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Nucleic Acids Research 2005 33(Web Server Issue):W154-W159; doi:10.1093/nar/gki588
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© The Author 2005. Published by Oxford University Press. All rights reserved
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Article

BhairPred: prediction of ß-hairpins in a protein from multiple alignment information using ANN and SVM techniques

Manish Kumar, Manoj Bhasin, Navjot K. Natt and G. P. S. Raghava*

Bioinformatics Centre, Institute of Microbial Technology Sector 39-A, Chandigarh, India

*To whom correspondence should be addressed. Tel: +91 172 2690557/2690225; Fax: +91 172 2690632/2690585; Email: raghava{at}imtech.res.in

Received December 15, 2004. Revised March 30, 2005. Accepted May 2, 2005.

This paper describes a method for predicting a supersecondary structural motif, ß-hairpins, in a protein sequence. The method was trained and tested on a set of 5102 hairpins and 5131 non-hairpins, obtained from a non-redundant dataset of 2880 proteins using the DSSP and PROMOTIF programs. Two machine-learning techniques, an artificial neural network (ANN) and a support vector machine (SVM), were used to predict ß-hairpins. An accuracy of 65.5% was achieved using ANN when an amino acid sequence was used as the input. The accuracy improved from 65.5 to 69.1% when evolutionary information (PSI-BLAST profile), observed secondary structure and surface accessibility were used as the inputs. The accuracy of the method further improved from 69.1 to 79.2% when the SVM was used for classification instead of the ANN. The performances of the methods developed were assessed in a test case, where predicted secondary structure and surface accessibility were used instead of the observed structure. The highest accuracy achieved by the SVM based method in the test case was 77.9%. A maximum accuracy of 71.1% with Matthew's correlation coefficient of 0.41 in the test case was obtained on a dataset previously used by X. Cruz, E. G. Hutchinson, A. Shephard and J. M. Thornton (2002) Proc. Natl Acad. Sci. USA, 99, 11157–11162. The performance of the method was also evaluated on proteins used in the ‘6th community-wide experiment on the critical assessment of techniques for protein structure prediction (CASP6)’. Based on the algorithm described, a web server, BhairPred (http://www.imtech.res.in/raghava/bhairpred/), has been developed, which can be used to predict ß-hairpins in a protein using the SVM approach.


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