Prediction of transmembrane regions of beta-barrel proteins using ANN- and SVM-based methods.

Natt, Navjot K and Kaur, Harpreet and Raghava, G.P.S. (2004) Prediction of transmembrane regions of beta-barrel proteins using ANN- and SVM-based methods. Proteins, 56 (1). pp. 11-8. ISSN 1097-0134

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Abstract

This article describes a method developed for predicting transmembrane beta-barrel regions in membrane proteins using machine learning techniques: artificial neural network (ANN) and support vector machine (SVM). The ANN used in this study is a feed-forward neural network with a standard back-propagation training algorithm. The accuracy of the ANN-based method improved significantly, from 70.4% to 80.5%, when evolutionary information was added to a single sequence as a multiple sequence alignment obtained from PSI-BLAST. We have also developed an SVM-based method using a primary sequence as input and achieved an accuracy of 77.4%. The SVM model was modified by adding 36 physicochemical parameters to the amino acid sequence information. Finally, ANN- and SVM-based methods were combined to utilize the full potential of both techniques. The accuracy and Matthews correlation coefficient (MCC) value of SVM, ANN, and combined method are 78.5%, 80.5%, and 81.8%, and 0.55, 0.63, and 0.64, respectively. These methods were trained and tested on a nonredundant data set of 16 proteins, and performance was evaluated using "leave one out cross-validation" (LOOCV). Based on this study, we have developed a Web server, TBBPred, for predicting transmembrane beta-barrel regions in proteins (available at http://www.imtech.res.in/raghava/tbbpred).

Item Type:	Article
Additional Information:	Copyright of this article belongs to OUP.
Subjects:	Q Science > QR Microbiology
Depositing User:	Dr. K.P.S.Sengar
Date Deposited:	06 Jan 2012 14:53
Last Modified:	09 Jan 2015 09:16
URI:	http://crdd.osdd.net/open/id/eprint/213

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