MHC binding prediction based on modeled physicochemical properties of peptides
Project description
MHCLovac
MHC binding prediction based on modeled physicochemical properties of peptides
About
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)
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.
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 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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