Division of Microbiology & Molecular Genetics, School of Medicine, Loma Linda University, Loma Linda, California, United States America.
AMALNA Consulting, Black Rock, Melbourne, VIC, Australia.
PLoS One. 2023 Sep 8;18(9):e0290845. doi: 10.1371/journal.pone.0290845. eCollection 2023.
Antimicrobial resistance is a great public health concern that is now described as a "silent pandemic". The global burden of antimicrobial resistance requires new antibacterial treatments, especially for the most challenging multidrug-resistant bacteria. There are various mechanisms by which bacteria develop antimicrobial resistance including expression of β-lactamase enzymes, overexpression of efflux pumps, reduced cell permeability through downregulation of porins required for β-lactam entry, or modifications in penicillin-binding proteins. Inactivation of the β-lactam antibiotics by β-lactamase enzymes is the most common mechanism of bacterial resistance to these agents. Although several effective small-molecule inhibitors of β-lactamases such as clavulanic acid and avibactam are clinically available, they act only on selected class A, C, and some class D enzymes. Currently, none of the clinically approved inhibitors can effectively inhibit Class B metallo-β-lactamases. Additionally, there is increased resistance to these inhibitors reported in several bacteria. The objective of this study is to use the Resonant Recognition Model (RRM), as a novel strategy to inhibit/modulate specific antimicrobial resistance targets. The RRM is a bio-physical approach that analyzes the distribution of energies of free electrons and posits that there is a significant correlation between the spectra of this energy distribution and related protein biological activity. In this study, we have used the RRM concept to evaluate the structure-function properties of a group of 22 β-lactamase proteins and designed 30-mer peptides with the desired RRM spectral periodicities (frequencies) to function as β-lactamase inhibitors. In contrast to the controls, our results indicate 100% inhibition of the class A β-lactamases from Escherichia coli and Enterobacter cloacae. Taken together, the RRM model can likely be utilized as a promising approach to design β-lactamase inhibitors for any specific class. This may open a new direction to combat antimicrobial resistance.
抗菌药物耐药性是一个严重的公共卫生问题,现在被描述为“无声的大流行”。全球范围内的抗菌药物耐药性问题需要新的抗菌治疗方法,尤其是针对最具挑战性的多药耐药菌。细菌产生抗菌药物耐药性的机制有很多种,包括表达β-内酰胺酶、过度表达外排泵、通过下调β-内酰胺进入所需的孔蛋白来降低细胞通透性,或修饰青霉素结合蛋白。β-内酰胺酶使β-内酰胺类抗生素失活是细菌对这些药物产生耐药性的最常见机制。虽然临床上有几种有效的β-内酰胺酶小分子抑制剂,如克拉维酸和阿维巴坦,但它们仅对某些 A 类、C 类和一些 D 类酶有效。目前,临床批准的抑制剂均不能有效抑制 B 类金属β-内酰胺酶。此外,一些细菌对抗这些抑制剂的耐药性也在增加。本研究旨在利用共振识别模型(RRM)作为一种新策略来抑制/调节特定的抗菌药物耐药性靶标。RRM 是一种生物物理方法,它分析自由电子能量的分布,并假定这种能量分布的光谱与相关蛋白质的生物活性之间存在显著相关性。在这项研究中,我们使用 RRM 概念来评估一组 22 种β-内酰胺酶蛋白的结构-功能特性,并设计了具有所需 RRM 光谱周期性(频率)的 30 肽作为β-内酰胺酶抑制剂。与对照相比,我们的结果表明,对大肠杆菌和阴沟肠杆菌的 A 类β-内酰胺酶的抑制率达到 100%。综上所述,RRM 模型可能可作为设计针对任何特定类别β-内酰胺酶抑制剂的有前途的方法。这可能为对抗抗菌药物耐药性开辟新的方向。