metfish

Code for the MetFish LDRD project

Installation

The following commands will install the code in this repository in such a way that will allow one to use the tools provided by said code. With that said, the provided sequence of commands may not suit your specific needs. As this repository follows PEP 517 style packaging, there are many ways to install the software, so please use discretion and adapt as necessary.

git clone [email protected]:lbl-cbg/metfish.git
cd metfish
pip install -r requirements.txt
pip install .

Training the model

Training the modified OpenFold model requires some additional dependencies and constraints. Please follow these instructions to setup a conda environment for model training and evaluation. Note the OpenFold installation requires CUDA 11.

conda create -n metfish python=3.9
conda activate metfish
pip install torch==1.12.1+cu113 -f https://download.pytorch.org/whl/torch_stable.html  # install separately to specify with findlinks
pip install .[training,viz]

To install additional resources required by OpenFold, run this command and copy to the openfold/resources folder:

wget -N --no-check-certificate -P openfold/resources \
    https://git.scicore.unibas.ch/schwede/openstructure/-/raw/7102c63615b64735c4941278d92b554ec94415f8/modules/mol/alg/src/stereo_chemical_props.txt

To run training, use the following command, which provides several additional flags:

python src/metfish/msa_model/train.py path/to/data path/to/output

Note that the training pipeline expects the following directory structure:

data_dir/
├── pdb/
│   └── {pdb_prefix}{name}{pdb_ext}  # e.g., fixed_1ABC_A.pdb
├── saxs_r/
│   └── {name}{saxs_ext}   # SAXS P(r) data files
├── msa/
│   └── a3m
│     └── {msa_id}.a3m     # MSA data files
└── scripts/
    ├── input_training.csv
    └── input_validation.csv

• CSV files (input_training.csv, input_validation.csv): Must contain:

  • name - Protein identifier (used to locate corresponding files)
  • seqres - Protein amino acid sequence
  • msa_id (optional) - MSA identifier if different from name

• PDB files: Standard PDB format structure files

  • Default naming: fixed_{name}.pdb (prefix can vary based on dataset)
  • The script auto-detects if using simulated data and adjusts paths accordingly

• SAXS files: CSV format containing P(r) distribution data

  • Must include a P(r) column with the pair distance distribution
  • Common naming patterns: {name}.pdb.pr.csv or {name}.pr.csv
  • The script auto-detects the extension pattern from existing files

• MSA directories: Each protein should have a subdirectory in msa/ containing:

  • A3M files (.a3m): Multiple sequence alignments in A3M format structured within an a3m subfolder.

Generating Figures

The scripts/generate_figures.py script runs model inference and generates comparison visualizations for protein structure predictions. This script processes multiple models (AlphaFold, SFold_NMR, SFold_NMA) and creates figures comparing their performance.

Basic Usage

python scripts/generate_figures.py \
  --data-dir /path/to/data \
  --ckpt-dir /path/to/checkpoints \
  --output-dir /path/to/output \
  --skip-inference false

Note that the inference pipeline expects the following directory structure:

data_dir/
├── input_all.csv          # CSV with protein names and sequences
├── pdbs/
│   └── {name}.pdb         # PDB structure files
├── saxs_r/
│   └── {name}{saxs_ext}   # SAXS P(r) data files
├── msa/
│   └── a3m
│     └── {msa_id}.a3m     # MSA data files

File Format Requirements:

• CSV file (input_all.csv): Must contain the following columns:

  • name - Protein identifier (used to locate corresponding files)
  • seqres - Protein amino acid sequence
  • msa_id (optional) - MSA identifier if different from name

• PDB files: Standard PDB format structure files

  • Default naming: {name}.pdb
  • Can be customized with --pdb-ext flag

• SAXS files: CSV format containing P(r) distribution data

  • Must include a P(r) column with the pair distance distribution
  • Default naming: {name}_atom_only.csv or {name}.pdb.pr.csv
  • Can be customized with --saxs-ext flag

• MSA directories: Each protein should have a subdirectory in msa/ containing:

  • A3M files (.a3m): Multiple sequence alignments in A3M format structured within an a3m subfolder.

Commands

• calc-pr: Calculate P(r) for models from a PDB or mmCIF file
• extract-seq: Extract Sequence from a PDB file.
• generate-nma-conformers: Generate conformations from normal mode analysis