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Development of Peptide-based Metallo-β-lactamase Inhibitors as a New Strategy to Combat Antimicrobial Resistance: A Mini-review

生物信息学 抗菌剂 抗生素耐药性 生物 对接(动物) 序列同源性 抗生素 计算生物学 微生物学 生物化学 医学 肽序列 基因 护理部
作者
Sheng Chen,Qipeng Cheng,Ping Zeng,Edward Wai Chi Chan
出处
期刊:Current Pharmaceutical Design [Bentham Science Publishers]
卷期号:28 (44): 3538-3545 被引量:2
标识
DOI:10.2174/1381612828666220929154255
摘要

Abstract: Global dissemination of antimicrobial resistance (AMR) not only poses a significant threat to human health, food security, and social development but also results in millions of deaths each year. In Gram-negative bacteria, the primary mechanism of resistance to β-lactam antibiotics is the production of β-lactamases, one of which is carbapenem-hydrolyzing β-lactamases known as carbapenemases. As a general scheme, these enzymes are divided into Ambler class A, B, C, and D based on their protein sequence homology. Class B β-lactamases are also known as metallo-β-lactamases (MBLs). The incidence of recovery of bacteria expressing metallo-β- lactamases (MBLs) has increased dramatically in recent years, almost reaching a pandemic proportion. MBLs can be further divided into three subclasses (B1, B2, and B3) based on the homology of protein sequences as well as the differences in zinc coordination. The development of inhibitors is one effective strategy to suppress the activities of MBLs and restore the activity of β-lactam antibiotics. Although thousands of MBL inhibitors have been reported, none have been approved for clinical use. This review describes the clinical application potential of peptide-based drugs that exhibit inhibitory activity against MBLs identified in past decades. In this report, peptide-based inhibitors of MBLs are divided into several groups based on the mode of action, highlighting compounds of promising properties that are suitable for further advancement. We discuss how traditional computational tools, such as in silico screening and molecular docking, along with new methods, such as deep learning and machine learning, enable a more accurate and efficient design of peptide-based inhibitors of MBLs.

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