PTPAMP: prediction tool for plant-derived antimicrobial peptides

Abstract

The emergence of antimicrobial peptides (AMPs) as a potential alternative to conventional antibiotics has led to the development of efficient computational methods for predicting AMPs. Among all organisms, the presence of multiple genes encoding AMPs in plants demands the development of a plant-based prediction tool. To this end, we developed models based on multiple peptide features like amino acid composition, dipeptide composition, and physicochemical attributes for predicting plant-derived AMPs. The selected compositional models are integrated into a web server termed PTPAMP. The designed web server is capable of classifying a query peptide sequence into four functional activities, i.e., antimicrobial (AMP), antibacterial (ABP), antifungal (AFP), and antiviral (AVP). Our models achieved an average area under the curve of 0.95, 0.91, 0.85, and 0.88 for AMP, ABP, AFP, and AVP, respectively, on benchmark datasets, which were ~ 6.75% higher than the state-of-the-art methods. Moreover, our analysis indicates the abundance of cysteine residues in plant-derived AMPs and the distribution of other residues like G, S, K, and R, which differ as per the peptide structural family. Finally, we have developed a user-friendly web server, available at the URL: http://www.nipgr.ac.in/PTPAMP/. We expect the substantial input of this predictor for high-throughput identification of plant-derived AMPs followed by additional insights into their functions.