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Front. Microbiol. | doi: 10.3389/fmicb.2018.00323

In silico approach for prediction of antifungal peptides.

  • 1Institute of Microbial Technology (CSIR), India
  • 2Indraprastha Institute of Information Technology Delhi, India

This paper describes in silico models developed using a wide range of peptide features for predicting antifungal peptides. Our analyses indicate that certain types of residue (e.g., C, G, H, K, R, Y) are more abundant in antifungal peptides. 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 antifungal peptides 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 84.88% on training and 84.64% on validation dataset. We benchmark models developed in this study and existing methods on a dataset contain compositionally similar antifungal and non-antifungal peptides. 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).

Keywords: antimicrobial peptides, Antifungal peptides, Amino acid composition, Support vector machine, motifs

Received: 08 Oct 2017; Accepted: 12 Feb 2018.

Edited by:

Octavio L. Franco, Universidade Católica de Brasília, Brazil

Reviewed by:

Gill Diamond, University of Florida, United States
William F. Porto, Universidade Católica Dom Bosco, Brazil  

Copyright: © 2018 Agrawal, Bhalla, Chaudhary, Kumar, Sharma and Raghava. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Gajendra P. Raghava, Institute of Microbial Technology (CSIR), Chandigarh, India, raghava@imtech.res.in