期刊
MEDICINAL CHEMISTRY
卷 16, 期 5, 页码 620-625出版社
BENTHAM SCIENCE PUBL LTD
DOI: 10.2174/1573406415666191002152441
关键词
Tuberculosis; anti-tubercular peptides; g-gap dipeptide; support vector; machine; feature selection; web-server
资金
- National Nature Scientific Foundation of China [31771471, 61772119]
- Natural Science Foundation for Distinguished Young Scholar of Hebei Province [C2017209244]
Background: Tuberculosis is one of the biggest threats to human health. Recent studies have demonstrated that anti-tubercular peptides are promising candidates for the discovery of new anti-tubercular drugs. Since experimental methods are still labor intensive, it is highly desirable to develop automatic computational methods to identify anti-tubercular peptides from the huge amount of natural and synthetic peptides. Hence, accurate and fast computational methods are highly needed. Methods and Results: In this study, a support vector machine based method was proposed to identify anti-tubercular peptides, in which the peptides were encoded by using the optimal g-gap dipeptide compositions. Comparative results demonstrated that our method outperforms existing methods on the same benchmark dataset. For the convenience of scientific community, a freely accessible web-server was built, which is available at http://lin-group.cn/server/iATP. Conclusion: It is anticipated that the proposed method will become a useful tool for identifying anti-tubercular peptides.
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