mhclovac

MHC binding prediction based on modeled physicochemical properties of peptides


Keywords
bioinformatics, mhc-binding-prediction
License
MIT
Install
pip install mhclovac==2.0.0

Documentation

MHCLovac

MHC binding prediction based on modeled physicochemical properties of peptides.

New release - MHCLovac 2.0

MHCLovac 2.0 makes almost somewhat accurate predictions. It really is not precise.


About

MHCLovac uses Bayesian linear regression for binding affinity prediction based on modeled physicochemical properties of peptides. MHCLovac uses pre-developed proteinko package to obtain modeled distributions of physicochemical properties.

modeled_distributions

Physicochemical properties in question are:

  • Hydropathy
  • Number of donor hydrogen bonds
  • Number of acceptor hydrogen bonds
  • Isoelectric point
  • Van der Waals molecular volume

Once the distributions are obtained, the area under the curve (AUC) is calculated using a sliding frame technique. The AUC values for each of five physicochemical properties are concatenated into single feature vector.

Model training is performed on standardized AUC values. We tested number of linear regression models and concluded that BayesianRidge algorithm from sklearn package produces most consistent predictions across various training set configurations.

regression_models

MHCLovac makes modestly accurate predictions, which can be seen on plots below.

binding_predictions

Installation

Install from PyPI repository

pip install mhclovac

Download and install from git repository

git clone https://github.com/stefs304/mhclovac
cd mhclovac
pip install .

Usage

mhclovac --fasta <fasta file> 
         --hla <hla type (ex. HLA-A*02:01)> 
         --peptide_length <peptide length>
         --output <output file (optional)>