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

Kaur, Harpreet ; Raghava, Gajendra Pal Singh (2006) Prediction of Cα-H···O and Cα-H···π interactions in proteins using recurrent neural network In Silico Biology, 6 (1-2). pp. 111-125. ISSN 1386-6338

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Official URL: http://iospress.metapress.com/content/7m13ehx6ra85...

Abstract

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.

Item Type:Article
Source:Copyright of this article belongs to IOS Press.
Keywords:Weak Hydrogen Bonds; Donor; Acceptor; Prediction; Neural Network; Secondary Structure
ID Code:43094
Deposited On:09 Jun 2011 13:13
Last Modified:17 Jun 2011 07:24

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