TY  - JOUR
N1  - Open Access
ID  - open2008
UR  - https://www.frontiersin.org/articles/10.3389/fmicb.2018.00323/full
A1  - Agrawal, Piyush
A1  - Bhalla, Sherry
A1  - Chaudhary, Kumardeep
A1  - Kumar, Rajesh
A1  - Sharma, Meenu
A1  - Raghava, G.P.S.
Y1  - 2018/02/26/
N2  - 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).
PB  -  Frontiers Research Foundation
JF  - Frontiers in microbiology
VL  - 9
KW  - amino acid composition; antifungal peptides; antimicrobial peptides; motifs; support vector machine
SN  - 1664-302X
TI  -                 
In silco Approach for Prediction of Antifungal Peptides.
ER  -