β-Lactamases Evolve against Antibiotics by Acquiring Large Active-Site Electric Fields

Machine-Learning Prediction of Protein Function from the Portrait of Its Intramolecular Electric Field

We introduce a machine learning framework designed to predict enzyme functionality directly from the heterogeneous electric fields inherent to protein active sites. We apply this method to a curated data set of heme-iron oxidoreductases, spanning three enzyme classes: monooxygenases, peroxidases, and catalases. Conventional analysis, focused on simplistic, point electric fields along the Fe-O bond, is shown to be inadequate for accurate activity prediction. Our model demonstrates that the enzyme's heterogeneous 3-D electric field, alone, can accurately predict its function, without relying on additional protein-specific information. Through feature selection, we uncover key electric field components that not only validate previous studies but also underscore the crucial role of multiple components beyond the traditionally emphasized electric field along the Fe-O bond in heme enzymes. Furthermore, by integrating protein dynamics, principal component analysis, clustering, and QM/MM calculations, we reveal that while dynamic complexities in protein structures can obscure predictions, the model still retains its accuracy. This research significantly advances our understanding of how protein scaffolds possess signature electric fields tailored to their functions at the active site. Moreover, it presents a novel electrostatics-based tool to harness these signature electric fields for predicting enzyme function.

The Evolution of the Acylation Mechanism in β-Lactamase and Rapid Protein Dynamics

β-Lactamases are a class of well-studied enzymes that are known to have existed since billions of years ago, starting as a defense mechanism to stave off competitors and are now enzymes responsible for antibiotic resistance. Using ancestral sequence reconstruction, it is possible to study the crystal structure of a laboratory resurrected 2-3 billion year-old β-lactamase. Comparing the ancestral enzyme to its modern counterpart, a TEM-1 β-lactamase, the structural changes are minor, and it is probable that dynamic effects play an important role in the evolution of function. We used molecular dynamics simulations and employed transition path sampling methods to identify the presence of rate-enhancing dynamics at the femtosecond level in both systems, found that these fast motions are more efficiently coordinated in the modern enzyme, and examined how specific dynamics can pinpoint evolutionary effects that are essential for improving enzymatic catalysis.

Carbapenem-induced β-lactamase-isoform expression trends in Acinetobacter baumannii

Carbapenem-resistant Acinetobacter baumannii (CRAb) is an urgent bacterial threat to public health, with only a few treatment options and a >50% fatality rate. Although several resistance mechanisms are understood, the appearance of these mutations is generally considered stochastic. Recent reports have, however, begun to challenge this assumption. Here, we demonstrate that independent samples of Ab, exposed to different carbapenems with escalating concentrations, show concentration- and carbapenem-dependent trends in β-lactamase-isoform expression. This result, based on the isoforms identified through label-free-quantification LC-MS/MS measurements of cell-free, gel-separated β-lactamases, suggests that the appearance of antibiotic resistance may be somewhat non-stochastic. Specifically, several minor AmpC/ADC β-lactamase-isoforms were found to exhibit both dose- and carbapenem-dependent expression, suggesting the possibility of non-stochastic mutations. Additionally, these also have high sequence similarity to major expressed isoforms, indicating a potential path over which resistance occurred in independent samples. Antibiotic resistance maybe somewhat antibiotic-directed by a hitherto unknown mechanism and further investigation may lead to new strategies for mitigating antibiotic resistance.

Teaser

The emergence of antibiotic-resistant β-lactamase proteins from mutations may exhibit patterns based on specific antibiotics.

Critical Evaluation of Polarizable and Nonpolarizable Force Fields for Proteins Using Experimentally Derived Nitrile Electric Fields

Molecular dynamics (MD) simulations are frequently carried out for proteins to investigate the role of electrostatics in their biological function. The choice of force field (FF) can significantly alter the MD results, as the simulated local electrostatic interactions lack benchmarking in the absence of appropriate experimental methods. We recently reported that the transition dipole moment (TDM) of the popular nitrile vibrational probe varies linearly with the environmental electric field, overcoming well-known hydrogen bonding (H-bonding) issues for the nitrile frequency and, thus, enabling the unambiguous measurement of electric fields in proteins (J. Am. Chem. Soc. 2022, 144 (17), 7562-7567). Herein, we utilize this new strategy to enable comparisons of experimental and simulated electric fields in protein environments. Specifically, previously determined TDM electric fields exerted onto nitrile-containing o-cyanophenylalanine residues in photoactive yellow protein are compared with MD electric fields from the fixed-charge AMBER FF and the polarizable AMOEBA FF. We observe that the electric field distributions for H-bonding nitriles are substantially affected by the choice of FF. As such, AMBER underestimates electric fields for nitriles experiencing moderate field strengths; in contrast, AMOEBA robustly recapitulates the TDM electric fields. The FF dependence of the electric fields can be partly explained by the presence of additional negative charge density along the nitrile bond axis in AMOEBA, which is due to the inclusion of higher-order multipole parameters; this, in turn, begets more head-on nitrile H-bonds. We conclude by discussing the implications of the FF dependence for the simulation of nitriles and proteins in general.

Key interaction networks: Identifying evolutionarily conserved non-covalent interaction networks across protein families

Protein structure (and thus function) is dictated by non-covalent interaction networks. These can be highly evolutionarily conserved across protein families, the members of which can diverge in sequence and evolutionary history. Here we present KIN, a tool to identify and analyze conserved non-covalent interaction networks across evolutionarily related groups of proteins. KIN is available for download under a GNU General Public License, version 2, from https://www.github.com/kamerlinlab/KIN. KIN can operate on experimentally determined structures, predicted structures, or molecular dynamics trajectories, providing insight into both conserved and missing interactions across evolutionarily related proteins. This provides useful insight both into protein evolution, as well as a tool that can be exploited for protein engineering efforts. As a showcase system, we demonstrate applications of this tool to understanding the evolutionary-relevant conserved interaction networks across the class A β-lactamases.

Designed Local Electric Fields-Promising Tools for Enzyme Engineering

Designing efficient catalysts is one of the ultimate goals of chemists. In this Perspective, we discuss how local electric fields (LEFs) can be exploited to improve the catalytic performance of supramolecular catalysts, such as enzymes. More specifically, this Perspective starts by laying out the fundamentals of how local electric fields affect chemical reactivity and review the computational tools available to study electric fields in various settings. Subsequently, the advances made so far in optimizing enzymatic electric fields through targeted mutations are discussed critically and concisely. The Perspective ends with an outlook on some anticipated evolutions of the field in the near future. Among others, we offer some pointers on how the recent data science/machine learning revolution, engulfing all science disciplines, could potentially provide robust and principled tools to facilitate rapid inference of electric field effects, as well as the translation between optimal electrostatic environments and corresponding chemical modifications.

Direct measurements of biomolecular electrostatics through experiments

Biomolecular electrostatics has been a subject of computational investigations based on 3D structures. This situation is changing because emerging experimental tools allow us to quantitatively investigate biomolecular electrostatics without any use of structure information. Now, electrostatic potentials around biomolecules can directly be measured for many residues simultaneously by nuclear magnetic resonance (NMR) spectroscopy. This NMR method can be used to study electrostatic aspects of various processes, including macromolecular association and liquid-liquid phase separation. Applications to structurally flexible biomolecules such as intrinsically disordered proteins are particularly useful. The new tools also facilitate examination of theoretical models and methods for biomolecular electrostatics.

Evaluating the Transition State Stabilization/Destabilization Effects of the Electric Fields from Scaffold Residues by a QM/MM Approach

The protein scaffolds of enzymes not only provide structural support for the catalytic center but also exert preorganized electric fields for electrostatic catalysis. In recent years, uniform oriented external electric fields (OEEFs) have been widely applied to enzymatic reactions to mimic the electrostatic effects of the environment. However, the electric fields exerted by individual residues in proteins may be quite heterogeneous across the active site, with varying directions and strengths at different positions of the active site. Here, we propose a QM/MM-based approach to evaluate the effects of the electric fields exerted by individual residues in the protein scaffold. In particular, the heterogeneity of the residue electric fields and the effect of the native protein environment can be properly accounted for by this QM/MM approach. A case study of the O-O heterolysis reaction in the catalytic cycle of TyrH shows that (1) for scaffold residues that are relatively far from the active site, the heterogeneity of the residue electric field in the active site is not very significant and the electrostatic stabilization/destabilization due to each residue can be well approximated with the interaction energy between a uniform electric field and the QM region dipole; (2) for scaffold residues near the active site, the residue electric fields can be highly heterogeneous along the breaking O-O bond. In such a case, approximating the residue electric fields as uniform fields may misrepresent the overall electrostatic effect of the residue. The present QM/MM approach can be applied to evaluate the residues' electrostatic impact on enzymatic reactions, which also can be useful in computational optimization of electric fields to boost the enzyme catalysis.