * `flexref`: *Semi-flexible refinement using a simulated annealing protocol through molecular dynamics simulations in torsion angle space (`it1` in haddock2.x).*
     * `emref`: *Refinement by energy minimisation (`itw` EM only in haddock2.4).*
     * `mdref`: *Refinement by a short molecular dynamics simulation in explicit solvent (`itw` in haddock2.X).*
+    * `openmm`: *Molecular Dynamics refinement module.*
 * **Scoring modules**
     * `emscoring`: *scoring of a complex performing a short EM (builds the topology and all missing atoms).*
     * `mdscoring`: *scoring of a complex performing a short MD in explicit solvent + EM (builds the topology and all missing atoms).*
+    * `prodigyligand`: *performs the scoring of input complexes using PRODIGY-ligand. It predicts deltaG of the complex and can return predictions as either deltaG or pKd values.*
+    * `prodigyprotein`: *performs the scoring of input complexes using PRODIGY (protein). It predicts deltaG of the complex and can return predictions as either deltaG or pKd values.*
+    * `sasascore`: *solvent accessibility analysis based on some user-defined residues that should be buried or accessible.*
 * **Analysis modules**
     * `caprieval`: *Calculates CAPRI metrics (i-RMSD, l-RMSD, Fnat, DockQ) with respect to the top scoring model or reference structure if provided.*
     * `clustfcc`: *Clusters models based on the fraction of common contacts (FCC)*
     * `clustrmsd`: *Clusters models based on pairwise RMSD matrix calculated with the `rmsdmatrix` module.*
     * `rmsdmatrix`: *Calculates the pairwise RMSD matrix between all the models generated in the previous step.*
+    * `ilrmsdmatrix`: *calculates of the interface-ligand RMSD (ilRMSD) matrix between all the models generated in the previous step.*
     * `seletop`: *Selects the top N models from the previous step.*
     * `seletopclusts`: *Selects top N clusters from the previous step.*
+    * `alascan`: *For each model, the module will mutate the interface residues and calculate the energy differences between the wild type and the mutant, thus providing a measure of the impact of such mutation.*
+    * `contactmap`: *aims at generating heatmaps and chordcharts of the contacts observed in the input complexes.*
+* **Extra modules**
+    * `exit`: *Stop the workflow when this module is reached.*
 
 The HADDOCK3 workflows are defined in simple configuration text files, similar to the TOML format but with extra features.
 Contrarily to HADDOCK2.X which follows a rigid (yet highly parameterisable)

From 893c316765f77b8cbef95775d98ac095b38f8216 Mon Sep 17 00:00:00 2001
From: Raphaelle Versini <rapha.versini@gmail.com>
Date: Thu, 10 Apr 2025 10:39:50 +0200
Subject: [PATCH 13/15] batch mode modified to local

---
 .../HADDOCK3/HADDOCK3-protein-protein-basic/index.md   | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md b/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md
index 7208590b0..99f2db75f 100644
--- a/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md
+++ b/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md
@@ -680,7 +680,7 @@ In this execution mode the HADDOCK3 job should be submitted to the batch system
 
 Now that we have all data ready, and know about execution modes of HADDOCK3 it is time to setup the docking for the first scenario. The restraint file to use for this is `e2a-hpr_air.tbl`. We proceed to produce 1000 rigidbody docking models, from which 200 will be selected and refined through flexible refinment and energy minimization. 
 For the analysis following the docking results, we are using the solved complex [1GGR](https://www.rcsb.org/structure/1GGR), named e2a-hpr_1GGR.pdb.
-The configuration file for this scenario (assuming a batch running mode) is:
+The configuration file for this scenario is:
 
 {% highlight toml %}
 # ====================================================================
@@ -690,7 +690,8 @@ The configuration file for this scenario (assuming a batch running mode) is:
 run_dir = "scenario1-full"
 
 # execution mode
-mode = "batch"
+mode = "local"
+ncores = 50
 #  it will take the system's default
 # queue = "short"
 # concatenate models inside each job, concat = 5 each .job will produce 5 models
@@ -769,7 +770,7 @@ submitting it to the batch system requesting in this local run mode a full node
 
 In scenario 2, we proceed to produce 1000 rigidbody docking models, from which we proceed to do a first clustering analysis. From the top clusters a flexible refinment then energy minization is done. 
 For the analysis following the docking results, we are using the solved complex [1GGR](https://www.rcsb.org/structure/1GGR), named e2a-hpr_1GGR.pdb.
-The configuration file for this scenario (assuming a batch running mode) is:
+The configuration file for this scenario is:
 
 {% highlight toml %}
 # ====================================================================
@@ -780,7 +781,8 @@ The configuration file for this scenario (assuming a batch running mode) is:
 run_dir = "scenario2-cltsel-full"
 
 # execution mode
-mode = "batch"
+mode = "local"
+ncores = 50
 # concatenate models inside each job, concat = 5 each .job will produce 5 models
 concat = 5
 #  Limit the number of concurrent submissions to the queue

From 956ed80344b93d51a225ffb2003794dab5519076 Mon Sep 17 00:00:00 2001
From: Raphaelle Versini <rapha.versini@gmail.com>
Date: Thu, 8 May 2025 13:43:28 +0200
Subject: [PATCH 14/15] conflict with master branch corrected

---
 publications/index.md | 25 ++++++++++++++++---------
 1 file changed, 16 insertions(+), 9 deletions(-)

diff --git a/publications/index.md b/publications/index.md
index d385d7b3c..2076cc48c 100644
--- a/publications/index.md
+++ b/publications/index.md
@@ -4,23 +4,31 @@ title: "Publications"
 image:
   feature: pages/banner_publications-mini.jpg
 ---
+## 2025
+
+* M. Giulini#, V. Reys#, J.M.C. Teixeira, B. Jiménez-García, R.V. Honorato, A. Kravchenko, X. Xu, R. Versini, A. Engel, S. Verhoevenand **A.M.J.J. Bonvin**. [HADDOCK3: A modular and versatile platform for integrative modelling of biomolecular complexes](https://doi.org/10.1101/2025.04.30.651432). _BioRXiv._ 10.1101/2025.04.30.651432 (2025).
+
+* M. Giulini#, X. Xu# and **A.M.J.J. Bonvin**. [Improved structural modelling of antibodies and their complexes with clustered diffusion ensembles](https://doi.org/10.1101/2025.02.24.639865). _BioRXiv._ 10.1101/2025.02.24.639865 (2025).
+
+* A. Basciu, M. Athar, H. Kurt, C. Neville, G. Malloci, F. Muredda, A. Bosin, P. Ruggerone, **A.M.J.J. Bonvin** and A.V. Vargiu. [Predicting binding events in very flexible, allosteric, multi-domain proteins](https://pubs.acs.org/doi/10.1021/acs.jcim.4c01810). _J. Chem. Inf. Mod._ Advanced Online Publication (2025).
+
+
 ## 2024
+
 * V. Reys∗, Ma. Giulini∗, V. Cojocaru, A. Engel, X. Xu, J. Roel-Touris, C. Geng, F. Ambrosetti, B.
-Jimenez-Garcia, Z. Jandova, P.I. Koukos, C. van Noort, J.M. . Teixeira, S.C. van Keulen, M. Reau, R.V. Honorato and **A.M.J.J. Bonvin**. [Integrative modeling in the age of machine learning: a summary of HADDOCK strategies in CAPRI rounds 47-55](https://doi.org/10.1101/2024.09.16.613212) _BioRXiv._ (2024).
+Jimenez-Garcia, Z. Jandova, P.I. Koukos, C. van Noort, J.M. . Teixeira, S.C. van Keulen, M. Reau, R.V. Honorato and **A.M.J.J. Bonvin**. [Integrative modeling in the age of machine learning: a summary of HADDOCK strategies in CAPRI rounds 47-55](http://doi.org/10.1002/prot.26789). _Proteins: Struc. Funct. &amp; Bioinformatics_  Advanced Online Publication (2024).
 
 * K. Devantier, T.L. Toft-Bertelsen, A. Prestel, V.M.S. Kjaer, C.a Sahin, M. Giulini, S. Louka, K. Spiess, A. Manandhar, K. Qvortrup, T. Ulven, B. Hjorth Bentzen, **A.M.J.J. Bonvin**, N. MacAulay, B.B. Kragelund and M.M. Rosenkilde. [The SH Protein of Mumps Virus is a Druggable Pentameric Viroporin](https://doi.org/10.1101/2024.08.09.60700). _BioRXiv._ 10.1101/2024.08.09.60700 (2024).
 
 * X. Xu and **A.M.J.J. Bonvin** [Ranking protein-protein models with large language models and graph neural networks](https://arxiv.org/abs/2407.16375). _arXiv_:2407.16375 (2024).
 
-* G. Bellinzona, D. Sassera and **A.M.J.J. Bonvin**. [Accelerating Protein-Protein Interaction screens with reduced AlphaFold-Multimer sampling](https://www.biorxiv.org/content/10.1101/2024.06.07.597882v2) _BioRXiv_ 10.1101/2024.06.07.597882 (2024).
+* G. Bellinzona, D. Sassera and **A.M.J.J. Bonvin**. [Accelerating Protein-Protein Interaction screens with reduced AlphaFold-Multimer sampling](https://doi.org/10.1093/bioadv/vbae153) _Bioinformatics Advances_ *4*:vbae153 (2024).
 
-* A. Basciu, M. Athar, H. Kurt, C. Neville, G. Malloci, F. Muredda, A. Bosin, P. Ruggerone, **A.M.J.J. Bonvin** and A.V. Vargiu. [Predicting binding events in very flexible, allosteric, multi-domain proteins](https://doi.org/10.1101/2024.06.02.597018). _BioRXiv._ 10.1101/2024.06.02.597018 (2024).
+* A. Ranaudo, M. Giulini, A. Pelissou Ayuso and **A.M.J.J. Bonvin**. [Modelling Protein-Glycan Interactions with HADDOCK](https://doi.org/10.1021/acs.jcim.4c01372). _J. Chem. Inf. Mod._ *64*, 7816–7825 (2024).
 
-* A. Ranaudo, M. Giulini, A. Pelissou Ayuso and **A.M.J.J. Bonvin**. [Modelling Protein-Glycan Interactions with HADDOCK](https://doi.org/10.1101/2024.07.31.605986). _J. Chem. Inf. Mod._ In press (2024). [BioRXiv](https://doi.org/10.1101/2024.07.31.605986)
+* M. Giulini, C. Schneider, D. Cutting, N. Desai, C. Deane and **A.M.J.J. Bonvin**. [Towards the accurate modelling of antibody-antigen complexes from sequence using machine learning and information-driven docking](https://doi.org/10.1093/bioinformatics/btae583). _Bioinformatics_ *40*:btae583, p. 1-11 (2024).[BioRxiv](https://www.biorxiv.org/content/10.1101/2023.11.17.567543v1)
 
-* M. Giulini, C. Schneider, D. Cutting, N. Desai, C. Deane and **A.M.J.J. Bonvin**. [Towards the accurate modelling of antibody-antigen complexes from sequence using machine learning and information-driven docking](https://doi.org/10.1093/bioinformatics/btae583). _Bioinformatics_ Advanced Online Publication, 10.1093/bioinformatics/btae583 (2024).[BioRxiv](https://www.biorxiv.org/content/10.1101/2023.11.17.567543v1)
-
-* R.V. Honorato, M.E. Trellet, B. Jiménez-García1, J.J. Schaarschmidt, M. Giulini, V. Reys,  P.I. Koukos, J.P.G.L.M. Rodrigues, E. Karaca, G.C.P. van Zundert, J. Roel-Touris, C.W. van Noort, Z. Jandová, A.S.J. Melquiond and **A.M.J.J. Bonvin**. [The HADDOCK2.4 web server: A leap forward in integrative modelling of biomolecular complexes](https://www.nature.com/articles/s41596-024-01011-0.epdf?sharing_token=UHDrW9bNh3BqijxD2u9Xd9RgN0jAjWel9jnR3ZoTv0O8Cyf_B_3QikVaNIBRHxp9xyFsQ7dSV3t-kBtpCaFZWPfnuUnAtvRG_vkef9o4oWuhrOLGbBXJVlaaA9ALOULn6NjxbiqC2VkmpD2ZR_r-o0sgRZoHVz10JqIYOeus_nM%3D). _Nature Prot._, Advanced Online Publication DOI: 10.1038/s41596-024-01011-0 (2024).
+* R.V. Honorato, M.E. Trellet, B. Jiménez-García1, J.J. Schaarschmidt, M. Giulini, V. Reys,  P.I. Koukos, J.P.G.L.M. Rodrigues, E. Karaca, G.C.P. van Zundert, J. Roel-Touris, C.W. van Noort, Z. Jandová, A.S.J. Melquiond and **A.M.J.J. Bonvin**. [The HADDOCK2.4 web server: A leap forward in integrative modelling of biomolecular complexes](https://www.nature.com/articles/s41596-024-01011-0.epdf?sharing_token=UHDrW9bNh3BqijxD2u9Xd9RgN0jAjWel9jnR3ZoTv0O8Cyf_B_3QikVaNIBRHxp9xyFsQ7dSV3t-kBtpCaFZWPfnuUnAtvRG_vkef9o4oWuhrOLGbBXJVlaaA9ALOULn6NjxbiqC2VkmpD2ZR_r-o0sgRZoHVz10JqIYOeus_nM%3D). _Nature Prot._, *19*, 3219–3241 (2024).
 
 * B. Vallat, B.M. Webb, J.D. Westbrook, T. Goddard, C.A. Hanke, A. Graziadei, E. Peisach, A. Zalevsky, J. Sagendorf, H. Tangmunarunkit, S. Voinea, M. Sekharan, J. Yu, **A.M.J.J. Bonvin**, Fr, DiMaio, G. Hummer, J. Meiler, E. Tajkhorshid, T. Ferrin, C.L. Lawson, A. Leitner, J. Rappsilber, C.A.M. Seidel, C.M. Jeffries, S.K. Burley, J. Hoch, G.i Kurisu, K.e Morris, A. Patwardhan, S. Velankar, T. Schwede, J. Trewhella, C. Kesselman, H.M. Berman, A. Sali. [IHMCIF: An extension of PDBx/mmCIF data standard for integrative structure determination methods](https://doi.org/10.1016/j.jmb.2024.168546). _J. Mol. Biol._, *436*:168546 (2024).
 
@@ -28,7 +36,7 @@ Jimenez-Garcia, Z. Jandova, P.I. Koukos, C. van Noort, J.M. . Teixeira, S.C. van
 
 * M. Giulini, R.V. Honorato, J.L. Rivera, **A.M.J.J. Bonvin**. [ARCTIC-3D: Automatic Retrieval and ClusTering of Interfaces in Complexes from 3D structural information](https://doi.org/10.1038/s42003-023-05718-w). _Comm. Biol._  *7*:49, p. 1-9  (2024).
 
-* X. Xu, **A.M.J.J. Bonvin**. [DeepRank-GNN-esm: A Graph Neural Network for Scoring Protein-Protein Models using Protein Language Model](https://doi.org/10.1093/bioadv/vbad191). _Bioinfo. Adv._  vbad191, _Advanced Online Publication  (2024).
+* X. Xu, **A.M.J.J. Bonvin**. [DeepRank-GNN-esm: A Graph Neural Network for Scoring Protein-Protein Models using Protein Language Model](https://doi.org/10.1093/bioadv/vbad191). _Bioinfo. Adv._  *4*:vbad191  (2024).
 
 
 ## 2023
@@ -1196,4 +1204,3 @@ _J. Magn. Reson._ *91*, 659-664 (1991).
 * I. Burghardt , L. Di Bari, **A. Bonvin** and G. Bodenhausen
 [Effect of strong coupling in multiple-quantum-filtered two-dimensional NOE spectroscopy.](https://doi.org/doi:10.1016/0022-2364(90)90044-A)
 _J. Magn. Reson._ *86*, 652-656 (1990).
-

From 11f890ce71e6c50229f1e717b9b9c6a2f68ac918 Mon Sep 17 00:00:00 2001
From: amjjbonvin <alexandre.bonvin@gmail.com>
Date: Fri, 16 May 2025 13:38:22 +0200
Subject: [PATCH 15/15] Update (avoiding image file duplication)

---
 .../e2a-surface-airs.png                      | Bin 28052 -> 0 bytes
 .../hpr-ensemble.png                          | Bin 25189 -> 0 bytes
 .../HADDOCK3-protein-protein-basic/index.md   |  51 ++++++------------
 .../phosphorylated-histidine.png              | Bin 5414 -> 0 bytes
 4 files changed, 16 insertions(+), 35 deletions(-)
 delete mode 100644 education/HADDOCK3/HADDOCK3-protein-protein-basic/e2a-surface-airs.png
 delete mode 100644 education/HADDOCK3/HADDOCK3-protein-protein-basic/hpr-ensemble.png
 delete mode 100644 education/HADDOCK3/HADDOCK3-protein-protein-basic/phosphorylated-histidine.png

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diff --git a/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md b/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md
index 99f2db75f..717a02ba6 100644
--- a/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md
+++ b/education/HADDOCK3/HADDOCK3-protein-protein-basic/index.md
@@ -18,10 +18,13 @@ This tutorial consists of the following sections:
 
 This tutorial demonstrates the use of the new modular HADDOCK3 version for predicting the structure of a protein-protein complex from NMR chemical shift perturbation (CSP) data. 
 Namely, we will dock two E. coli proteins involved in glucose transport: the glucose-specific enzyme IIA (E2A) and the histidine-containing phosphocarrier protein (HPr). 
-The structures in the free form have been determined using X-ray crystallography (E2A) (PDB ID [1F3G](https://www.ebi.ac.uk/pdbe/entry/pdb/1f3g){:target="_blank"}) and NMR spectroscopy (HPr) (PDB ID [1HDN](https://www.ebi.ac.uk/pdbe/entry/pdb/1hdn){:target="_blank"}). 
+The structures in the free form have been determined using X-ray crystallography (E2A) (PDB ID [1F3G](https://www.ebi.ac.uk/pdbe/entry/pdb/1f3g){:target="_blank"}) 
+and NMR spectroscopy (HPr) (PDB ID [1HDN](https://www.ebi.ac.uk/pdbe/entry/pdb/1hdn){:target="_blank"}). 
 The structure of the native complex has also been determined with NMR (PDB ID [1GGR](https://www.ebi.ac.uk/pdbe/entry/pdb/1ggr){:target="_blank"}). 
-These NMR experiments have also provided us with an array of data on the interaction itself (chemical shift perturbations, intermolecular NOEs, residual dipolar couplings, and simulated diffusion anisotropy data), which will be useful for the docking. 
-For this tutorial, we will only make use of inteface residues identified from NMR chemical shift perturbation data as described in [Wang *et al*, EMBO J (2000)](https://onlinelibrary.wiley.com/doi/10.1093/emboj/19.21.5635/abstract){:target="_blank"}.
+These NMR experiments have also provided us with an array of data on the interaction itself 
+(chemical shift perturbations, intermolecular NOEs, residual dipolar couplings, and simulated diffusion anisotropy data), which will be useful for the docking. 
+For this tutorial, we will only make use of inteface residues identified from NMR chemical shift perturbation data as described 
+in [Wang *et al*, EMBO J (2000)](https://onlinelibrary.wiley.com/doi/10.1093/emboj/19.21.5635/abstract){:target="_blank"}.
 
 Throughout the tutorial, colored text will be used to refer to questions or instructions, and/or PyMOL commands.
 
@@ -171,16 +174,6 @@ combining a multitude of independent modules that perform specialized tasks.
 
 ## Software requirements
 
-### Installing CNS
-The other required piece of software to run HADDOCK is its computational engine,
-CNS (Crystallography and NMR System –
-[https://cns-online.org](https://cns-online.org){:target="_blank"}). CNS is
-freely available for non-profit organizations. In order to get access to all
-features of HADDOCK you will need to compile CNS using the additional files
-provided in the HADDOCK distribution in the `varia/cns1.3` directory. Compilation of
-CNS might be non-trivial. Some guidance on installing CNS is provided in the online
-HADDOCK3 documentation page [here](https://www.bonvinlab.org/haddock3/CNS.html){:target="_blank"}.
-
 
 ### Installing HADDOCK3
 
@@ -252,7 +245,7 @@ As a preparation step before docking, it is advised to remove any irrelevant wat
 
 <a class="prompt prompt-pymol">remove resn HOH</a>
 
-Now let's vizualize the residues affected by binding as identified by NMR. From [Wang *et al*, EMBO J (2000)](https://onlinelibrary.wiley.com/doi/10.1093/emboj/19.21.5635/abstract){:target="_blank"} the following residues of E2A were identified has having significant chemical shift perturbations:
+Now let us vizualize the residues affected by binding as identified by NMR. From [Wang *et al*, EMBO J (2000)](https://onlinelibrary.wiley.com/doi/10.1093/emboj/19.21.5635/abstract){:target="_blank"} the following residues of E2A were identified has having significant chemical shift perturbations:
 
 <a class="prompt prompt-info">38,40,45,46,69,71,78,80,94,96,141</a>
 
@@ -266,7 +259,7 @@ color red, e2a_active<br>
 </a>
 
 <figure align="center">
-<img src="./e2a-surface-airs.png">
+<img src="/eduction/HADDOCK24/HADDOCK24-protein-protein-basic/e2a-surface-airs.png">
 </figure>
 
 Inspect the surface.
@@ -307,7 +300,7 @@ Since the biological function of this complex is to transfer a phosphate group f
 <a class="prompt prompt-question">Check the list of supported modified amino acids.</a>
 <a class="prompt prompt-question">What is the proper residue name for a phospho-histidine in HADDOCK?</a>
 
-In order to use a modified amino-acid in HADDOCK, the only thing you will need to do is to edit the PDB file and change the residue name of the amino-acid you want to modify. Don't bother deleting irrelevant atoms or adding missing ones, HADDOCK will take care of that. For E2A, the histidine that is phosphorylated has residue number 90. In order to change it to a phosphorylated histidine do the following:
+In order to use a modified amino-acid in HADDOCK, the only thing you will need to do is to edit the PDB file and change the residue name of the amino-acid you want to modify. Don not bother deleting irrelevant atoms or adding missing ones, HADDOCK will take care of that. For E2A, the histidine that is phosphorylated has residue number 90. In order to change it to a phosphorylated histidine do the following:
 
 <a class="prompt prompt-info">Edit the PDB file (*e2a_1F3G_B.pdb*) in your favorite editor</a>
 <a class="prompt prompt-info">Change the name of histidine 90 to NEP </a>
@@ -365,7 +358,7 @@ show lines, hpr_active<br>
 </a>
 
 <figure align="center">
-<img src="./hpr-ensemble.png">
+<img src="/eduction/HADDOCK24/HADDOCK24-protein-protein-basic/hpr-ensemble.png">
 </figure>
 
 You should be able to see the amount of conformational space sampled by those surface side-chains. You can clearly see that some residues do sample a large variety of conformations, one of which might lead to much better docking results.
@@ -676,7 +669,7 @@ In this execution mode the HADDOCK3 job should be submitted to the batch system
 
 <hr>
 
-### Scenario 1: 1000 rigidbody docking models, selection of top200 and flexible refinement + EM 
+### Scenario 1: 1000 rigidbody docking models, selection of top 200 and flexible refinement + EM 
 
 Now that we have all data ready, and know about execution modes of HADDOCK3 it is time to setup the docking for the first scenario. The restraint file to use for this is `e2a-hpr_air.tbl`. We proceed to produce 1000 rigidbody docking models, from which 200 will be selected and refined through flexible refinment and energy minimization. 
 For the analysis following the docking results, we are using the solved complex [1GGR](https://www.rcsb.org/structure/1GGR), named e2a-hpr_1GGR.pdb.
@@ -691,13 +684,8 @@ run_dir = "scenario1-full"
 
 # execution mode
 mode = "local"
-ncores = 50
-#  it will take the system's default
-# queue = "short"
-# concatenate models inside each job, concat = 5 each .job will produce 5 models
-concat = 5
-#  Limit the number of concurrent submissions to the queue
-queue_limit = 100
+# maximum of 50 cores (limited by the number of available cores)
+ncores = 50  
 
 # molecules to be docked
 molecules =  [
@@ -723,13 +711,11 @@ hise_1 = 15
 [rigidbody]
 tolerance = 5
 ambig_fname = "data/e2a-hpr_air.tbl"
-sampling = 1000
 
 [caprieval]
 reference_fname = "data/e2a-hpr_1GGR.pdb"
 
 [seletop]
-select = 200
 
 [caprieval]
 reference_fname = "data/e2a-hpr_1GGR.pdb"
@@ -751,7 +737,6 @@ reference_fname = "data/e2a-hpr_1GGR.pdb"
 [clustfcc]
 
 [seletopclusts]
-top_models = 500
 
 [caprieval]
 reference_fname = "data/e2a-hpr_1GGR.pdb"
@@ -782,11 +767,8 @@ run_dir = "scenario2-cltsel-full"
 
 # execution mode
 mode = "local"
-ncores = 50
-# concatenate models inside each job, concat = 5 each .job will produce 5 models
-concat = 5
-#  Limit the number of concurrent submissions to the queue
-queue_limit = 100
+# maximum of 50 cores (limited by the number of available cores)
+ncores = 50  
 
 # molecules to be docked
 molecules =  [
@@ -820,7 +802,7 @@ reference_fname = "data/e2a-hpr_1GGR.pdb"
 [clustfcc]
 
 [seletopclusts]
-## select the best 20 models of each cluster
+# select the best 20 models of each cluster
 top_models = 20
 
 [caprieval]
@@ -843,7 +825,6 @@ reference_fname = "data/e2a-hpr_1GGR.pdb"
 [clustfcc]
 
 [seletopclusts]
-top_models = 500
 
 [caprieval]
 reference_fname = "data/e2a-hpr_1GGR.pdb"
diff --git a/education/HADDOCK3/HADDOCK3-protein-protein-basic/phosphorylated-histidine.png b/education/HADDOCK3/HADDOCK3-protein-protein-basic/phosphorylated-histidine.png
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