In Current pharmaceutical design ; h5-index 57.0
Global dissemination of antimicrobial resistance (AMR) not only posed a significant threat to human health, food security, and social development, but also resulted 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 were 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. Development of inhibitors is one effective strategy to suppress the activities of MBLs and restore the activity of β-lactam antibiotics. Although thousands of MBLs inhibitors have been reported, none has 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 design of peptide-based inhibitors of MBLs designs more accurate and efficient.
Cheng Qipeng, Zeng Ping, Chi Chan Edward Wai, Chen Sheng
deep learning, in silico screening, machine learning, metallo-β-lactamases (MBLs), molecular docking, peptide-based inhibitors