A review on the diversity of antimicrobial peptides and genome mining strategies for their prediction

Abstract

Antibiotic resistance has become one of the most serious threats to human health in recent years. In response to the increasing microbial resistance to the antibiotics currently available, it is imperative to develop new antibiotics or explore new approaches to combat antibiotic resistance. Antimicrobial peptides (AMPs) have shown considerable promise in this regard, as the microbes develop low or no resistance against them. The discovery and development of AMPs still confront numerous obstacles such as finding a target, developing assays, and identifying hits and leads, which are time-consuming processes, making it difficult to reach the market. However, with the advent of genome mining, new antibiotics could be discovered efficiently using tools such as BAGEL, antiSMASH, RODEO, etc., providing hope for better treatment of diseases in the future. Computational methods used in genome mining automatically detect and annotate biosynthetic gene clusters in genomic data, making it a useful tool in natural product discovery. This review aims to shed light on the history, diversity, and mechanisms of action of AMPs and the data on new AMPs identified by traditional as well as genome mining strategies. It further substantiates the various phases of clinical trials for some AMPs, as well as an overview of genome mining databases and tools built expressly for AMP discovery. In light of the recent advancements, it is evident that targeted genome mining stands as a beacon of hope, offering immense potential to expedite the discovery of novel antimicrobials.