Abstract
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.
Highlights
Antimicrobial peptides (AMPs) are important components of the innate immune system that have been found to be effective against disease causing pathogens
The binary (0, 1) and compositional features were used in AntiBP and AntiBP2 respectively to map the peptide sequences onto numeric feature vectors, where the numeric vectors were used as input in artificial neural network (ANN)[13] and support vector machine (SVM)[14] respectively for prediction of antibacterial peptides
This paper presents a SVM-based computational approach that can be used for predicting the effective AMPs with higher accuracy as compared to several existing approaches
Summary
Antimicrobial peptides (AMPs) are important components of the innate immune system that have been found to be effective against disease causing pathogens. Development of sequence-based computational tools can be helpful in designing the effective antimicrobial agents by identifying the best candidate AMP prior to the synthesis and testing against pathogens in wet-lab[7] In this direction, computational tools like AntiBP1, AMPER8, CAMP3, AntiBP29, AVPpred[10], ClassAMP11, iAMP-2L7 and EFC-FCBF12 have been developed for the prediction of AMPs. The binary (0, 1) and compositional features were used in AntiBP and AntiBP2 respectively to map the peptide sequences onto numeric feature vectors, where the numeric vectors were used as input in artificial neural network (ANN)[13] and support vector machine (SVM)[14] respectively for prediction of antibacterial peptides. The proposed approach was found to perform better than several existing approaches for predicting AMPs, when comparison was made using bench mark dataset
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