The Role of Active Site Flexible Loops in Catalysis and of Zinc in Conformational Stability of Bacillus cereus 569/H/9 β-Lactamase

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

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.

NDM-1 Zn1-binding residue His116 plays critical roles in antibiotic hydrolysis

Bacteria expressing NDM-1 have been labeled as superbugs because it confers upon them resistance to a broad range of β-lactam antibiotics. The enzyme has a di‑zinc active centre, with the Zn2 site extensively studied. The roles of active-site Zn1 ligand residues are, however, still not fully understood. We carried out structure-function studies using the mutants, H116A, H116N, and H116Q. Zinc content analysis showed that Zn1 binding was weakened by 40 to 60% in the H116 mutants. The enzymatic-activity studies showed that the lower hydrolysis rates were mainly caused by their weaker substrate binding. The catalytic efficiency (k_cat/K_m) of the mutants followed the order: WT > > H116Q (decreased by 4-20 fold) > H116A (decreased by 20-700 fold) ≥ H116N (decreased by 6-800 fold). The maximum effect was observed on H116N against penicillin G, whereas ampicillin was not hydrolyzed at all. The fold-increase of K_m values, which informs the weakening of substrate binding, were: H116A by 5-45 fold; H116N by 6-100 fold; H116Q by 2-10 fold. Molecular dynamics simulations suggested that the Zn1 site mutations affected the positions of Zn2 and the bridging hydroxide, by 0.8 to 1.2 Å, with the largest changes of ~1.5 Å observed on Zn2 ligand C221. A native hydrogen bond between H118 and D236 was disrupted in the H116N and H116Q mutants, which led to increased flexibility of loop 10. Consequently, residue N233 was no longer maintained at an optimal position for substrate binding. H116 connected loop 7 across Zn1 to loop 10, thereby contributed to the overall integrity. This work revealed that the H116-Zn1 interaction plays a critical role in defining the substrate-binding site. From these results, it can be inferred that inhibition strategies targeting the zinc ions may be a new direction for drug development.

A multiscale approach to predict the binding mode of metallo beta-lactamase inhibitors

Antibiotic resistance is a major threat to global public health. β-lactamases, which catalyze breakdown of β-lactam antibiotics, are a principal cause. Metallo β-lactamases (MBLs) represent a particular challenge because they hydrolyze almost all β-lactams and to date no MBL inhibitor has been approved for clinical use. Molecular simulations can aid drug discovery, for example, predicting inhibitor complexes, but empirical molecular mechanics (MM) methods often perform poorly for metalloproteins. Here we present a multiscale approach to model thiol inhibitor binding to IMP-1, a clinically important MBL containing two catalytic zinc ions, and predict the binding mode of a 2-mercaptomethyl thiazolidine (MMTZ) inhibitor. Inhibitors were first docked into the IMP-1 active site, testing different docking programs and scoring functions on multiple crystal structures. Complexes were then subjected to molecular dynamics (MD) simulations and subsequently refined through QM/MM optimization with a density functional theory (DFT) method, B3LYP/6-31G(d), increasing the accuracy of the method with successive steps. This workflow was tested on two IMP-1:MMTZ complexes, for which it reproduced crystallographically observed binding, and applied to predict the binding mode of a third MMTZ inhibitor for which a complex structure was crystallographically intractable. We also tested a 12-6-4 nonbonded interaction model in MD simulations and optimization with a SCC-DFTB QM/MM approach. The results show the limitations of empirical models for treating these systems and indicate the need for higher level calculations, for example, DFT/MM, for reliable structural predictions. This study demonstrates a reliable computational pipeline that can be applied to inhibitor design for MBLs and other zinc-metalloenzyme systems.

Bioinformatic analysis of subfamily-specific regions in 3D-structures of homologs to study functional diversity and conformational plasticity in protein superfamilies

Local 3D-structural differences in homologous proteins contribute to functional diversity observed in a superfamily, but so far received little attention as bioinformatic analysis was usually carried out at the level of amino acid sequences. We have developed Zebra3D - the first-of-its-kind bioinformatic software for systematic analysis of 3D-alignments of protein families using machine learning. The new tool identifies subfamily-specific regions (SSRs) - patterns of local 3D-structure (i.e. single residues, loops, or secondary structure fragments) that are spatially equivalent within families/subfamilies, but are different among them, and thus can be associated with functional diversity and function-related conformational plasticity. Bioinformatic analysis of protein superfamilies by Zebra3D can be used to study 3D-determinants of catalytic activity and specific accommodation of ligands, help to prepare focused libraries for directed evolution or assist development of chimeric enzymes with novel properties by exchange of equivalent regions between homologs, and to characterize plasticity in binding sites. A companion Mustguseal web-server is available to automatically construct a 3D-alignment of functionally diverse proteins, thus reducing the minimal input required to operate Zebra3D to a single PDB code. The Zebra3D + Mustguseal combined approach provides the opportunity to systematically explore the value of SSRs in superfamilies and to use this information for protein design and drug discovery. The software is available open-access at https://biokinet.belozersky.msu.ru/Zebra3D.

Protein formulation through automated screening of pH and buffer conditions, using the Robotein® high throughput facility

Among various factors, the direct environment (e.g. pH, buffer components, salts, additives, etc.…) is known to have a crucial effect on both the stability and activity of proteins. In particular, proper buffer and pH conditions can improve their stability and function significantly during purification, storage and handling, which is highly relevant for both academic and industrial applications. It can also promote data reproducibility, support the interpretation of experimental results and, finally, contribute to our general understanding of the biophysical properties of proteins. In this study, we have developed a high throughput screen of 158 different buffers/pH conditions in which we evaluated: (i) the protein stability, using differential scanning fluorimetry and (ii) the protein function, using either enzymatic assays or binding activity measurements, both in an automated manner. The modular setup of the screen allows for easy implementation of other characterization methods and parameters, as well as additional test conditions. The buffer/pH screen was validated with five different proteins used as models, i.e. two active-site serine β-lactamases, two metallo-β-lactamases (one of which is only active as a tetramer) and a single-domain dromedary antibody fragment (V_HH or nanobody). The formulation screen allowed automated and fast determination of optimum buffer and pH profiles for the tested proteins. Besides the determination of the optimum buffer and pH, the collection of pH profiles of many different proteins may also allow to delineate general concepts to understand and predict the relationship between pH and protein properties.