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Bioinformatics Advance Access originally published online on March 29, 2005
Bioinformatics 2005 21(10):2370-2374; doi:10.1093/bioinformatics/bti358
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

Improved method for predicting ß-turn using support vector machine

Qidong Zhang , Sukjoon Yoon {dagger} and William J. Welsh *

Department of Pharmacology, University of Medicine and Dentistry of New Jersey (UMDNJ), Robert Wood Johnson Medical School and Informatics Institute of UMDNJ 675 Hoes Lane, Piscataway, NJ 08854, USA

*To whom correspondence should be addressed.

Motivation: Numerous methods for predicting ß-turns in proteins have been developed based on various computational schemes. Here, we introduce a new method of ß-turn prediction that uses the support vector machine (SVM) algorithm together with predicted secondary structure information. Various parameters from the SVM have been adjusted to achieve optimal prediction performance.

Results: The SVM method achieved excellent performance as measured by the Matthews correlation coefficient (MCC = 0.45) using a 7-fold cross validation on a database of 426 non-homologous protein chains. To our best knowledge, this MCC value is the highest achieved so far for predicting ß-turn. The overall prediction accuracy Qtotal was 77.3%, which is the best among the existing prediction methods. Among its unique attractive features, the present SVM method avoids overtraining and compresses information and provides a predicted reliability index.

Availability: The algorithm is available via a web server on: http://serine.umdnj.edu/~zhangq3/betaturn/

Contact: welshwj{at}umdnj.edu

Supplementary information: http://serine.umdnj.edu/~zhangq3/betaturn


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