creators_name: Agrawal, Piyush
creators_name: Bhalla, Sherry
creators_name: Chaudhary, Kumardeep
creators_name: Kumar, Rajesh
creators_name: Sharma, Meenu
creators_name: Raghava, G.P.S.
type: article
datestamp: 2018-03-27 04:37:18
lastmod: 2019-03-20 11:33:42
metadata_visibility: show
title:                 
In silco Approach for Prediction of Antifungal Peptides.
ispublished: pub
subjects: QR
keywords: amino acid composition; antifungal peptides; antimicrobial peptides; motifs; support vector machine
note: Open Access
abstract: 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).
date: 2018-02-26
date_type: published
publication: Frontiers in microbiology
volume: 9
publisher:  Frontiers Research Foundation
pagerange: 323
refereed: TRUE
issn: 1664-302X
official_url: https://www.frontiersin.org/articles/10.3389/fmicb.2018.00323/full
citation:   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