Multiscale Simulations of Clavulanate Inhibition Identify the Reactive Complex in Class A β-Lactamases and Predict the Efficiency of Inhibition

Mechanistic Insights into Enzyme Catalysis from Explaining Machine-Learned Quantum Mechanical and Molecular Mechanical Minimum Energy Pathways

With the increasing popularity of machine learning (ML) applications, the demand for explainable artificial intelligence techniques to explain ML models developed for computational chemistry has also emerged. In this study, we present the development of the Boltzmann-weighted cumulative integrated gradients (BCIG) approach for effective explanation of mechanistic insights into ML models trained on high-level quantum mechanical and molecular mechanical (QM/MM) minimum energy pathways. Using the acylation reactions of the Toho-1 β-lactamase and two antibiotics (ampicillin and cefalexin) as the model systems, we show that the BCIG approach could quantitatively attribute the energetic contribution in one system and the relative reactivity of individual steps across different systems to specific chemical processes such as the bond making/breaking and proton transfers. The proposed BCIG contribution attribution method quantifies chemistry-interpretable insights in terms of contributions from each elementary chemical process, which is in agreement with the validating QM/MM calculations and our intuitive mechanistic understandings of the model reactions.

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.

Allosteric communication in class A β-lactamases occurs via cooperative coupling of loop dynamics

Understanding allostery in enzymes and tools to identify it offer promising alternative strategies to inhibitor development. Through a combination of equilibrium and nonequilibrium molecular dynamics simulations, we identify allosteric effects and communication pathways in two prototypical class A β-lactamases, TEM-1 and KPC-2, which are important determinants of antibiotic resistance. The nonequilibrium simulations reveal pathways of communication operating over distances of 30 Å or more. Propagation of the signal occurs through cooperative coupling of loop dynamics. Notably, 50% or more of clinically relevant amino acid substitutions map onto the identified signal transduction pathways. This suggests that clinically important variation may affect, or be driven by, differences in allosteric behavior, providing a mechanism by which amino acid substitutions may affect the relationship between spectrum of activity, catalytic turnover, and potential allosteric behavior in this clinically important enzyme family. Simulations of the type presented here will help in identifying and analyzing such differences.

Dynamical nonequilibrium molecular dynamics reveals the structural basis for allostery and signal propagation in biomolecular systems

ABSTRACT

A dynamical approach to nonequilibrium molecular dynamics (D-NEMD), proposed in the 1970s by Ciccotti et al., is undergoing a renaissance and is having increasing impact in the study of biological macromolecules. This D-NEMD approach, combining MD simulations in stationary (in particular, equilibrium) and nonequilibrium conditions, allows for the determination of the time-dependent structural response of a system using the Kubo-Onsager relation. Besides providing a detailed picture of the system's dynamic structural response to an external perturbation, this approach also has the advantage that the statistical significance of the response can be assessed. The D-NEMD approach has been used recently to identify a general mechanism of inter-domain signal propagation in nicotinic acetylcholine receptors, and allosteric effects in β -lactamase enzymes, for example. It complements equilibrium MD and is a very promising approach to identifying and analysing allosteric effects. Here, we review the D-NEMD approach and its application to biomolecular systems, including transporters, receptors, and enzymes.

Molecular Bases of the Membrane Association Mechanism Potentiating Antibiotic Resistance by New Delhi Metallo-β-lactamase 1

Resistance to last-resort carbapenem antibiotics is an increasing threat to human health, as it critically limits therapeutic options. Metallo-β-lactamases (MBLs) are the largest family of carbapenemases, enzymes that inactivate these drugs. Among MBLs, New Delhi metallo-β-lactamase 1 (NDM-1) has experienced the fastest and largest worldwide dissemination. This success has been attributed to the fact that NDM-1 is a lipidated protein anchored to the outer membrane of bacteria, while all other MBLs are soluble periplasmic enzymes. By means of a combined experimental and computational approach, we show that NDM-1 interacts with the surface of bacterial membranes in a stable, defined conformation, in which the active site is not occluded by the bilayer. Although the lipidation is required for a long-lasting interaction, the globular domain of NDM-1 is tuned to interact specifically with the outer bacterial membrane. In contrast, this affinity is not observed for VIM-2, a natively soluble MBL. Finally, we identify key residues involved in the membrane interaction with NDM-1, which constitute potential targets for developing therapeutic strategies able to combat resistance granted by this enzyme.

MEPSAnd: minimum energy path surface analysis over n-dimensional surfaces

SUMMARY

n-dimensional energy surfaces are becoming computationally accessible, yet interpreting their information is not straightforward. We present minimum energy path surface analysis over n-dimensional surfaces (MEPSAnd), an open source GUI-based program that natively calculates minimum energy paths across energy surfaces of any number of dimensions. Among other features, MEPSAnd can compute the path through lowest barriers and automatically provide a set of alternative paths. MEPSAnd offers distinct plotting solutions as well as direct python scripting.

AVAILABILITY AND IMPLEMENTATION

MEPSAnd is freely available (under GPLv3 license) at: http://bioweb.cbm.uam.es/software/MEPSAnd/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Molecular Basis of Class A β-Lactamase Inhibition by Relebactam

β-Lactamase production is the major β-lactam resistance mechanism in Gram-negative bacteria. β-Lactamase inhibitors (BLIs) efficacious against serine β-lactamase (SBL) producers, especially strains carrying the widely disseminated class A enzymes, are required. Relebactam, a diazabicyclooctane (DBO) BLI, is in phase 3 clinical trials in combination with imipenem for the treatment of infections by multidrug-resistant Enterobacteriaceae We show that relebactam inhibits five clinically important class A SBLs (despite their differing spectra of activity), representing both chromosomal and plasmid-borne enzymes, i.e., the extended-spectrum β-lactamases L2 (inhibition constant 3 μM) and CTX-M-15 (21 μM) and the carbapenemases KPC-2, -3, and -4 (1 to 5 μM). Against purified class A SBLs, relebactam is an inferior inhibitor compared with the clinically approved DBO avibactam (9- to 120-fold differences in half maximal inhibitory concentration [IC₅₀]). MIC assays indicate relebactam potentiates β-lactam (imipenem) activity against KPC-producing Klebsiella pneumoniae, with similar potency to avibactam (with ceftazidime). Relebactam is less effective than avibactam in combination with aztreonam against Stenotrophomonas maltophilia K279a. X-ray crystal structures of relebactam bound to CTX-M-15, L2, KPC-2, KPC-3, and KPC-4 reveal its C2-linked piperidine ring can sterically clash with Asn104 (CTX-M-15) or His/Trp105 (L2 and KPCs), rationalizing its poorer inhibition activity than that of avibactam, which has a smaller C2 carboxyamide group. Mass spectrometry and crystallographic data show slow, pH-dependent relebactam desulfation by KPC-2, -3, and -4. This comprehensive comparison of relebactam binding across five clinically important class A SBLs will inform the design of future DBOs, with the aim of improving clinical efficacy of BLI-β-lactam combinations.

An Efficient Computational Assay for β-Lactam Antibiotic Breakdown by Class A β-Lactamases

Class A β-lactamases cause clinically relevant resistance to β-lactam antibiotics. Carbapenem degradation is a particular concern. We present an efficient QM/MM molecular simulation protocol that accurately predicts the activity of β-lactamases against carbapenems. Simulations take less than 24 CPU hours, a greater than 99% reduction, and do not require fitting against experimental data or significant parametrization. This computational assay also reveals mechanistic details of β-lactam breakdown and should assist in evaluating emerging β-lactamase variants and developing new antibiotics.

Quantum Mechanics/Molecular Mechanics (QM/MM) Calculations Support a Concerted Reaction Mechanism for the Zika Virus NS2B/NS3 Serine Protease with Its Substrate

Zika virus (ZIKV) is mainly transmitted to humans by Aedes species mosquitoes and is associated with serious pathological disorders including microcephaly in newborns and Guillain-Barré syndrome in adults. Currently, there is no vaccine or anti-ZIKV drug available for preventing or controlling ZIKV infection. An attractive drug target for ZIKV treatment is a two-compartment (NS2B/NS3) serine protease that processes viral polyprotein during infection. Here, conventional molecular dynamics simulations of the ZIKV protease in complex with peptide substrate (TGKRS) sequence at the C-terminus of NS2B show that the substrate is in the active conformation for the cleavage reaction by ZIKV protease. Hybrid quantum mechanics/molecular mechanics (QM/MM) umbrella sampling simulations (PM6/ff14SB) of acylation results reveal that proton transfer from S135 to H51 and nucleophilic attack on the substrate by S135 are concerted. The rate-limiting step involves the formation of a tetrahedral intermediate. In addition, the single-point energy QM/MM calculations, precisely at the level of coupled cluster theory (LCCSD(T)/(aug)-cc-pVTZ), were performed to correct the potential energy profiles for the first step of the acylation process. The average computed activation barrier at this level of theory is 16.3 kcal mol^-1. Therefore, the computational approaches presented here are helpful for further designing of NS2B/NS3 inhibitors based on transition-state analogues.

Predicting allostery and microbial drug resistance with molecular simulations

Beta-lactamase enzymes mediate the most common forms of gram-negative antibiotic resistance affecting clinical treatment. They also constitute an excellent model system for the difficult problem of understanding how allosteric mutations can augment catalytic activity of already-competent enzymes. Multiple allosteric mutations have been identified that alter catalytic activity or drug-resistance spectrum in class A beta lactamases, but predicting these in advance continues to be challenging. Here, we review computational techniques based on structure and/or molecular simulation to predict such mutations. Structure-based techniques have been particularly helpful in developing graph algorithms for analyzing critical residues in beta-lactamase function, while classical molecular simulation has recently shown the ability to prospectively predict allosteric mutations increasing beta-lactamase activity and drug resistance. These will ultimately achieve the greatest power when combined with simulation methods that model reactive chemistry to calculate activation free energies directly.