Identification of voltage-gated potassium channel subfamilies from sequence information using support vector machine.

Proteins belonging to different subfamilies of Voltage-gated K(+) channels (VKC) are functionally divergent. The traditional method to classify ion channels is more time consuming. Thus, it is highly desirable to develop novel computational methods for VKC subfamily classification. In this study, a support vector machine based method was proposed to predict VKC subfamilies using amino acid and dipeptide compositions. In order to remove redundant information, a novel feature selection technique was employed to single out optimized features. In the jackknife cross-validation, the proposed method (VKCPred) achieved an overall accuracy of 93.09% with 93.22% average sensitivity and 98.34% average specificity, which are superior to that of other two state-of-the-art classifiers. These results indicate that VKCPred can be efficiently used to identify and annotate voltage-gated K(+) channels' subfamilies. The VKCPred software and dataset are freely available at http://cobi.uestc.edu.cn/people/hlin/tools/VKCPred/.