Abstract
Ion channels are integral membrane proteins that control movement of ions into or out of cells. They are key components in a wide range of biological processes. Different types of ion channels have different biological functions. With the appearance of vast proteomic data, it is highly desirable for both basic research and drug-target discovery to develop a computational method for the reliable prediction of ion channels and their types. In this study, we developed a support vector machine-based method to predict ion channels and their types using primary sequence information. A feature selection technique, analysis of variance (ANOVA), was introduced to remove feature redundancy and find out an optimized feature set for improving predictive performance. Jackknife cross-validated results show that the proposed method can discriminate ion channels from non-ion channels with an overall accuracy of 86.6%, classify voltage-gated ion channels and ligand-gated ion channels with an overall accuracy of 92.6% and predict four types (potassium, sodium, calcium and anion) of voltage-gated ion channels with an overall accuracy of 87.8%, respectively. These results indicate that the proposed method can correctly identify ion channels and provide important instructions for drug-target discovery. The predictor can be freely downloaded from http://cobi.uestc.edu.cn/people/hlin/tools/IonchanPred/.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call