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TrSSP / Transporter Substrate Specificity Prediction Server
A web server that implements SVM models. Users can upload or paste protein sequences in Fasta format for transporter and substrate prediction. Six prediction modules have been provided on this web server: an amino acid composition based SVM, an AAIndex based SVM, a PSSM (SwissProt) based SVM, an AAIndex/PSSM (SwissProt) hybrid SVM, a PSSM (UniRef90) based SVM, and an AAIndex/PSSM (UniRef90) hybrid SVM. The TrSSP web server uses the amino acid composition module as the default.
A genome-scale transporter prediction and characterization system called TransportTP that combined homology-based and machine learning methods in a two-phase classification approach. First, traditional homology methods were employed to predict novel transporters based on sequence similarity to known classified proteins in the Transporter Classification Database (TCDB). Second, machine learning methods were used to integrate a variety of features to refine the initial predictions. A set of rules based on transporter features was developed by machine learning using well-curated proteomes as guides.
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