Designed Local Electric Fields-Promising Tools for Enzyme Engineering

Modulating phosphate transfer process for promoting phosphorylation activity of acid phosphatase

Klebsiella pneumonia acid phosphatase is widely employed in the large-scale synthesis of nucleotides. It was found that the phosphate acceptance capability of the substrate limited the efficiency of the phosphate transfer process. By reducing steric hindrance and optimizing substrate interaction with the catalytic site, variants of Klebsiella pneumonia acid phosphatase were designed, with the E104G variant showing significantly enhanced hydrolysis activity while maintaining high phosphorylation activity. Crystal structure and quantum mechanics/molecular mechanics analyses of the E104G variant revealed that the mutation promotes substrate binding and lowers the energy barrier. Based on these insights, several mutations were designed, achieving significantly improved conversion rates. By knocking out degradation-related enzymes, the degradation rates of inosinic acid and guanylic acid were successfully controlled. This study provides a structure-based top-down design strategy that effectively enhances enzyme specificity, offering a promising enzyme candidate for large-scale nucleotide synthesis.

Multiobjective Evolutionary Strategy for Improving Semiempirical Hamiltonians in the Study of Enzymatic Reactions at the QM/MM Level of Theory

Quantum mechanics/molecular mechanics (QM/MM) simulations are crucial for understanding enzymatic reactions, but their accuracy depends heavily on the quantum-mechanical method used. Semiempirical methods offer computational efficiency but often struggle with accuracy in complex systems. This work presents a novel multiobjective evolutionary strategy for optimizing semiempirical Hamiltonians, specifically designed to enhance their performance in enzymatic QM/MM simulations while remaining broadly applicable to condensed-phase systems. Our methodology combines automated parameter optimization, targeting ab initio or density functional theory (DFT)-reference potential energy surfaces, atomic charges, and gradients, with comprehensive validation through minimum free energy path (MFEP) calculations. To demonstrate its effectiveness, we applied our approach to improve the GFN2-xTB Hamiltonian using two enzymatic systems that involve hydride transfer reactions where the activation energy barrier is severely underestimated: Crotonyl-CoA carboxylase/reductase (CCR) and dihydrofolate reductase (DHFR). The optimized parameters showed significant improvements in reproducing potential and free energy surfaces, closely matching higher-level DFT calculations. Through an efficient two-stage optimization process, we first developed parameters for CCR using reaction path data, then refined these parameters for DHFR by incorporating a targeted set of additional training geometries. This strategic approach minimized the computational cost while achieving accurate descriptions of both systems, as validated through QM/MM simulations using the Adaptive String Method (ASM). Our method represents an efficient approach for optimizing semiempirical methods to study larger systems and longer time scales, with potential applications in enzymatic reaction mechanism studies, drug design, and enzyme engineering.

Leveraging Quantum Chemistry and Machine Learning for the Design of Low-Valent Transition Metal Catalysts in Nitrogen to Ammonia Conversion

The conversion of N2 to NH3 under ambient conditions is a major goal in sustainable chemistry. Homogeneous catalysts, particularly those employing cyclic(alkyl)(amino)carbene (CAAC) ligands, have demonstrated promise in stabilizing low-valent Fe centers, yet industrial-level turnover numbers (TONs) and frequencies (TOFs) remain unmet. Here, we integrate quantum chemistry, molecular dynamics, and machine learning (ML) to uncover mechanistic features governing nitrogen reduction reaction (NRR) activity and guide catalyst design. Density functional theory (DFT) and ab initio molecular dynamics reveal that [Fe(CAAC)2] leverages redox noninnocent CAAC ligands to stabilize Fe(I) ([FeI(CAAC)2·-]), with strong antiferromagnetic coupling (JFe-CAAC = -1817 cm-1). Flexibility of bulky Dipp groups found to hinder N2 binding, rationalizing experimental observations. The exothermic formation of [(CAAC(H))2Fe] (ΔG = -4.5 kJ/mol) with in situ generated H2 exposure rationalizes the lower TON observed via catalyst deactivation. ML models trained on quantum descriptors such as M-C bond lengths, spin density, and frontier orbital energies identify the M-C distance as a key predictor of reactivity. A composite free energy metric (ΔGtot) encompassing cis-trans isomerization (ΔG10), N2 binding (ΔG20), and the first reduction step (ΔG30) enables ranking of candidate catalysts. Moreover, Ti and V complexes show the lowest ΔGtot (24-60 kJ/mol), while late transition and coinage metals exceed 120 kJ/mol, correlating with lower activity. By providing unprecedented insights into the interplay among ligand design, metal choice, and catalytic efficiency, this work lays a critical foundation for the rational design of homogeneous NRR catalysts, with implications for advancing sustainable ammonia production technologies.

Methods for Theoretical Treatment of Local Fields in Proteins and Enzymes

Electric fields generated by protein scaffolds are crucial in enzymatic catalysis. This review surveys theoretical approaches for detecting, analyzing, and comparing electric fields, electrostatic potentials, and their effects on the charge density within enzyme active sites. Pioneering methods like the empirical valence bond approach rely on evaluating ionic and covalent resonance forms influenced by the field. Strategies employing polarizable force fields also facilitate field detection. The vibrational Stark effect connects computational simulations to experimental Stark spectroscopy, enabling direct comparisons. We highlight how protein dynamics induce fluctuations in local fields, influencing enzyme activity. Recent techniques assess electric fields throughout the active site volume rather than only at specific bonds, and machine learning helps relate these global fields to reactivity. Quantum theory of atoms in molecules captures the entire electron density landscape, providing a chemically intuitive perspective on field-driven catalysis. Overall, these methodologies show protein-generated fields are highly dynamic and heterogeneous, and understanding both aspects is critical for elucidating enzyme mechanisms. This holistic view empowers rational enzyme engineering by tuning electric fields, promising new avenues in drug design, biocatalysis, and industrial applications. Future directions include incorporating electric fields as explicit design targets to enhance catalytic performance and biochemical functionalities.

Beyond symmetric self-assembly and effective molarity: unlocking functional enzyme mimics with robust organic cages

The bespoke environments in enzyme active sites can selectively accelerate chemical reactions by as much as 1019. Macromolecular and supramolecular chemists have been inspired to understand and mimic these accelerations and selectivities for applications in catalysis for sustainable synthesis. Over the past 60+ years, mimicry strategies have evolved with changing interests, understanding, and synthetic advances but, ubiquitously, research has focused on use of a molecular "cavity". The activities of different cavities vary with the subset of features available to a particular cavity type. Unsurprisingly, without synthetic access to mimics able to encompass more/all of the functional features of enzyme active sites, examples of cavity-catalyzed processes demonstrating enzyme-like rate accelerations remain rare. This perspective will briefly highlight some of the key advances in traditional cavity catalysis, by cavity type, in order to contextualize the recent development of robust organic cage catalysts, which can exploit stability, functionality, and reduced symmetry to enable promising catalytic modes.

Long-Range Electrostatics in Serine Proteases: Machine Learning-Driven Reaction Sampling Yields Insights for Enzyme Design

Computational enzyme design is a promising technique for producing novel enzymes for industrial and clinical needs. A key challenge that this technique faces is to consistently achieve the desired activity. Fundamental studies of natural enzymes revealed critical contributions from second-shell - and even more distant - residues to their remarkable efficiency. In particular, such residues organize the internal electrostatic field to promote the reaction. Engineering such fields computationally proved to be a promising strategy, which, however, has some limitations. Charged residues necessarily form specific patterns of local interactions that may be exploited for structural integrity. As a result, it is impossible to probe the electrostatic field alone by substituting amino acids. We hypothesize that an approach that isolates the influences of residues' charges from other influences could yield deeper insights. We use molecular modeling with AI-enhanced QM/MM reaction sampling to implement such an approach and apply it to a model serine protease subtilisin. We find that the negative charge 8 Å away from the catalytic site is crucial to achieving the enzyme's catalytic efficiency, contributing more than 2 kcal/mol to lowering the barrier. In contrast, a positive charge from the second-closest charged residue opposes the efficiency of the reaction by raising the barrier by 0.8 kcal/mol. This result invites discussion into the role of this residue and trade-offs that might have taken place in the evolution of such enzymes. Our approach is transferable and can help investigate the evolution of electrostatic preorganization in other enzymes. We believe that the study and engineering of electrostatic fields in enzymes is a promising direction to advance both fundamental and applied enzymology and lead to the design of new powerful biocatalysts.

Mechanistic Perspective on C-N and C-S Bond Construction Catalyzed by Cytochrome P450 Enzymes

Cytochrome P450 enzymes catalyze a large number of oxidative transformations that are responsible for natural product synthesis. Recent studies have revealed their unique ability to catalyze the formation of C-N and C-S bonds, broadening their biosynthetic applications. However, the enzymatic mechanisms behind these reactions are still unclear. This review focuses on theoretical insights into the mechanisms of P450-catalyzed C-N and C-S bond formation. The key roles of the conformational dynamics of substrate radicals, influenced by the enzyme environment, in modulating selectivity and reactivity are highlighted. Understanding these reaction mechanisms offers valuable guidance for P450 enzyme engineering and the design of biosynthetic applications.

Electric-Field Catalysis on Carbon Nanotubes in Electromicrofluidic Reactors: Monoterpene Cyclizations

The control over the movement of electrons during chemical reactions with oriented external electric fields (OEEFs) has been predicted to offer a general approach to catalysis. Recently, we suggested that many problems to realize electric-field catalysis in practice under scalable bulk conditions could possibly be solved on multiwalled carbon nanotubes in electromicrofluidic reactors. Here, we selected monoterpene cyclizations to assess the scope of our system in organic synthesis. We report that electric-field catalysis can function by stabilizing both anionic and cationic transition states, depending on the orientation of the applied field. Moreover, electric-field catalysis can promote reactions which are barely accessible by general Brønsted and Lewis acids and field-free anion-π and cation-π interactions, and drive chemoselectivity toward intrinsically disfavored products without the need for pyrene interfacers attached to the substrate to prolong binding to the carbon nanotubes. Finally, interfacing with chiral organocatalysts is explored and evidence against contributions from redox chemistry is provided.

Electric fields imbue enzyme reactivity by aligning active site fragment orbitals

It is broadly recognized that intramolecular electric fields, produced by the protein scaffold and acting on the active site, facilitate enzymatic catalysis. This field effect can be described by several theoretical models, each of which is intuitive to varying degrees. In this contribution, we show that a fundamental effect of electric fields is to generate electrostatic potentials that facilitate the energetic alignment of reactant frontier orbitals. We apply this model to demystify the impact of electric fields on high-valent iron-oxo heme proteins: catalases, peroxidases, and peroxygenases/monooxygenases. Specifically, we show that this model easily accounts for the observed field-induced changes to the spin distribution within peroxidase active sites and explains the transition between epoxidation and hydroxylation pathways seen in Cytochrome P450 active site models. Thus, for the intuitive interpretation of the chemical effect of the field, the strategy involves analyzing the response of the orbitals of active site fragments, and their energetic alignment. We note that the energy difference between fragment orbitals involved in charge redistribution acts as a measure for the chemical hardness/softness of the reactive complex. This measure, and its sensitivity to electric fields, offers a single parameter model from which to quantitatively assess the effects of electric fields on reactivity and selectivity. Thus, the model provides an additional perspective to describe electrostatic preorganization and offers ways for its manipulation.

Theoretical Insight into the Fluorescence Spectral Tuning Mechanism: A Case Study of Flavin-Dependent Bacterial Luciferase

Bioluminescence of bacteria is widely applied in biological imaging, environmental toxicant detection, and many other situations. Understanding the spectral tuning mechanism not only helps explain the diversity of colors observed in nature but also provides principles for bioengineering new color variants for practical applications. In this study, time-dependent density functional theory (TD-DFT) and quantum mechanics and molecular mechanics (QM/MM) calculations have been employed to understand the fluorescence spectral tuning mechanism of bacterial luciferase with a focus on the electrostatic effect. The spectrum can be tuned by both a homogeneous dielectric environment and oriented external electric fields (OEEFs). Increasing the solvent polarity leads to a redshift of the fluorescence emission maximum, λF, accompanied by a substantial increase in density. In contrast, applying an OEEF along the long axis of the isoalloxazine ring (X-axis) leads to a significant red- or blue-shift in λF, depending on the direction of the OEEF, yet with much smaller changes in intensity. The effect of polar solvents is directionless, and the red-shifts can be attributed to the larger dipole moment of the S1 state compared with that of the S0 state. However, the effect of OEEFs directly correlates with the difference dipole moment between the S1 and S0 states, which is directional and is determined by the charge redistribution upon deexcitation. Moreover, the electrostatic effect of bacterial luciferase is in line with the presence of an internal electric field (IEF) pointing in the negative X direction. Finally, the key residues that contribute to this IEF and strategies for modulating the spectrum through site-directed point mutations are discussed.

Enhanced Selectivity in Microdroplet-Mediated Enzyme Catalysis

Natural enzymes with enhanced catalytic activity and selectivity have long been studied by tuning the microenvironment around the active site, but how to modulate the active-site electric field in a simple fashion remains challenging. Here, we demonstrate that microdroplets as a simple yet versatile reactor can enhance the electric field at the active site of an enzyme. By using horseradish peroxidase as a model, improved selectivity in microdroplet-mediated enzyme catalysis can be obtained. Quantum mechanical/molecular dynamics calculations and vibrational Stark spectroscopy reveal that the electric field at the microdroplet interface can influence the electrostatic preorganization and orientation of the enzyme to enhance its internal electric field. As a result, the free energies of the substrate and heme can be tuned by the internal electric field, thereby changing its catalytic reaction pathway for a classical substrate, 3,3',5,5'-tetramethylbenzidine, and enabling selective C-N additions for specific substrates. This finding provides a green, simple, and effective way to modulate enzyme-catalyzed reactions and holds promise for a broad spectrum of biosensing and biosynthesis applications.

My Vision of Electric-Field-Aided Chemistry in 2050

This manuscript outlines my outlook on the development of electric-field (EF)-mediated-chemistry and the vision of its state by 2050. I discuss applications of oriented-external electric-fields (OEEFs) on chemical reactions and proceed with relevant experimental verifications. Subsequently, the Perspective outlines other ways of generating EFs, e.g., by use of pH-switchable charges, ionic additives, water droplets, and so on. A special section summarizes conceptual principles for understanding and predicting OEEF effects, e.g., the "reaction-axis rule", the capability of OEEFs to act as tweezers that orient reactants and accelerate their reaction, etc. Finally, I discuss applications of OEEFs in continuous-flow setups, which may, in principle, scale-up to molar concentrations. The Perspective ends with the vision that by 2050, OEEF usage will change chemical education, if not also the art of making new molecules.

Substrate Conformational Switch Enables the Stereoselective Dimerization in P450 NascB: Insights from Molecular Dynamics Simulations and Quantum Mechanical/Molecular Mechanical Calculations

P450 NascB catalyzes the coupling of cyclo-(l-tryptophan-l-proline) (1) to generate (-)-naseseazine C (2) through intramolecular C-N bond formation and intermolecular C-C coupling. A thorough understanding of its catalytic mechanism is crucial for the engineering or design of P450-catalyzed C-N dimerization reactions. By employing MD simulations, QM/MM calculations, and enhanced sampling, we assessed various mechanisms from recent works. Our study demonstrates that the most favorable pathway entails the transfer of a hydrogen atom from N7-H to Cpd I. Subsequently, there is a conformational change in the substrate radical, shifting it from the Re-face to the Si-face of N7 in Substrate 1. The Si-face conformation of Substrate 1 is stabilized by the protein environment and the π-π stacking interaction between the indole ring and heme porphyrin. The subsequent intermolecular C3-C6' bond formation between Substrate 1 radical and Substrate 2 occurs via a radical attack mechanism. The conformational switch of the Substrate 1 radical not only lowers the barrier of the intermolecular C3-C6' bond formation but also yields the correct stereoselectivity observed in experiments. In addition, we evaluated the reactivity of the ferric-superoxide species, showing it is not reactive enough to initiate the hydrogen atom abstraction from the indole NH group of the substrate. Our simulation provides a comprehensive mechanistic insight into how the P450 enzyme precisely controls both the intramolecular C-N cyclization and intermolecular C-C coupling. The current findings align with the available experimental data, emphasizing the pivotal role of substrate dynamics in governing P450 catalysis.

Electrostatics as a Guiding Principle in Understanding and Designing Enzymes

Enzyme design faces challenges related to the implementation of the basic principles that govern the catalytic activity in natural enzymes. In this work, we revisit basic electrostatic concepts that have been shown to explain the origin of enzymatic efficiency like preorganization and reorganization. Using magnitudes such as the electrostatic potential and the electric field generated by the protein, we explain how these concepts work in different enzymes and how they can be used to rationalize the consequences of point mutations. We also discuss examples of protein design in which electrostatic effects have been implemented. For the near future, molecular simulations, coupled with the use of machine learning methods, can be used to implement electrostatics as a guiding principle for enzyme designs.