Prediction of C alpha-H...O and C alpha-H...pi interactions in proteins using recurrent neural network.

Kaur, Harpreet and Raghava, G.P.S. (2006) Prediction of C alpha-H...O and C alpha-H...pi interactions in proteins using recurrent neural network. In silico biology, 6 (1-2). pp. 111-25. ISSN 1386-6338

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Abstract

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

Item Type:	Article
Additional Information:	OPEN ACCESS
Uncontrolled Keywords:	weak hydrogen bonds, donor, acceptor, prediction, neural network, secondary structure
Subjects:	Q Science > QR Microbiology
Depositing User:	Dr. K.P.S.Sengar
Date Deposited:	09 Jan 2012 04:23
Last Modified:	09 Jan 2012 04:23
URI:	http://crdd.osdd.net/open/id/eprint/148

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