Centre of Biotechnology and Microbiology, University of Peshawar, Peshawar 25120, Pakistan.
Infection and Immunity Program, Department of Microbiology, Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia.
Int J Mol Sci. 2021 Oct 28;22(21):11691. doi: 10.3390/ijms222111691.
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.
抗菌肽 (AMPs) 分布于所有生命领域，是宿主防御的不可或缺的组成部分。它们主要由带正电荷的短肽组成，具有多种结构和靶标。鉴于各种病原体对现有抗菌疗法的耐药性不断出现，抗菌肽最近作为潜在的治疗药物引起了广泛关注。随着新抗菌肽的发现不断增加，已经开发了许多专门针对抗菌肽的数据库，以收集基础和药理学信息。在这篇综述中，我们总结了 AMPs 的来源、结构、作用模式和分类。此外，我们还研究了当前的 AMP 数据库，比较了用于预测抗菌活性和作用机制的有价值的计算工具，并强调了可用于提高 AMP 活性以对抗全球抗菌耐药性的新机器学习方法。
