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Current Bioinformatics

Editor-in-Chief

ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

Research Article

Characterization and Prediction of Presynaptic and Postsynaptic Neurotoxins Based on Reduced Amino Acids and Biological Properties

Author(s): Yiyin Cao, Chunlu Yu, Shenghui Huang, Shiyuan Wang, Yongchun Zuo* and Lei Yang*

Volume 16, Issue 3, 2021

Published on: 07 July, 2020

Page: [364 - 370] Pages: 7

DOI: 10.2174/1574893615999200707150512

Price: $65

Abstract

Background: Presynaptic and postsynaptic neurotoxins are two important categories of neurotoxins. Due to the important role of presynaptic and postsynaptic neurotoxins in pharmacology and neuroscience, their identification has become very important biologically.

Methods: In this study, statistical tests and F-scores were used to calculate differences between amino acids and biological properties. The support vector machine was used to predict presynaptic and postsynaptic neurotoxins using reduced amino acid alphabet types.

Results: Using the reduced amino acid alphabet as input parameters of the support vector machine, the overall accuracy of our classifier increased to 91.07%, which was the highest overall accuracy observed in this study. When compared with the other published methods, better predictive results were obtained by our classifier.

Conclusion: In summary, we analyzed the differences between two neurotoxins with respect to amino acids and biological properties, constructing a classifier that predicts these two neurotoxins using the reduced amino acid alphabet.

Keywords: Neurotoxin, reduced amino acid alphabet, biological property, support vector machine, machine learning, jackknife test.

Graphical Abstract
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