Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou's general PseAAC.
Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou's general PseAAC.
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DOI：
10.1038/srep42362
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发表时间：
2017-02-13
影响因子：
4.6
通讯作者：
Rao AR
中科院分区：
文献类型：
--
作者：
Meher PK;Sahu TK;Saini V;Rao AR
抗菌肽（Antimicrobial Peptides，AMP）是先天免疫系统的重要组成部分，已发现其对引起疾病的病原体有效。通过湿实验室实验鉴定AMP是昂贵的。因此，开发有效的计算工具是必不可少的，以确定最佳的候选AMP之前，在体外实验。在这项研究中，我们试图开发一种基于支持向量机（SVM）的计算方法，用于预测AMP，提高准确性。最初，生成肽的组成、物理化学和结构特征，其随后用作SVM中的输入以预测AMP。与现有的几种方法相比，所提出的方法取得了更高的准确性，同时使用基准数据集进行比较。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/上免费访问。所提出的方法被认为是补充的工具和技术，在过去已经开发的预测AMP。
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.