Prediction of Antimicrobial Peptides from Metatranscriptomic samples: A case study

Abstract

The COVID-19 pandemic has shed new light on the ongoing antimicrobial resistance (AMR) global crisis as the use of antibiotics has dramatically increased to treat severely ill patients. In this post-pandemic era, the world is in urgent need of new and effective drugs that can fight multiple drug-resistant (MDR) bacterial infections. Natural antimicrobial peptides (AMP) are produced as a first line of defense in almost all living beings and they offer the most promising alternative to conventional antibiotics. These peptides are able to eliminate MDR bacteria through a set of killing mechanisms that do not induce bacterial resistance quickly. Moreover, they are effective against other classes of pathogens including virus, fungi, protozoa etc. as well as containing antitumor, anticancer and immunomodulatory properties. The comprehensive advantages of AMPs led to extensive ongoing research and trials to make AMP-based drugs commercially available for clinical purposes. Metagenomics is a culture-independent technique to study and characterize unculturable microbes, which can be applied to identify organisms and their bioactive components, including AMPs, from diverse environmental samples. This review provides a brief overview of the origins, functions, and potentials of AMPs, including the computational identification of putative AMPs, and also discusses the concepts and applications of metagenomics and Metatranscriptomics. Finally, a case study employs a workflow for predicting probable AMPs from the metatranscriptomic data of uncultured marine sediment microbiota, and characterizes the identified peptides both structurally and functionally. The establishment of such prediction pipelines makes way for discovering novel AMPs from the ever-increasing metagenomic data.