Wei Chen*, Pengmian Feng and Fulei Nie Pages 620 - 625 ( 6 )
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.
Tuberculosis, anti-tubercular peptides, g-gap dipeptide, support vector, machine, feature selection, web-server.
Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611730, Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611730, Center for Genomics and Computational Biology, School of Life Sciences, North China University of Science and Technology, Tangshan 063000