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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


MHCLovac is based on the idea that each peptide can be represented as a signal of a certain physicochemical property. Each amino acid that comprises a peptide encodes specific combination of physicochemical properties that determine physicochemical properties of the peptide at a given position. By modelling amino acids properties in a smart way, MHCLovac obtains a unique physicochemical signal, or profile of each peptide (1.a). This is done for variety of physicochemical properties including hydrophobicity, charge, isoelectric point, donor and acceptor hydrogen bonds etc. Finally, by averaging out profiles of ligands known to bind certain HLA type, an HLA-specific profile for each physicochemical property is obtained. (1.c)

physicochemical profile

MHCLovac works by first building a reference profile database, containing profiles specific to each HLA type. This database is compiled based on experimentally confirmed ligands for each HLA type (resources/training_data) and physicochemical propery schemas (resources/schemas). MHCLovac package includes mhclovac-build tool, used for compiling a reference database and offers variety of options for choosing appropriate parameters.
Prebuilt databases for four species (human, chimpanzee, macaque, mouse) are included in the package.

Once the reference database is compiled, MHC binding prediction can be preformed by scoring similarity of each potential ligand to precompiled HLA-specific profile (for each physicochemical property included in the profile database). Scoring is based on correlation coefficients, with the availability of assigning weights to each physicochemical schema used. MHCLovac offers degree of flexibility with regard to scoring.

  • Research and description of the tool will be available soon on wiki pages.


Install from PyPI repository

pip install mhclovac

Download and install from git repository

git clone
cd mhclovac
pip install .


mhclovac --fasta example.fasta 
         --hla HLA-A*02:01 
         --peptide_length 9
         --reference_data human
         --output output.txt

To build reference profiles use mhclovac-build which is included in the package.

mhclovac-build --input_data binding_data.tsv
               --schemas h:hydrophobicity.csv i:isoelectric.csv
               --species human
               --output human.hdf5

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Files for mhclovac, version 1.0.1
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