Prediction of voltage-gated ion channel toxins by increment of diversity

Abstract

Voltage-gated ion channel is the molecular target for a broad range of toxins. Voltage-gated ion channel toxins are excellent pharmacological tools in toxicology and neuroscience. They have been used as molecular scaffolding agents, drugs, and insecticides. In this study, voltage-gated calcium, potassium and sodium channel toxins are predicted by the increment of diversity (ID) algorithm. Each protein is represented by 400 pseudo amino acid compositions and 9 MEME motif features. The Maximum Relevance Minimum Redundancy (MRMR) is applied for ranking 400 pseudo amino acid compositions. The results of jackknife test indicate that the best predictive results are obtained when using 50 higher ranked pseudo amino acid compositions and 9 MEME motif features. Based on the predictive results, our results suggest the usefulness and potential of ID algorithm for prediction of voltage-gated ion channel toxins using protein sequence derived information.