Antimicrobial peptides (AMPs) are distributed across all kingdoms of life and are an indispensable component of host defenses. They consist of predominantly short cationic peptides with a wide variety of structures and targets. Given the ever-emerging resistance of various pathogens to existing antimicrobial therapies, AMPs have recently attracted extensive interest as potential therapeutic agents. As the discovery of new AMPs has increased, many databases specializing in AMPs have been developed to collect both fundamental and pharmacological information. In this review, we summarize the sources, structures, modes of action, and classifications of AMPs. Additionally, we examine current AMP databases, compare valuable computational tools used to predict antimicrobial activity and mechanisms of action, and highlight new machine learning approaches that can be employed to improve AMP activity to combat global antimicrobial resistance.

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Serine proteases are the most predominant class performing a number of activities in organisms. Undergoing several mutations in their sequence over a span of a billion years yet S1 chymotrypsin/trypsin family has maintained a common fold. Granule Associated Serine Peptidases of Immune Defense (GASPIDS) belonging to the S1 class, found in the granules of immune cells are explicitly involved in the regulation of immune-related functions possessing a conserved catalytic triad Ser-Asp-His. The neutrophils along with other cells express four serine proteases (ELA2, PR3, CTSG and NSP4) sharing certain common characteristics. Similarly, CTLs and NK cells express a set of proteases, Granzymes. This study infers an evolutionary relationship among GASPIDs. We employed computational strategies and found that a higher degree of similarity existed between NSP4 and GZMM as compared to their members i.e. NSPs and granzymes, respectively. Similarly, GZMM a protease of NK cells and t cells lineage is found in the Met-ase locus consisting of NSPs genes i.e., Ela2, Prtn3 and Ctsg. The evolutionary relationship of Prss57/NSP4 and gzmm/GZMM was reconstructed through empirical phylogenetic analysis which revealed Prss57/NSP4 to be closely related to gzmm/GZMM. Additional co-expression analysis was carried out to determine the regulatory role of Prss57, inducing Gzmm. From this work, we inferred that Prss57/NSP4 is closely related to Gzmm/GZMM.