Abstract
This paper describes in silico models 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).
Highlights
Despite tremendous advances in the field of antibiotics; the morbidity and mortality are quite high due to invasive fungal infections (Kanafani and Perfect, 2008)
The analysis showed that certain residues like C, G, H, K, R, and S, are more abundant or frequent in antifungal peptides (AFPs) whereas non-AFPs are dominated by residues like A, D, E, I, L, V, and W (Figure 1)
We found the addition of extra three features increased the performance of Matthews correlation coefficient (MCC) up to 0.01% compared to that obtained from the simple amino acid composition
Summary
Despite tremendous advances in the field of antibiotics; the morbidity and mortality are quite high due to invasive fungal infections (Kanafani and Perfect, 2008). (Sanglard, 2016) are responsible for causing 1.4 million deaths worldwide per year (Brown et al, 2012). Drug or antibiotic resistance is one of the major causes of millions death per year due to antifungal infections (Haegerstrand et al, 1992; Miceli et al, 2011). In order to overcome the problem of drug resistance, researchers are exploring alternatives to antibiotics (small molecules). One of the alternates to small chemical-based drugs is peptide-based therapeutics. It is safer and more effective than traditional therapeutics and provides effective arms to researchers fight against fungus. One can understand importance of peptide-based therapeutics from the fact that in the last one decade, number of peptide resources has been developed (Kapoor et al, 2012; Novkovic, 2012; Gautam et al, 2014; Waghu et al, 2014; Kumar et al, 2015; Agrawal et al, 2016; Mathur et al, 2016; Singh et al, 2016; Wang et al, 2016; Usmani et al, 2017)
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