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MHC binding prediction based on modeled physicochemical properties of peptides

Project description


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.


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.


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.


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



Install from PyPI repository

pip install mhclovac

Download and install from git repository

git clone
cd mhclovac
pip install .


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

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Files for mhclovac, version 2.0.0
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Filename, size mhclovac-2.0.0-py3-none-any.whl (7.3 MB) File type Wheel Python version py3 Upload date Hashes View
Filename, size mhclovac-2.0.0.tar.gz (7.0 MB) File type Source Python version None Upload date Hashes View

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