Title:Development of Peptide-based Metallo-β-lactamase Inhibitors as a New Strategy to
Combat Antimicrobial Resistance: A Mini-review
Volume: 28
Issue: 44
Author(s): Qipeng Cheng, Ping Zeng, Edward Wai Chi Chan and Sheng Chen*
Affiliation:
- Department of Infectious Diseases
and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Kowloon, Hong
Kong
Keywords:
Metallo-β-lactamases (MBLs), peptide-based inhibitors, in silico screening, molecular docking, deep learning, machine learning.
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.
