Please cite our paper: Mishra NK, Chang J, Zhao PX(2014). Prediction of membrane transport proteins and their substrate specificities using primary sequence information. PLoS One. 9(6):e100278..
TrSSP: the Transporter Substrate Specificity Prediction Server   
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The present tool developed as a part of transporter annotation at substrate level specificity of transporter proteins. To demonstrate this study, we perform a systematic study of transporter proteins and create an integrative Support Vector Machine (SVM) based transporter substrate specificity predictor called TrSSP that is based on primary sequence information such as amino acid composition, AAIndex composition and PSSM profiles. We used to predict on seven classes of substrate specific transporter (amino acid transporter, anion transporter, cation transporter, electron transporter, protein/mRNA transporter, sugar transporter and other transporter) as well as transporter/non-transporter through a five-fold cross-validation, our AAIndex and PSSM based hybrid model achieve an average accuracy 76.69% with MCC 0.49. Benchmarking of TrSSP server on independent dataset achieve an average accuracy 77.64% with MCC 0.40. Performance of our model is better than any existing methods and same time it will work for more substrate specific classes. Five diverse prediction modules based on various features of a protein sequence have been implemented on the World Wide Web as a dynamic web server 'TrSSP' that provide wider options to the users extracting different features from their query protein sequences e.g. the simple amino acid composition, sequence-order based dipeptide composition, Position Specific Scoring Matrix (PSSM), including our best performing hybrid classifier. Therefore, we believe that 'TrSSP' can serve as a better complement to accurately annotate the Arabidopsis thaliana proteome. The complete list of subcellular predictions generated through ‘TrSSP’ is available under the 'Datasets' section.


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