Journal
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
Volume 22, Issue 21, Pages -Publisher
MDPI
DOI: 10.3390/ijms222111691
Keywords
antimicrobial peptide; database; structure; mode of action; machine learning; HMM; BLAST
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Antimicrobial peptides (AMPs) are widely distributed and play a crucial role in host defense against pathogens. With emerging resistance to existing therapies, AMPs have garnered increased interest as potential therapeutic agents. The development of AMP databases and computational tools, along with new machine learning approaches, are being utilized to enhance AMP activity and combat global antimicrobial resistance.
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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