Record Details

In silco Approach for Prediction of Antifungal Peptides.

DIR@IMTECH: CSIR-Institute of Microbial Technology

View Archive Info
 
 
Field Value
 
Title In silco Approach for Prediction of Antifungal Peptides.
 
Creator Agrawal, Piyush
Bhalla, Sherry
Chaudhary, Kumardeep
Kumar, Rajesh
Sharma, Meenu
Raghava, G.P.S.
 
Subject QR Microbiology
 
Description This paper describesmodels developed using a wide range of peptide features for predicting antifungal peptides (AFPs). Our analyses indicate that certain types of residue (e.g., C, G, H, K, R, Y) are more abundant in AFPs. The positional residue preference analysis reveals the prominence of the particular type of residues (e.g., R, V, K) at N-terminus and a certain type of residues (e.g., C, H) at C-terminus. In this study, models have been developed for predicting AFPs using a wide range of peptide features (like residue composition, binary profile, terminal residues). The support vector machine based model developed using compositional features of peptides achieved maximum accuracy of 88.78% on the training dataset and 83.33% on independent or validation dataset. Our model developed using binary patterns of terminal residues of peptides achieved maximum accuracy of 84.88% on training and 84.64% on validation dataset. We benchmark models developed in this study and existing methods on a dataset containing compositionally similar antifungal and non-AFPs. It was observed that binary based model developed in this study preforms better than any model/method. In order to facilitate scientific community, we developed a mobile app, standalone and a user-friendly web server 'Antifp' (http://webs.iiitd.edu.in/raghava/antifp).
 
Publisher Frontiers Research Foundation
 
Date 2018-02-26
 
Type Article
PeerReviewed
 
Relation https://www.frontiersin.org/articles/10.3389/fmicb.2018.00323/full
http://crdd.osdd.net/open/2008/
 
Identifier Agrawal, Piyush and Bhalla, Sherry and Chaudhary, Kumardeep and Kumar, Rajesh and Sharma, Meenu and Raghava, G.P.S. (2018) In silco Approach for Prediction of Antifungal Peptides. Frontiers in microbiology, 9. p. 323. ISSN 1664-302X