bioiain 0.0.10.0.0

Package for structural analysis of proteins

bioiain

By Iain Visa ([e-mail])

Toolbox for structural analysis of proteins.

WIP EVERYTHING IS UNDER DEELOPMENT!

Many features are still not commented/documented or even mentioned. Feel free to explore and/or use any functions.

Can be downloaded from the test PiPy repository but note the used version as any function might change during development.

If you were to use this and find any issue I'll be happy to fix it :D

INFO

Relevant python code can be found in the src folder within their relevant folders. The test is for development use, and it's contents will be definitely deleted/modified at some point, and are not included in the package.

Preset workflows are being developed, including the projectDimer workflow. (WIP)

Protein Framework

Originally based on Biopython's hierarchy, but no longer dependant on it. Classes for structures and chains are included for manipulation and analysis of protein models. Designed to be expandable, custom classes are encouraged to match each purpose.

Unlike Biopython, residues and atoms do not share the base entity framework as they behave in significantly different ways. Also respective classes for nucleotides, ligands and water are included.

Includes general-purpose tools and pipelines for importing, processing,saving, and exporting structures in mmCIF format (but PDB is still slightly supported)

WIP: Allow PDB parsing, dealing with structures with several models, cast data from respective Biopython objects.

Symmetries

This framework is designed to work with all the information available in crystallographic structures, therefore symmetry is considered when available.

Machine Learning

Still at a very early stage, Bioiain includes a PyTorch-based ML framework to simplify the development and training of ML models, focused on structural data.

This includes a base model with all the utilities commonly used during train/test/eval/inference of models.

Also a dataset/embedding framework is also set up with integrations with the Protein Framework.

This includes integrated logging using Tensorboard.

ALEPH

Characteristic vectors are a powerful abstraction of protein structure, and can be calculated with ALEPH, through direct integration within the FragmentedStructure and Fragment classes included in the Protein Framework.

Tools

Utility functions to use and parse some external tools are included. For now this includes:

• DSSP
• PISA

Utilities

Additionally, a large set of utilities is included, from logging, to common mathematical operations.

Visualization

For structural visualisation, a custom PyMol scripting framework is included, replacing heavy sessions with generative commands.

Some common matplotlib utilities are also included.