Support Vector Machine Based Prediction of Glutathione S-Transferase Proteins

Author(s): Nitish Kumar Mishra, Manish Kumar, G.P.S. Raghava.

Journal Name: Protein & Peptide Letters

Volume 14 , Issue 6 , 2007

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Glutathione S-transferase (GST) proteins play vital role in living organism that includes detoxification of exogenous and endogenous chemicals, survivability during stress condition. This paper describes a method developed for predicting GST proteins. We have used a dataset of 107 GST and 107 non-GST proteins for training and the performance of the method was evaluated with five-fold cross-validation technique. First a SVM based method has been developed using amino acid and dipeptide composition and achieved the maximum accuracy of 91.59% and 95.79% respectively. In addition we developed a SVM based method using tripeptide composition and achieved maximum accuracy 97.66% which is better than accuracy achieved by HMM based searching (96.26%). Based on above study a web-server GSTPred has been developed (

Keywords: GST protein, Support vector machine, artificial intelligence, sensitivity, specificity, correlation

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Article Details

Year: 2007
Page: [575 - 580]
Pages: 6
DOI: 10.2174/092986607780990046
Price: $58

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