Support vector machine based prediction of glutathione S-transferase proteins.
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 (http://www.imtech.res.in/raghava/gstpred/).
Mishra
Nitish Kumar
NK
Bioinformatics Center, Institute of Microbial Technology, Chandigarh, India.
Kumar
Manish
M
Raghava
G P S
GP
D000596:Amino Acids / Q000032:analysis; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D005982:Glutathione Transferase / Q000737:chemistry* / Q000378:metabolism; D008390:Markov Chains; D010455:Peptides / Q000737:chemistry* / Q000378:metabolism; D020539:Sequence Analysis, Protein; D014584:User-Computer Interface
D000596:Amino Acids; D010455:Peptides; D005982:Glutathione Transferase