Predicting Presynaptic and Postsynaptic Neurotoxins by Developing Feature Selection Technique. (12th February 2017)

• Record Type: Journal Article
• Authors: Tang, Hua; Yang, Yunchun; Zhang, Chunmei; Chen, Rong; Huang, Po; Duan, Chenggang; Zou, Ping
• Other Names: Cao Ren-Zhi Academic Editor.
• Is Part Of: BioMed research international. Volume 2017(2017)
• Publication Date: 2017-02-12
• Subjects: Medicine -- Periodicals; Biology -- Periodicals; Biotechnology -- Periodicals; Life sciences -- Periodicals; 610.5
• DOI: 10.1155/2017/3267325
• Languages: English
• ISSNs: 2314-6133

Abstract

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.