Inhibition of β-lactamase function by de novo designed peptide

Abstract

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.

Analysis of Myelin Using the Resonant Recognition Model

“…Even more the RRM model describes this complex relationship through single number representing resonant frequency for all constituents in the process of myelin existence. Once when such characteristic frequency has been identified, we can use it to design de novo peptides, as we did successfully in number of other examples [15][16][17][18][19][20][21][22][23][24], that would be able to repair damaged myelin, which possibly could be used for treatment of MS and AD.…”

“…Interestingly, we have identified that all participants in the myelin formation and stability, both proteins and cholesterol as small molecule, do have only one common RRM frequency at f=0.1953±0.0060, indicating that this frequency is critical for myelin formation and stability. With such identified RRM characteristic frequency, the next step would be to design de novo bioactive peptides using already established and successfully tested RRM procedure [15][16][17][18][19][20][21][22][23][24]. Such designed peptides might be able to facilitate repair of damaged myelin and could lead to new possible treatment of MS and AD.…”

“…Once the characteristic frequency for biological function and/or interaction of the macromolecule is identified, it is possible to design de novo peptides/proteins with the desired RRM frequency components and consequently with desired biological functions and/or interactions [8][9][10][11][15][16][17][18][19][20][21][22][23][24]. This concept of de novo designed bioactive peptides using the RRM model has already been successfully applied and experimentally tested in a number of examples in the design of: peptide that can prevent resistant bacteria [24], peptide that can prevent SARS-CoV-2 virus entry into the host cells via ACE2 receptor [22][23], peptide to mimic myxoma virus oncolytic function [21], IL-12 analogue [20], peptides related to skin cancer [19], HIV envelope protein analogue [16][17][18], FGF analogue [15].…”