SPRINGS: Prediction of Protein-Protein Interaction Sites Using Artificial Neural Networks
- Published
- Accepted
- Subject Areas
- Computational Biology
- Keywords
- Leave One Out Cross Validation, Neural Networks, Position-specific scoring matrix, Protein-protein interactions, Sequence-based predictor
- Copyright
- © 2014 Singh et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
- Cite this article
- 2014. SPRINGS: Prediction of Protein-Protein Interaction Sites Using Artificial Neural Networks. PeerJ PrePrints 2:e266v1 https://doi.org/10.7287/peerj.preprints.266v1
Abstract
Knowledge of protein-protein interaction sites provides an important base for deciphering novel drug targets and applications of enzyme-based studies. But on account of biological complexity and transient forms, determination of these sites is a challenge in biology. Various computational approaches are being explored for relevant prediction based on available protein sequence-structure information. Here we propose a novel method SPRINGS (Sequence-based predictor of PRotein- protein interactING Sites) for identification of interaction sites based on sequences. It uses protein evolutionary information, averaged cumulative hydropathy and predicted relative solvent accessibility from amino acid chains in artificial neural network architecture with a promising performance for protein-protein interactions sites based research and applications.
Author Comment
This is a submission to PeerJ for review.