Prediction of C^α-H···O and C^α-H···π interactions in proteins using recurrent neural network

Abstract

In this study, an attempt has been made to develop a method for predicting weak hydrogen bonding interactions, namely, C^α-H···O and C^α-H···π interactions in proteins using artificial neural network. Both standard feed-forward neural network (FNN) and recurrent neural networks (RNN) have been trained and tested using five-fold cross-validation on a non-homologous dataset of 2298 protein chains where no pair of sequences has more than 25% sequence identity. It has been found that the prediction accuracy varies with the separation distance between donor and acceptor residues. The maximum sensitivity achieved with RNN for C^α-H···O is 51.2% when donor and acceptor residues are four residues apart (i.e. at Δ_D-A=4) and for C^α-H···π is 82.1% at Δ_D-A=3. The performance of RNN is increased by 1-3% for both types of interactions when PSIPRED predicted protein secondary structure is used. Overall, RNN performs better than feed-forward networks at all separation distances between donor-acceptor pair for both types of interactions. Based on the observations, a web server CHpredict (available at http://www.imtech.res.in/raghava/chpredict/) has been developed for predicting donor and acceptor residues in C^α-H···O and C^α-H···π interactions in proteins.