论文标题：Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC
期刊：
作者：Prabina Kumar Meher, Tanmaya Kumar Sahu, Varsha Saini & Atmakuri Ramakrishna Rao
发表时间： 2017/02/13
数字识别码：10.1038/srep42362
原文链接：
《明升体育app手机版》发表的一项研究Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC尝试开发了一种以支持向量机（SVM）为基础的计算方法，用于以更高的精确度来预测抗微生物肽（AMP）。
抗微生物肽（AMP）是先天性免疫系统的重要组成部分，能有效抵抗致病病原体。通过湿实验室（wet-lab）内的实验来鉴定AMP往往极其昂贵。因此，需要开发出一种有效的计算工具，用于在体外实验之前鉴定出最佳的候选AMP。来自印度农业统计研究所的Atmakuri Ramakrishna Rao及其同事生成了肽的组分特征、物理明升手机特征和结构特征，随后将这些特征用作SVM的输入以预测AMP。通过使用基准数据集进行比较发现，他们所提出的方法比一些现有方法精确度更高。在这个新方法的基础上，研究人员还开发了一种在线预测服务器iAMPpred，用于帮助研究人员预测AMP。iAMPpred可在上免费获取。这一方法很好地补充了现有的用于预测AMP的工具和技术。
摘要：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 vitroexperimentation. 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.
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