Predicting the Types of Ion Channel-Targeted Conotoxins Based on AVC-SVM Model.

Wang Xianfang,Wang Junmei,Zhang Yue,Wang Xiaolei

doi:10.1155/2017/2929807

Abstract

The conotoxin proteins are disulfide-rich small peptides. Predicting the types of ion channel-targeted conotoxins has great value in the treatment of chronic diseases, epilepsy, and cardiovascular diseases. To solve the problem of information redundancy existing when using current methods, a new model is presented to predict the types of ion channel-targeted conotoxins based on AVC (Analysis of Variance and Correlation) and SVM (Support Vector Machine). First, the F value is used to measure the significance level of the feature for the result, and the attribute with smaller F value is filtered by rough selection. Secondly, redundancy degree is calculated by Pearson Correlation Coefficient. And the threshold is set to filter attributes with weak independence to get the result of the refinement. Finally, SVM is used to predict the types of ion channel-targeted conotoxins. The experimental results show the proposed AVC-SVM model reaches an overall accuracy of 91.98%, an average accuracy of 92.17%, and the total number of parameters of 68. The proposed model provides highly useful information for further experimental research. The prediction model will be accessed free of charge at our web server.

Highlights

Conotoxins proteins have many merits, such as low relative molecular mass, stable structure, remarkable activity, high selectivity, and ease of synthesis [1]
According to its different targets acting on the organism, the conotoxins can be divided into three categories [3]: (1) acting on voltage-gated ion channels, (2) acting on the ligand-gated ion channel, and (3) acting on other receptors
In 2014, neural network and SVM classifier were used to predict lipid binding proteins by Bakhtiarizadeh et al [4]; the experiments showed that SVM was more successful at discriminating between LBPs and non-LBPs than neural network

Summary

Introduction

Conotoxins proteins have many merits, such as low relative molecular mass, stable structure, remarkable activity, high selectivity, and ease of synthesis [1]. The voltage-gated ion channels, known as voltage-sensitive channels, include potassium ion channels, calcium ion channels, and sodium ion channels. The performance of using different machine learning algorithms in predicting different targets is different. In 2014, neural network and SVM classifier were used to predict lipid binding proteins by Bakhtiarizadeh et al [4]; the experiments showed that SVM was more successful at discriminating between LBPs and non-LBPs than neural network. In 2016, the potential druggable proteins were predicted through comparing 6 kinds of machine learning algorithms by Jamali et al.; the experiments showed that neural network was the best classifier when predicting potential druggable proteins [5]. We will compare the performance of several different machine learning algorithms in the prediction of ion channel types of conotoxin

Methods

Results

Conclusion