iATP: A Sequence Based Method for Identifying Anti-tubercular Peptides

Abstract

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.

Keywords: Tuberculosis; anti-tubercular peptides; feature selection; g-gap dipeptide; machine; support vector; web-server.