Prediction of Conotoxin Superfamilies by the Naive Bayes Classifier

Conotoxins are a group of high specialized and functionally diverse peptides. Because the conotoxins are selectivity for membrane receptors and ion channels, so they can make valuable biological probes and drug targets. Successful prediction of the conotoxin superfamily peptide has important biological meaning in the pharmacology of the neurotoxins. In this work, based on the concept that the function of toxin protein is determined by its protein sequence, the Naive Bayes classifier and feature selection method are proposed to predict the conotoxin superfamilies. The obtained results of the jackknife test indicated that the overall prediction accuracy is 84.92% for the dataset with 305 conotoxins. This algorithm was also used to predict the dataset with 116 conotoxins and 60 non-conotoxins, the higher predictive rates than some previous studies are obtained in our study.