DELPHI: accurate deep ensemble model for protein interaction sites prediction

Abstract Motivation Proteins usually perform their functions by interacting with other proteins, which is why accurately predicting protein-protein interaction (PPI) binding sites is a fundamental problem. Experimental methods are slow and expensive. Therefore, great efforts are being made towards increasing the performance of computational methods. Results We propose DELPHI (DEep Learning Prediction of Highly probable protein Interaction sites), a new sequence-based deep learning suite for PPI binding sites prediction. DELPHI has an ensemble structure with data augmentation and it employs novel features in addition to existing ones. We comprehensively compare DELPHI to nine state-of-the-art programs on five datasets and show that it is more accurate. Availability The trained model, source code for training, predicting, and data processing are freely available at https://github.com/lucian-ilie/DELPHI . All datasets used in this study can be downloaded at http://www.csd.uwo.ca/~ilie/DELPHI/ . Contact ilie@uwo.ca