PyProt

PyProt (short for Python Proteins) is a python package with libraries designed to represent and manipulate proteins.

What's in the box

Here is an overview of the sub-packages and the libraries they contain:

• base contains basic representation classes
  • aminoacid defines the AminoAcid class which represents a single amino acid
  • sequence represents an amino acid Sequence (or protein) and works as a python list
• data contains parsers for standard data files
  • fasta parses and saves .fasta files which contain proteins
  • dssp parses and saves .dssp files which contain score matrices
• align contains classes that align proteins together and represent the results
  • align defines the Align class which aligns sequences together, and Aligned which stores the alignment results
  • blosum creates scoring matrices with the BLOSUM algorithm
  • score represents scoring matrices, both position-specific (PSSM) and not (ScoreMatrix)
• structure implements algorithms that can be trained on data sets to issue structure predictions for new proteins
  • GOR implements the GOR.3 algorithm for structure prediction

Dependencies

In order to use this package, you'll need a working version of Python 3.3 or later installed, as well as pip.

The installation process will automatically install all of the package's dependencies, which are listed in the setup-req.txt file.

Installation

You can install Pyprot in the following ways (make sure you use a Python 3 version of pip):

• By executing the following in your command line

pip install git+https://github.com/StanIsAdmin/PyProt.git --user

• By downloading the package's source code here, unzipping it and then running

pip install <downloaded-code-path> --user

Documentation

Not sure what's in the box yet ? Check the online documentation.

The source code is documented in the standard docstring format, so its documentation will appear automatically if you use an editor that supports that format (which really means any editor but vim).

Working examples are provided in the examples/ folder of the repository, with code and explanations embedded inside jupyter notebook files. You can read them from GitHub, but in order to run them yourself, you'll need to install Jupyter.

Contribute

Anyone is welcome to contribute by submitting a pull request or by opening new issues.