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

DIR@IMTECH: CSIR-Institute of Microbial Technology

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Title Prediction of transmembrane regions of beta-barrel proteins using ANN- and SVM-based methods.
 
Creator Natt, Navjot K
Kaur, Harpreet
Raghava, G.P.S.
 
Subject QR Microbiology
 
Description 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).
 
Publisher Oxford University Press
 
Date 2004-07-01
 
Type Article
PeerReviewed
 
Format application/pdf
 
Identifier http://crdd.osdd.net/open/213/1/raghava2004.6.pdf
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
 
Relation http://onlinelibrary.wiley.com/doi/10.1002/prot.20092/pdf
http://crdd.osdd.net/open/213/