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

Natt, Navjyot K. ; Kaur, Harpreet ; Raghava, G. P. S. (2004) Prediction of transmembrane regions of β-barrel proteins using ANN- and SVM-based methods Proteins: Structure, Function, and Bioinformatics, 56 (1). pp. 11-18. ISSN 0887-3585

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Official URL: http://onlinelibrary.wiley.com/doi/10.1002/prot.20...

Related URL: http://dx.doi.org/10.1002/prot.20092

Abstract

This article describes a method developed for predicting transmembrane β-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 β-barrel regions in proteins (available at http://www.imtech.res.in/raghava/tbbpred).

Item Type:Article
Source:Copyright of this article belongs to John Wiley and Sons.
Keywords:β-Barrels; Transmembrane Proteins; Artificial Neural Networks; Multiple Ssequence Alignment; Support Vector Machine; Physicochemical Parameters; LOOCV
ID Code:37319
Deposited On:25 Apr 2011 13:05
Last Modified:17 May 2016 20:13

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