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Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC.

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Title Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC.
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Creator Prabina Kumar Meher
Tanmaya Sahu
Varsha Saini
Atmakuri Ramakrishna Rao
 
Subject Antimicrobial peptides
pathogens
vitro experimentation
 
Description Not Available
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.
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Date 2022-08-05T07:25:29Z
2022-08-05T07:25:29Z
2017-02-13
 
Type Research Paper
 
Identifier Meher, P., Sahu, T., Saini, V. et al. Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC. Sci Rep 7, 42362 (2017). https://doi.org/10.1038/srep42362
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http://krishi.icar.gov.in/jspui/handle/123456789/73709
 
Language English
 
Relation Not Available;
 
Publisher Not Available