Prediction of Cα-H····O and Cα-H···π interactions in proteins using recurrent neural network

摘要

In this study, an attempt has been made to develop a method for predicting weak hydrogen bonding interactions, namely, Cα-H···O and Cα-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 Cα-H···O is 51.2 when donor and acceptor residues are four residues apart (i. e. at ΔD-A = 4) and for Cα-H···π is 82.1 at ΔD-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 Cα-H···O and Cα-H···π interactions in proteins.