Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou's general PseAAC.
作者： Prabina Kumar Meher , Tanmaya Kumar Sahu , Varsha Saini , Atmakuri Ramakrishna Rao
DOI: 10.1038/SREP42362
关键词:
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 AMPs through wet-lab experiment is expensive. Therefore, development efficient computational tool essential identify best candidate AMP prior in vitro experimentation. In this study, we made an attempt develop a support vector machine (SVM) based approach for prediction with improved accuracy. Initially, compositional, physico-chemical and structural features were generated subsequently used as input SVM AMPs. The proposed achieved higher accuracy than several existing approaches, while compared using benchmark dataset. Based on approach, online server iAMPpred has also developed help scientific community predicting AMPs, which freely accessible at http://cabgrid.res.in:8080/amppred/. believed supplement tools techniques past
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