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

View Article: PubMed Central - PubMed

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.

No MeSH data available.


Snapshots of (a) server page of iAMPpred and (b) result page after execution of the program with an example dataset. The results are displayed in a tabular format showing the sequence identifier and the probabilities with which the sequences are predicted as antibacterial, antiviral and antifungal peptides.
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f4: Snapshots of (a) server page of iAMPpred and (b) result page after execution of the program with an example dataset. The results are displayed in a tabular format showing the sequence identifier and the probabilities with which the sequences are predicted as antibacterial, antiviral and antifungal peptides.

Mentions: An online prediction server “iAMPpred” has been developed to predict the propensity of a peptide sequence as antibacterial, antiviral and antifungal peptides. Snapshots of the web pages showing the execution of iAMPpred for an example dataset along with the results are shown in Fig. 4. For user guidance with regard to feature generation, prediction method and input-output, links have been provided in the main menu. The sequences with probabilities of being antiviral, antibacterial and antifungal peptides are displayed in the result page. For reproducible research, links to download the trained datasets (http://cabgrid.res.in:8080/amppred/about.html) are also provided. The prediction server is freely accessible at http://cabgrid.res.in:8080/amppred.


Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou ’ s general PseAAC
Snapshots of (a) server page of iAMPpred and (b) result page after execution of the program with an example dataset. The results are displayed in a tabular format showing the sequence identifier and the probabilities with which the sequences are predicted as antibacterial, antiviral and antifungal peptides.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5304217&req=5

f4: Snapshots of (a) server page of iAMPpred and (b) result page after execution of the program with an example dataset. The results are displayed in a tabular format showing the sequence identifier and the probabilities with which the sequences are predicted as antibacterial, antiviral and antifungal peptides.
Mentions: An online prediction server “iAMPpred” has been developed to predict the propensity of a peptide sequence as antibacterial, antiviral and antifungal peptides. Snapshots of the web pages showing the execution of iAMPpred for an example dataset along with the results are shown in Fig. 4. For user guidance with regard to feature generation, prediction method and input-output, links have been provided in the main menu. The sequences with probabilities of being antiviral, antibacterial and antifungal peptides are displayed in the result page. For reproducible research, links to download the trained datasets (http://cabgrid.res.in:8080/amppred/about.html) are also provided. The prediction server is freely accessible at http://cabgrid.res.in:8080/amppred.

View Article: PubMed Central - PubMed

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.

No MeSH data available.