KinConform takes any number of input structures, separates their chains and generates a fasta file of sequences. This fasta file is aligned (using MAPGAPS) to kinase profiles, identifying which chains are kinases. A series of measurements are then taken for each kinase chain and used as input to a machine learning classifier.
The output is tab-delimited and displays the conformation (active/inactive) for each input kinase chain. ##Usage
./kinconform XXX.pdb YYY.pdb ZZZ.pdb > a.out
To run test structures, simply cd test and make all.
Note: the profile and model directories should be colocated with kinconform. To install, add a symbolic link to kinconform from your bin/ directory. #Dependencies
Please ensure the following software is installed:
NumPy <http://www.numpy.org>MDAnalysis <http://www.mdanalysis.org>Biocma <https://github.com/etal/biocma>MAPGAPS <http://mapgaps.igs.umaryland.edu>
#Installation of development environment create conda environment:
conda activate kinconform
conda install mdanalysis
##Building biomca and mapgaps ###Biomca Go to the included biomca folder. Activate the kinconform conda environment first.
cd biomca
python setup.py build
python setup.py install
###MAPGAPS The source code of MAPGAPS is not available and there's only a linux compiled distribution of it available. This distribution is included in the MAPGAPS folder. If you are not on linux, this can be used within Docker on other OS's.
#Building Docker Image