Predicting Presynaptic and Postsynaptic Neurotoxins by Developing Feature Selection Technique.

Hua Tang,Ping Zou,Chenggang Duan,Po Huang,Chunmei Zhang,Yunchun Yang,Rong Chen

doi:10.1155/2017/3267325

Hua Tang, Ping Zou + Show 5 more

Open Access

https://doi.org/10.1155/2017/3267325

Copy DOI

Journal: BioMed research international	Publication Date: Jan 1, 2017
Citations: 8	License type: CC BY 4.0

Affiliation: Southwest Medical University

Abstract

Presynaptic and postsynaptic neurotoxins are proteins which act at the presynaptic and postsynaptic membrane. Correctly predicting presynaptic and postsynaptic neurotoxins will provide important clues for drug-target discovery and drug design. In this study, we developed a theoretical method to discriminate presynaptic neurotoxins from postsynaptic neurotoxins. A strict and objective benchmark dataset was constructed to train and test our proposed model. The dipeptide composition was used to formulate neurotoxin samples. The analysis of variance (ANOVA) was proposed to find out the optimal feature set which can produce the maximum accuracy. In the jackknife cross-validation test, the overall accuracy of 94.9% was achieved. We believe that the proposed model will provide important information to study neurotoxins.

Highlights

Neurotoxins act typically against channels to block or enhance synaptic transmission
The study of presynaptic and postsynaptic neurotoxin will give us important clues for drug-target discovery and drug design
To overcome the shortcoming mentioned above, in this study, we developed a new method based on feature selection technique to predict presynaptic neurotoxins and postsynaptic neurotoxins

Summary

Introduction

Neurotoxins act typically against channels to block or enhance synaptic transmission. The function of presynaptic neurotoxins is to act at the presynaptic membrane [2]. They usually block neuromuscular transmission and inhibit the neurotransmitter release due to their specific enzymatic activities [3]. It is important to develop machine learning approach to predict presynaptic and postsynaptic neurotoxins. Song proposed using bilayer support vector machine (SVM) to improve prediction accuracy based on a new benchmark dataset [7]. To overcome the shortcoming mentioned above, in this study, we developed a new method based on feature selection technique to predict presynaptic neurotoxins and postsynaptic neurotoxins. We will introduce how to construct a new benchmark dataset, to formulate neurotoxin samples using peptide sequences, and to obtain the expected result produced by best feature subset

Materials and Methods

Results and Discussion

Conclusions