Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi-110012, India.
Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi-110012, India.
Sci Rep. 2017 Feb 13;7:42362. doi: 10.1038/srep42362.
Antimicrobial peptides (AMPs) are important components of the innate immune system that have been found to be effective against disease causing pathogens. Identification of AMPs through wet-lab experiment is expensive. Therefore, development of efficient computational tool is essential to identify the best candidate AMP prior to the in vitro experimentation. In this study, we made an attempt to develop a support vector machine (SVM) based computational approach for prediction of AMPs with improved accuracy. Initially, compositional, physico-chemical and structural features of the peptides were generated that were subsequently used as input in SVM for prediction of AMPs. The proposed approach achieved higher accuracy than several existing approaches, while compared using benchmark dataset. Based on the proposed approach, an online prediction server iAMPpred has also been developed to help the scientific community in predicting AMPs, which is freely accessible at http://cabgrid.res.in:8080/amppred/. The proposed approach is believed to supplement the tools and techniques that have been developed in the past for prediction of AMPs.
抗菌肽 (AMPs) 是先天免疫系统的重要组成部分，已被证明对致病病原体有效。通过湿实验室实验鉴定 AMP 成本高昂。因此，开发有效的计算工具对于在体外实验之前识别最佳候选 AMP 至关重要。在这项研究中，我们尝试开发一种基于支持向量机 (SVM) 的计算方法，以提高预测 AMP 的准确性。最初，生成了肽的组成、物理化学和结构特征，随后将其用作 SVM 预测 AMP 的输入。与基准数据集相比，所提出的方法的准确性更高。基于所提出的方法，还开发了一个在线预测服务器 iAMPpred，以帮助科学界预测 AMP，该服务器可在 http://cabgrid.res.in:8080/amppred/ 免费访问。所提出的方法被认为可以补充过去为预测 AMP 而开发的工具和技术。
