Predicting enzymatic reactivity: from theory to design

AlphaFold2 and Deep Learning for Elucidating Enzyme Conformational Flexibility and Its Application for Design

The recent success of AlphaFold2 (AF2) and other deep learning (DL) tools in accurately predicting the folded three-dimensional (3D) structure of proteins and enzymes has revolutionized the structural biology and protein design fields. The 3D structure indeed reveals key information on the arrangement of the catalytic machinery of enzymes and which structural elements gate the active site pocket. However, comprehending enzymatic activity requires a detailed knowledge of the chemical steps involved along the catalytic cycle and the exploration of the multiple thermally accessible conformations that enzymes adopt when in solution. In this Perspective, some of the recent studies showing the potential of AF2 in elucidating the conformational landscape of enzymes are provided. Selected examples of the key developments of AF2-based and DL methods for protein design are discussed, as well as a few enzyme design cases. These studies show the potential of AF2 and DL for allowing the routine computational design of efficient enzymes.

Multiscale Simulations of the Covalent Inhibition of the SARS-CoV-2 Main Protease: Four Compounds and Three Reaction Mechanisms

We report the results of computational modeling of the reactions of the SARS-CoV-2 main protease (MPro) with four potential covalent inhibitors. Two of them, carmofur and nirmatrelvir, have shown experimentally the ability to inhibit MPro. Two other compounds, X77A and X77C, were designed computationally in this work. They were derived from the structure of X77, a non-covalent inhibitor forming a tight surface complex with MPro. We modified the X77 structure by introducing warheads capable of reacting with the catalytic cysteine residue in the MPro active site. The reaction mechanisms of the four molecules with MPro were investigated by quantum mechanics/molecular mechanics (QM/MM) simulations. The results show that all four compounds form covalent adducts with the catalytic cysteine Cys 145 of MPro. From the chemical perspective, the reactions of these four molecules with MPro follow three distinct mechanisms. The reactions are initiated by a nucleophilic attack of the thiolate group of the deprotonated cysteine residue from the catalytic dyad Cys145-His41 of MPro. In the case of carmofur and X77A, the covalent binding of the thiolate to the ligand is accompanied by the formation of the fluoro-uracil leaving group. The reaction with X77C follows the nucleophilic aromatic substitution SNAr mechanism. The reaction of MPro with nirmatrelvir (which has a reactive nitrile group) leads to the formation of a covalent thioimidate adduct with the thiolate of the Cys145 residue in the enzyme active site. Our results contribute to the ongoing search for efficient inhibitors of the SARS-CoV-2 enzymes.

The Quantum Chemical Cluster Approach in Biocatalysis

ConspectusThe quantum chemical cluster approach has been used for modeling enzyme active sites and reaction mechanisms for more than two decades. In this methodology, a relatively small part of the enzyme around the active site is selected as a model, and quantum chemical methods, typically density functional theory, are used to calculate energies and other properties. The surrounding enzyme is modeled using implicit solvation and atom fixing techniques. Over the years, a large number of enzyme mechanisms have been solved using this method. The models have gradually become larger as a result of the faster computers, and new kinds of questions have been addressed. In this Account, we review how the cluster approach can be utilized in the field of biocatalysis. Examples from our recent work are chosen to illustrate various aspects of the methodology. The use of the cluster model to explore substrate binding is discussed first. It is emphasized that a comprehensive search is necessary in order to identify the lowest-energy binding mode(s). It is also argued that the best binding mode might not be the productive one, and the full reactions for a number of enzyme-substrate complexes have therefore to be considered to find the lowest-energy reaction pathway. Next, examples are given of how the cluster approach can help in the elucidation of detailed reaction mechanisms of biocatalytically interesting enzymes, and how this knowledge can be exploited to develop enzymes with new functions or to understand the reasons for lack of activity toward non-natural substrates. The enzymes discussed in this context are phenolic acid decarboxylase and metal-dependent decarboxylases from the amidohydrolase superfamily. Next, the application of the cluster approach in the investigation of enzymatic enantioselectivity is discussed. The reaction of strictosidine synthase is selected as a case study, where the cluster calculations could reproduce and rationalize the selectivities of both the natural and non-natural substrates. Finally, we discuss how the cluster approach can be used to guide the rational design of enzyme variants with improved activity and selectivity. Acyl transferase from Mycobacterium smegmatis serves as an instructive example here, for which the calculations could pinpoint the factors controlling the reaction specificity and enantioselectivity. The cases discussed in this Account highlight thus the value of the cluster approach as a tool in biocatalysis. It complements experiments and other computational techniques in this field and provides insights that can be used to understand existing enzymes and to develop new variants with tailored properties.

Predicting Effects of Site-Directed Mutagenesis on Enzyme Kinetics by QM/MM and QM Calculations: A Case of Glutamate Carboxypeptidase II

Quantum and molecular mechanics (QM/MM) and QM-only (cluster model) modeling techniques represent the two workhorses in mechanistic understanding of enzyme catalysis. One of the stringent tests for QM/MM and/or QM approaches is to provide quantitative answers to real-world biochemical questions, such as the effect of single-point mutations on enzyme kinetics. This translates into predicting the relative activation energies to 1-2 kcal·mol^-1 accuracy; such predictions can be used for the rational design of novel enzyme variants with desired/improved characteristics. Herein, we employ glutamate carboxypeptidase II (GCPII), a dizinc metallopeptidase, also known as the prostate specific membrane antigen, as a model system. The structure and activity of this major cancer antigen have been thoroughly studied, both experimentally and computationally, which makes it an ideal model system for method development. Its reaction mechanism is quite well understood: the reaction coordinate comprises a "tetrahedral intermediate" and two transition states and experimental activation Gibbs free energy of ∼17.5 kcal·mol^-1 can be inferred for the known k_cat ≈ 1 s^-1. We correlate experimental kinetic data (including the E424H variant, newly characterized in this work) for various GCPII mutants (k_cat = 8.6 × 10^-5 s^-1 to 2.7 s^-1) with the energy profiles calculated by QM/MM and QM-only (cluster model) approaches. We show that the near-quantitative agreement between the experimental values and the calculated activation energies (ΔH^⧧) can be obtained and recommend the combination of the two protocols: QM/MM optimized structures and cluster model (QM) energetics. The trend in relative activation energies is mostly independent of the QM method (DFT functional) used. Last but not least, a satisfactory correlation between experimental and theoretical data allows us to provide qualitative and fairly simple explanations of the observed kinetic effects which are thus based on a rigorous footing.

In Silico Identification and Experimental Validation of Distal Activity-Enhancing Mutations in Tryptophan Synthase

Allostery is a central mechanism for the regulation of multi-enzyme complexes. The mechanistic basis that drives allosteric regulation is poorly understood but harbors key information for enzyme engineering. In the present study, we focus on the tryptophan synthase complex that is composed of TrpA and TrpB subunits, which allosterically activate each other. Specifically, we develop a rational approach for identifying key amino acid residues of TrpB distal from the active site. Those residues are predicted to be crucial for shifting the inefficient conformational ensemble of the isolated TrpB to a productive ensemble through intra-subunit allosteric effects. The experimental validation of the conformationally driven TrpB design demonstrates its superior stand-alone activity in the absence of TrpA, comparable to those enhancements obtained after multiple rounds of experimental laboratory evolution. Our work evidences that the current challenge of distal active site prediction for enhanced function in computational enzyme design has become within reach.

The Effects of the Metal Ion Substitution into the Active Site of Metalloenzymes: A Theoretical Insight on Some Selected Cases

A large number of enzymes need a metal ion to express their catalytic activity. Among the different roles that metal ions can play in the catalytic event, the most common are their ability to orient the substrate correctly for the reaction, to exchange electrons in redox reactions, to stabilize negative charges. In many reactions catalyzed by metal ions, they behave like the proton, essentially as Lewis acids but are often more effective than the proton because they can be present at high concentrations at neutral pH. In an attempt to adapt to drastic environmental conditions, enzymes can take advantage of the presence of many metal species in addition to those defined as native and still be active. In fact, today we know enzymes that contain essential bulk, trace, and ultra-trace elements. In this work, we report theoretical results obtained for three different enzymes each of which contains different metal ions, trying to highlight any differences in their working mechanism as a function of the replacement of the metal center at the active site.

Scalable molecular dynamics on CPU and GPU architectures with NAMD

NAMDis a molecular dynamics program designed for high-performance simulations of very large biological objects on CPU- and GPU-based architectures. NAMD offers scalable performance on petascale parallel supercomputers consisting of hundreds of thousands of cores, as well as on inexpensive commodity clusters commonly found in academic environments. It is written in C++ and leans on Charm++ parallel objects for optimal performance on low-latency architectures. NAMD is a versatile, multipurpose code that gathers state-of-the-art algorithms to carry out simulations in apt thermodynamic ensembles, using the widely popular CHARMM, AMBER, OPLS, and GROMOS biomolecular force fields. Here, we review the main features of NAMD that allow both equilibrium and enhanced-sampling molecular dynamics simulations with numerical efficiency. We describe the underlying concepts utilized by NAMD and their implementation, most notably for handling long-range electrostatics; controlling the temperature, pressure, and pH; applying external potentials on tailored grids; leveraging massively parallel resources in multiple-copy simulations; and hybrid quantum-mechanical/molecular-mechanical descriptions. We detail the variety of options offered by NAMD for enhanced-sampling simulations aimed at determining free-energy differences of either alchemical or geometrical transformations and outline their applicability to specific problems. Last, we discuss the roadmap for the development of NAMD and our current efforts toward achieving optimal performance on GPU-based architectures, for pushing back the limitations that have prevented biologically realistic billion-atom objects to be fruitfully simulated, and for making large-scale simulations less expensive and easier to set up, run, and analyze. NAMD is distributed free of charge with its source code at www.ks.uiuc.edu.

Bottom-Up Nonempirical Approach To Reducing Search Space in Enzyme Design Guided by Catalytic Fields

Currently developed protocols of theozyme design still lead to biocatalysts with much lower catalytic activity than enzymes existing in nature, and, so far, the only avenue of improvement was the in vitro laboratory-directed evolution (LDE) experiments. In this paper, we propose a different strategy based on "reversed" methodology of mutation prediction. Instead of common "top-down" approach, requiring numerous assumptions and vast computational effort, we argue for a "bottom-up" approach that is based on the catalytic fields derived directly from transition state and reactant complex wave functions. This enables direct one-step determination of the general quantitative angular characteristics of optimal catalytic site and simultaneously encompasses both the transition-state stabilization (TSS) and ground-state destabilization (GSD) effects. We further extend the static catalytic field approach by introducing a library of atomic multipoles for amino acid side-chain rotamers, which, together with the catalytic field, allow one to determine the optimal side-chain orientations of charged amino acids constituting the elusive structure of a preorganized catalytic environment. Obtained qualitative agreement with experimental LDE data for Kemp eliminase KE07 mutants validates the proposed procedure, yielding, in addition, a detailed insight into possible dynamic and epistatic effects.

Design and SAR Analysis of Covalent Inhibitors Driven by Hybrid QM/MM Simulations

Quantum mechanics/molecular mechanics (QM/MM) hybrid technique is emerging as a reliable computational method to investigate and characterize chemical reactions occurring in enzymes. From a drug discovery perspective, a thorough understanding of enzyme catalysis appears pivotal to assist the design of inhibitors able to covalently bind one of the residues belonging to the enzyme catalytic machinery. Thanks to the current advances in computer power, and the availability of more efficient algorithms for QM-based simulations, the use of QM/MM methodology is becoming a viable option in the field of covalent inhibitor design. In the present review, we summarized our experience in the field of QM/MM simulations applied to drug design problems which involved the optimization of agents working on two well-known drug targets, namely fatty acid amide hydrolase (FAAH) and epidermal growth factor receptor (EGFR). In this context, QM/MM simulations gave valuable information in terms of geometry (i.e., of transition states and metastable intermediates) and reaction energetics that allowed to correctly predict inhibitor binding orientation and substituent effect on enzyme inhibition. What is more, enzyme reaction modelling with QM/MM provided insights that were translated into the synthesis of new covalent inhibitor featured by a unique combination of intrinsic reactivity, on-target activity, and selectivity.

Catalytic Hydrolysis Mechanism of Cocaine by Human Carboxylesterase 1: An Orthoester Intermediate Slows Down the Reaction

Human carboxylesterase 1 (hCES1) is a major carboxylesterase in the human body and plays important roles in the metabolism of a wide variety of substances, including lipids and drugs, and therefore is attracting more and more attention from areas including lipid metabolism, pharmacokinetics, drug–drug interactions, and prodrug activation. In this work, we studied the catalytic hydrolysis mechanism of hCES1 by the quantum mechanics computation method, using cocaine as a model substrate. Our results support the four-step theory of the esterase catalytic hydrolysis mechanism, in which both the acylation stage and the deacylation stage include two transition states and a tetrahedral intermediate. The roles and cooperation of the catalytic triad, S221, H468, and E354, were also analyzed in this study. Moreover, orthoester intermediates were found in hCES1-catalyzed cocaine hydrolysis reaction, which significantly elevate the free energy barrier and slow down the reaction. Based on this finding, we propose that hCES1 substrates with β-aminocarboxylester structure might form orthoester intermediates in hCES1-catalyzed hydrolysis, and therefore prolong their in vivo half-life. Thus, this study helps to clarify the catalytic mechanism of hCES1 and elucidates important details of its catalytic process, and furthermore, provides important insights into the metabolism of hCES1 substrates and drug designing.

Computational Understanding of the Selectivities in Metalloenzymes

Metalloenzymes catalyze many different types of biological reactions with high efficiency and remarkable selectivity. The quantum chemical cluster approach and the combined quantum mechanics/molecular mechanics methods have proven very successful in the elucidation of the reaction mechanism and rationalization of selectivities in enzymes. In this review, recent progress in the computational understanding of various selectivities including chemoselectivity, regioselectivity, and stereoselectivity, in metalloenzymes, is discussed.

Recent Trends in Quantum Chemical Modeling of Enzymatic Reactions

The quantum chemical cluster approach is a powerful method for investigating enzymatic reactions. Over the past two decades, a large number of highly diverse systems have been studied and a great wealth of mechanistic insight has been developed using this technique. This Perspective reviews the current status of the methodology. The latest technical developments are highlighted, and challenges are discussed. Some recent applications are presented to illustrate the capabilities and progress of this approach, and likely future directions are outlined.

Rapid Estimation of Catalytic Efficiency by Cumulative Atomic Multipole Moments: Application to Ketosteroid Isomerase Mutants

We propose a simple atomic multipole electrostatic model to rapidly evaluate the effects of mutation on enzyme activity and test its performance on wild-type and mutant ketosteroid isomerase. The predictions of our atomic multipole model are similar to those obtained with symmetry-adapted perturbation theory at a fraction of the computational cost. We further show that this approach is relatively insensitive to the precise amino acid side chain conformation in mutants and may thus be useful in computational enzyme (re)design.

Protein Conformational Landscapes and Catalysis. Influence of Active Site Conformations in the Reaction Catalyzed by L-Lactate Dehydrogenase

In the last decade L-Lactate Dehydrogenase (LDH) has become an extremely useful marker in both clinical diagnosis and in monitoring the course of many human diseases. It has been assumed from the 80s that the full catalytic process of LDH starts with the binding of the cofactor and the substrate followed by the enclosure of the active site by a mobile loop of the protein before the reaction to take place. In this paper we show that the chemical step of the LDH catalyzed reaction can proceed within the open loop conformation, and the different reactivity of the different protein conformations would be in agreement with the broad range of rate constants measured in single molecule spectrometry studies. Starting from a recently solved X-ray diffraction structure that presented an open loop conformation in two of the four chains of the tetramer, QM/MM free energy surfaces have been obtained at different levels of theory. Depending on the level of theory used to describe the electronic structure, the free energy barrier for the transformation of pyruvate into lactate with the open conformation of the protein varies between 12.9 and 16.3 kcal/mol, after quantizing the vibrations and adding the contributions of recrossing and tunneling effects. These values are very close to the experimentally deduced one (14.2 kcal·mol^-1) and ~2 kcal·mol^-1 smaller than the ones obtained with the closed loop conformer. Calculation of primary KIEs and IR spectra in both protein conformations are also consistent with our hypothesis and in agreement with experimental data. Our calculations suggest that the closure of the active site is mainly required for the inverse process; the oxidation of lactate to pyruvate. According to this hypothesis H4 type LDH enzyme molecules, where it has been propose that lactate is transformed into pyruvate, should have a better ability to close the mobile loop than the M4 type LDH molecules.