Metadata-Version: 2.1
Name: mhclovac
Version: 1.0.1
Summary: MHC binding prediction based on modeled physicochemical properties of peptides
Home-page: https://github.com/stefs304/mhclovac
Author: Stefan Stojanovic
Author-email: stefs304@gmail.com
License: UNKNOWN
Description: # MHCLovac
        MHC binding prediction based on modeled physicochemical properties of peptides
        
        * [About](#about)
        * [Installation](#installation)
        * [Usage](#usage)
        
        <hr>
        
        <a name='about'></a>
        ### 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)*
          
        ![physicochemical profile](https://raw.githubusercontent.com/stefs304/mhclovac/master/resources/plots/example.png)
         
          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](https://github.com/stefs304/mhclovac/tree/master/resources/training_data)) 
          and physicochemical propery schemas 
          ([resources/schemas](https://github.com/stefs304/mhclovac/tree/master/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](https://github.com/stefs304/mhclovac/wiki) pages.
           
        
        <a name='installation'></a>
        ### 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 .
        ```
        
        <a name='usage'></a>
        ### 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
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 2
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Healthcare Industry
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Chemistry
Description-Content-Type: text/markdown
