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

Abstract：

:This paper describes a method for predicting a supersecondary structural motif, beta-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 beta-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 beta-hairpins in a protein using the SVM approach.

journal_name

Nucleic Acids Res

journal_title

Nucleic acids research

authors

Kumar M,Bhasin M,Natt NK,Raghava GP

doi

10.1093/nar/gki588

keywords:

subject

Has Abstract

pub_date

2005-07-01 00:00:00

pages

W154-9

issue

Web Server issue

eissn

0305-1048

issn

1362-4962

journal_volume

33

pub_type

杂志文章

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