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Prediction of pi-turns in proteins using PSI-BLAST profiles and secondary structure information.

Abstract：

:Due to the structural and functional importance of tight turns, some methods have been proposed to predict gamma-turns, beta-turns, and alpha-turns in proteins. In the past, studies of pi-turns were made, but not a single prediction approach has been developed so far. It will be useful to develop a method for identifying pi-turns in a protein sequence. In this paper, the support vector machine (SVM) method has been introduced to predict pi-turns from the amino acid sequence. The training and testing of this approach is performed with a newly collected data set of 640 non-homologous protein chains containing 1931 pi-turns. Different sequence encoding schemes have been explored in order to investigate their effects on the prediction performance. With multiple sequence alignment and predicted secondary structure, the final SVM model yields a Matthews correlation coefficient (MCC) of 0.556 by a 7-fold cross-validation. A web server implementing the prediction method is available at the following URL: http://210.42.106.80/piturn/.

journal_name

Biochem Biophys Res Commun

journal_title

Biochemical and biophysical research communications

authors

Wang Y,Xue ZD,Shi XH,Xu J

doi

10.1016/j.bbrc.2006.06.066

subject

Has Abstract

pub_date

2006-09-01 00:00:00

pages

574-80

issue

3

eissn

0006-291X

issn

1090-2104

pii

S0006-291X(06)01386-6

journal_volume

347

pub_type

杂志文章

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