To improve the predictions of binding residues with DNA, RNA, carbohydrate, and peptide via multi-task deep neural networks

Motivation:The interactions of proteins with DNA, RNA, peptide, and carbohydrate play key roles in various biological processes.The studies of uncharacterized protein-molecules interactions could be aided by accurate predictions of residues that bind with partner molecules.However, the existing methods for predicting binding residues on proteins remain of relatively low accuracies due to the limited number of complex structures in databases.As different types of molecules partially share chemical mechanisms, the predictions for each molecular type should benefit from the binding information with other molecules types. Results:In this study, we employed a multiple task deep learning strategy to develop a new sequence-based method for simultaneously predicting binding residues/sites with multiple important molecule types named MTDsite.By combining four training sets for DNA, RNA, peptide, and carbohydrate-binding proteins, our method yielded accurate and robust predictions with AUC values of 0.852, 0836, 0.758, and 0.776 on their respective independent test sets, which are 0.52 to 6.6% better than other state-of-the-art methods.More importantly, this study provides a new strategy to improve predictions by combining multiple similar tasks.