OnionMHC

The source code for our paper: OnionMHC: A deep learning model for peptide — HLA-A*02:01 binding predictions using both structure and sequence feature sets

Peptide - HLA-A*02:01 binding prediction using structure and sequence feature sets

Required Modules

1. Tensorflow 2.0.0
   
2. Pandas 0.25.3
   
3. scikit-learn 0.21.3
   
4. scipy 1.3.2
   
5. Numpy 1.17.4
   

Generating structure based features

Structure based features can be generated by running generate_features_cam_o.py. Each line in the input file <input.dat> contains the path to the pdb structures.

python generate_features_cam_o.py -inp <input.dat> -out <output.csv>

Parallelizing generation of structure based features using mpirun

mpirun -np 16 python generate_features_cam_o.py -inp <input.dat> -out <output.csv>

Making Predictions

1. Clone this repository
   

git clone https://github.com/shikhar249/OnionMHC

2. Run onionmhc.py
   

python onionmhc.py -struc <structure-based features file> -seq <sequence file> -mod path/to/models/fold{0..4}_model{0..2}_bls_lstm.h5 -out <output file>

The example of prediction results will be shown as:

peptide_sequences	OnionMHC_score	Binding_affinity(nM)
FLIAYQPLL	0.901299	2.909335
NLLTTPKFT	0.483376	267.669274
GTHSWEYWG	0.062278	25487.509380
...	...	...

Citations

Please cite our paper

@article{doi:10.1142/S2424913020500095,
author = {Saxena, Shikhar and Animesh, Sambhavi and Fullwood, Melissa J. and Mu, Yuguang},
title = {OnionMHC: A deep learning model for peptide — HLA-A*02:01 binding predictions using both structure and sequence feature sets},
journal = {Journal of Micromechanics and Molecular Physics},
volume = {05},
number = {03},
pages = {2050009},
year = {2020},
doi = {10.1142/S2424913020500095}