An overview of databases and bioinformatics tools for plant antimicrobial peptides

Identification, Screening and Antibacterial Mechanism Analysis of Novel Antimicrobial Peptides from Sturgeon (Acipenser ruthenus) Spermary

Fish is an important source of antimicrobial peptides. This study aimed to identify and screen antibacterial peptides with excellent antibacterial activity derived from sturgeon spermary peptides (SSPs) and to analyze their antibacterial activity and mechanism. Liquid chromatography-mass spectrometry/mass spectrometry methods were used to analyze and identify peptide sequences, computational prediction tool and molecular docking methods were used for virtual screening of antimicrobial peptides, and finally, candidate peptides were synthesized by solid-phase synthesis method. The results demonstrate that SSPs have excellent inhibitory activity against Escherichia coli with an inhibitory rate of 76.46%. Most parts of the SSPs were derived from the sturgeon (Acipenser ruthenus) histones, and the coverage of histone H2B was the highest (45%). Two novel peptides (NDEELNKLM and RSSKRRQ) were obtained by in silico prediction tools and molecular docking, which may interact with the DNA gyrase and dihydrofolate reductase of E. coli by forming salt bridges and hydrogen bonds. Compared to the individual peptides, the antibacterial effect was significantly improved by mixing the two peptides in equal proportions. Two novel peptides change the permeability of the E. coli cell membranes and may exert antimicrobial activity by inhibiting the metabolic process of the nucleic acids.

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. 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.