PTPAMP: Prediction Tool for Plant-derived Antimicrobial Peptides

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. Amongst all organisms, the presence of multiple genes encoding AMPs in plants demands 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 in a web server termed PTPAMP. The designed web server is capable to classify a query peptide sequence into four functional activities i.e. antimicrobial (AMP), antibacterial (ABP), antifungal (AFP), and antiviral (AVP). PTPAMP achieved an average area under the curve (AUC) 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 residue in plant-derived AMPs and distribution of other residues like G, S, K, and R which differs as per the peptide structural family. Finally, the developed web server is made user-friendly and presently available at http://www.nipgr.ac.in/PTPAMP/. We expect the substantial input of this predictor for high throughput identification of plant-derived AMPs followed by the additional insights into their functions.