Paper abstract / Intro:

Accurate protein binding site annotations are vital for a profound understanding of biological processes and protein interactions. Due to less supporting information a large number of proteins remain uncharacterised. For large sets of uncharacterised proteins, only amino acid information is available. In this paper, we proposed BSite-pro – a traditional approach – which makes use of protein sequence data for classification of binding sites. The classification procedure requires hand-crafted features from protein sequences. Our results stipulate noteworthy enhancements concerning predicted accuracy and recall upon comparison with previously proposed sequence-based techniques. BSite-pro achieves an overall validation accuracy of 85.06% and recall of 82.17%. Finally, we discuss that using the same feature extraction methods and model, we profitably dealt with two contrasting types of problem i.e. protein active and conserved sites prediction.

Authors:

• Malik Yasrub Bashir, Khalid Muneer ([email protected], [email protected]) -- Queries about ML should go here.
• Hafeez ur Rehman ([email protected]) -- Queries about Bioinformatics should go here.

Conference paper is available here: https://ieeexplore.ieee.org/document/8994703

Import points:

• Requires python3.6

• See requirements.txt for exact version of libraries used.

• It's suggested that you use virtualenv to create a new environment and then install required packages(Updated).

  pip3 install virtualenv
  virtualenv bs
  cd bs
  . bin/activate
  git cone <git_repo_url>
  pip install -r <git_repo_name>src/requirements.txt
  

Execution:

1. The code is properly commented with guidelines on how to extract features of protein sequence data.
2. Once the files have been created, one can run train/validation:

This is done through python train.py.

License

This code is provided under the MIT License.

Copyright 2019 Hafeez Ur Rehman and Malik Yasrub Bashir

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.