Conotoxins are small disulfide-rich peptides that are invaluable channel-targeted peptides and target neuronal receptors. They show prospects for being potent pharmaceuticals in the treatment of Alzheimer's disease, Parkinson's disease, and epilepsy. Accurate prediction of conotoxin superfamily would have many important applications towards the understanding of its biological and pharmacological functions. In the present study, we develop a novel approach called PredCSF for predicting the conotoxin superfamily using modified one-versus-rest SVMs. The input features to the PredCSF classifers are composed of physicochemical properties, evolutionary information, second structure and amino acid composition. Each SVM classifier's output is the probability assigned to a particular superfamily. The prediction results show that PredCSF can obtain an overall accuracy of 90.65% based on a newly constructed benchmark dataset. It is expected to be a powerful tool for in silico identification of novel conotonxins. Please click
here to download the stand-alone PredCSF software package.