SPRINGS: Prediction of Protein-Protein Interaction Sites Using Artificial Neural Networks

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.