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.

背景：结核病是对人类健康的最大威胁之一。最近的研究表明，抗结核肽是发现新的抗结核药物的有希望的候选者。由于实验方法仍然是劳动密集型的，因此非常需要开发自动计算方法以从大量的天然和合成肽中鉴定抗结核肽。因此，非常需要准确且快速的计算方法。

方法和结果：在这项研究中，提出了一种基于支持向量机的方法来鉴定抗结核肽，其中通过使用最佳的g-gap二肽组成来编码这些肽。比较结果表明，在相同基准数据集上，我们的方法优于现有方法。为了方便科学界，构建了一个可免费访问的Web服务器，该服务器可从http://lin-group.cn/server/iATP获得。

结论：预期所提出的方法将成为鉴定抗结核肽的有用工具。