Computational strategies for the design of new enzymatic functions

Noncanonical Amino Acids in Biocatalysis

In recent years, powerful genetic code reprogramming methods have emerged that allow new functional components to be embedded into proteins as noncanonical amino acid (ncAA) side chains. In this review, we will illustrate how the availability of an expanded set of amino acid building blocks has opened a wealth of new opportunities in enzymology and biocatalysis research. Genetic code reprogramming has provided new insights into enzyme mechanisms by allowing introduction of new spectroscopic probes and the targeted replacement of individual atoms or functional groups. NcAAs have also been used to develop engineered biocatalysts with improved activity, selectivity, and stability, as well as enzymes with artificial regulatory elements that are responsive to external stimuli. Perhaps most ambitiously, the combination of genetic code reprogramming and laboratory evolution has given rise to new classes of enzymes that use ncAAs as key catalytic elements. With the framework for developing ncAA-containing biocatalysts now firmly established, we are optimistic that genetic code reprogramming will become a progressively more powerful tool in the armory of enzyme designers and engineers in the coming years.

(De)carboxylation mechanisms of heteroaromatic substrates catalyzed by prenylated FMN-dependent UbiD decarboxylases: An in-silico study

The UbiD enzymes are proposed to catalyze reversible (de)carboxylation reaction of unsaturated carboxylic acids using prenylated flavin mononucleotide (prFMN) as a cofactor. This positions UbiD enzymes as promising candidates for converting CO2 into valuable chemicals. However, their industrial-scale biotransformation is currently constrained by low conversion rates attributed to thermodynamic limitations. To enhance the carboxylation activity of UbiD enzymes, a molecular-level understanding of the (de)carboxylation mechanisms is necessary. In this study, we investigated the reaction mechanisms of heteroaromatic substrates catalyzed by PtHmfF, PaHudA, and AnlnD enzymes using molecular dynamics (MD) simulations and free energy calculations. Our extensive mechanistic study elucidates the mechanisms involved in the formation of the initial prFMN-substrate intermediate. Specifically, we observed nucleophilic attack during decarboxylation, while carboxylation reactions involving furoic acid, pyrrole, and indole tend to favor a 1,3-dipolar cycloaddition mechanism. Furthermore, we identified proton transfer as the rate-limiting step in the carboxylation reaction. In addition, we considered the perspectives of reaction energies and electron transfer to understand the distinct mechanisms underlying decarboxylation and carboxylation. Our calculated free energies are consistent with available experimental kinetics data. Finally, we explored how different rotamers of catalytic residues influence the efficiency of the initial intermediate formation.

From random to rational: improving enzyme design through electric fields, second coordination sphere interactions, and conformational dynamics

Enzymes are versatile and efficient biological catalysts that drive numerous cellular processes, motivating the development of enzyme design approaches to tailor catalysts for diverse applications. In this perspective, we investigate the unique properties of natural, evolved, and designed enzymes, recognizing their strengths and shortcomings. We highlight the challenges and limitations of current enzyme design protocols, with a particular focus on their limited consideration of long-range electrostatic and dynamic effects. We then delve deeper into the impact of the protein environment on enzyme catalysis and explore the roles of preorganized electric fields, second coordination sphere interactions, and protein dynamics for enzyme function. Furthermore, we present several case studies illustrating successful enzyme-design efforts incorporating enzyme strategies mentioned above to achieve improved catalytic properties. Finally, we envision the future of enzyme design research, spotlighting the challenges yet to be overcome and the synergy of intrinsic electric fields, second coordination sphere interactions, and conformational dynamics to push the state-of-the-art boundaries.

Mechanism-Based Redesign of GAP to Activate Oncogenic Ras

Ras GTPases play a crucial role in cell signaling pathways. Mutations of the Ras gene occur in about one third of cancerous cell lines and are often associated with detrimental clinical prognosis. Hot spot residues Gly12, Gly13, and Gln61 cover 97% of oncogenic mutations, which impair the enzymatic activity in Ras. Using QM/MM free energy calculations, we present a two-step mechanism for the GTP hydrolysis catalyzed by the wild-type Ras.GAP complex. We found that the deprotonation of the catalytic water takes place via the Gln61 as a transient Brønsted base. We also determined the reaction profiles for key oncogenic Ras mutants G12D and G12C using QM/MM minimizations, matching the experimentally observed loss of catalytic activity, thereby validating our reaction mechanism. Using the optimized reaction paths, we devised a fast and accurate procedure to design GAP mutants that activate G12D Ras. We replaced GAP residues near the active site and determined the activation barrier for 190 single mutants. We furthermore built a machine learning for ultrafast screening, by fast prediction of the barrier heights, tested both on the single and double mutations. This work demonstrates that fast and accurate screening can be accomplished via QM/MM reaction path optimizations to design protein sequences with increased catalytic activity. Several GAP mutations are predicted to re-enable catalysis in oncogenic G12D, offering a promising avenue to overcome aberrant Ras-driven signal transduction by activating enzymatic activity instead of inhibition. The outlined computational screening protocol is readily applicable for designing ligands and cofactors analogously.

Tuning the Catalytic Activity of Synthetic Enzyme KE15 with DNA

Efficiency improvement of synthetic enzymes through scaffold modifications suffers from limitations in terms of effectiveness, cost, and potential devastating consequences for protein structural stability. Here, we propose an alternative to scaffold modification, within electrostatic preorganization theory, where the enzyme's greater environment is designed to support the evolution of the reaction in the active site. We demonstrate the feasibility of such an approach by placing a (polar) DNA fragment in the surroundings of the Kemp eliminase enzyme KE15 (structure from Houk's group) and computing the resulting change in catalytic activity. We find that the introduction of a DNA fragment magnifies the contribution of protein residues to the stabilization of the transition state, estimated from electric field calculations with polarizable molecular dynamics. Our randomly generated test systems reveal a 2.0 kcal/mol reduction in activation energy, suggesting that even more significant catalytic improvements could be made by optimizing DNA size, sequence, and orientation with respect to the enzyme, validating our approach.

Quantum mechanical study on the activation mechanism of human carbonic anhydrase VII cluster model with bis-histamine schiff bases and bis-spinaceamine derivatives

The activation mechanism of human carbonic anhydrase (hCA) isoform VII, hCA VII, with histamine, histamine bis-Schiff bases and bis-spinaceamine derivatives has been investigated using quantum mechanical calculations. The DFT-D3 method has been employed to calculate in detail the electronic structure and electronic energy of different compounds and complexes throughout the reaction pathway. The model system of hCA VII included the core catalytic center, the Zn²⁺ ion, its three histidine ligands and a hydroxide ion or water molecule coordinated to it. Furthermore, Thr199, Glu106 and the deep water molecule were considered in the model. Five activators of this enzyme, including histamine as standard, in complex with the cluster model of hCA VII were investigated. Thermodynamic functions for the overall reaction and for the complexation between activators and hCA VII were evaluated. Our results demonstrate that the protonatable moiety of these activators participates in proton transfer reactions from the zinc-bound water molecule to the reaction medium, promoting the formation of the catalytically active zinc hydroxide species of the enzyme. The QM analysis revealed that the electrostatic interactions between activators and hCA VII are the driving force of the enzyme-activator complex formation.

Computational Prediction of Binding Affinity for CDK2-ligand Complexes. A Protein Target for Cancer Drug Discovery

BACKGROUND

CDK2 participates in the control of eukaryotic cell-cycle progression. Due to the great interest in CDK2 for drug development and the relative easiness in crystallizing this enzyme, we have over 400 structural studies focused on this protein target. This structural data is the basis for the development of computational models to estimate CDK2-ligand binding affinity.

OBJECTIVE

This work focuses on the recent developments in the application of supervised machine learning modeling to develop scoring functions to predict the binding affinity of CDK2.

METHOD

We employed the structures available at the protein data bank and the ligand information accessed from the BindingDB, Binding MOAD, and PDBbind to evaluate the predictive performance of machine learning techniques combined with physical modeling used to calculate binding affinity. We compared this hybrid methodology with classical scoring functions available in docking programs.

RESULTS

Our comparative analysis of previously published models indicated that a model created using a combination of a mass-spring system and cross-validated Elastic Net to predict the binding affinity of CDK2-inhibitor complexes outperformed classical scoring functions available in AutoDock4 and AutoDock Vina.

CONCLUSION

All studies reviewed here suggest that targeted machine learning models are superior to classical scoring functions to calculate binding affinities. Specifically for CDK2, we see that the combination of physical modeling with supervised machine learning techniques exhibits improved predictive performance to calculate the protein-ligand binding affinity. These results find theoretical support in the application of the concept of scoring function space.

Biomolecular QM/MM Simulations: What Are Some of the "Burning Issues"?

QM/MM simulations have become an indispensable tool in many chemical and biochemical investigations. Considering the tremendous degree of success, including recognition by a 2013 Nobel Prize in Chemistry, are there still "burning challenges" in QM/MM methods, especially for biomolecular systems? In this short Perspective, we discuss several issues that we believe greatly impact the robustness and quantitative applicability of QM/MM simulations to many, if not all, biomolecules. We highlight these issues with observations and relevant advances from recent studies in our group and others in the field. Despite such limited scope, we hope the discussions are of general interest and will stimulate additional developments that help push the field forward in meaningful directions.

Computational design of enzymes for biotechnological applications

Enzymes are the natural catalysts that execute biochemical reactions upholding life. Their natural effectiveness has been fine-tuned as a result of millions of years of natural evolution. Such catalytic effectiveness has prompted the use of biocatalysts from multiple sources on different applications, including the industrial production of goods (food and beverages, detergents, textile, and pharmaceutics), environmental protection, and biomedical applications. Natural enzymes often need to be improved by protein engineering to optimize their function in non-native environments. Recent technological advances have greatly facilitated this process by providing the experimental approaches of directed evolution or by enabling computer-assisted applications. Directed evolution mimics the natural selection process in a highly accelerated fashion at the expense of arduous laboratory work and economic resources. Theoretical methods provide predictions and represent an attractive complement to such experiments by waiving their inherent costs. Computational techniques can be used to engineer enzymatic reactivity, substrate specificity and ligand binding, access pathways and ligand transport, and global properties like protein stability, solubility, and flexibility. Theoretical approaches can also identify hotspots on the protein sequence for mutagenesis and predict suitable alternatives for selected positions with expected outcomes. This review covers the latest advances in computational methods for enzyme engineering and presents many successful case studies.

Artificial Enzymes for Diels-Alder Reactions

The Diels-Alder (DA) reaction is a cycloaddition of a conjugated diene and an alkene (dienophile) leading to the formation of a cyclohexene derivative through a concerted mechanism. As DA reactions generally proceed with a high degree of regio- and stereoselectivity, they are widely used in synthetic organic chemistry. Considering eco-conscious public and governmental movements, efforts are now directed towards the development of synthetic processes that meet environmental concerns. Artificial enzymes, which can be developed to catalyze abiotic reactions, appear to be important synthetic tools in the synthetic biology field. This review describes the different strategies used to develop protein-based artificial enzymes for DA reactions, including for in cellulo approaches.

Multiscale computation delivers organophosphorus reactivity and stereoselectivity to immunoglobulin scavengers

Developed quantum mechanics/molecular mechanics (QM/MM) supercomputation is used to identify maturation of immunoglobulin (Ig) reactivity through optimization of amino acids in the catalytic core, focused on enhanced nucleophilic attack on phosphorus. A computationally optimized variant, l-Leu47Lys, delivers 340-fold reactivity enhancement for paraoxon relative to wild-type Ig-paraoxonase resulting from substrate-induced ionization of the Tyr37 nucleophile. Variant antibody stereoselectivity toward a P-chiral phenylphosphonate toxin results from starkly contrasting solvent-assisted or general base catalytic mechanisms relative to the parent Ig-paraoxonase. These mechanisms are defined by crystallographic and QM/MM analyses as resulting from changed substrate orientation and participating amino acid conformations within the catalytic site.

Quantum mechanics/molecular mechanics (QM/MM) maturation of an immunoglobulin (Ig) powered by supercomputation delivers novel functionality to this catalytic template and facilitates artificial evolution of biocatalysts. We here employ density functional theory-based (DFT-b) tight binding and funnel metadynamics to advance our earlier QM/MM maturation of A17 Ig-paraoxonase (WTIgP) as a reactibody for organophosphorus toxins. It enables regulation of biocatalytic activity for tyrosine nucleophilic attack on phosphorus. The single amino acid substitution l-Leu47Lys results in 340-fold enhanced reactivity for paraoxon. The computed ground-state complex shows substrate-induced ionization of the nucleophilic l-Tyr37, now H-bonded to l-Lys47, resulting from repositioning of l-Lys47. Multiple antibody structural homologs, selected by phenylphosphonate covalent capture, show contrasting enantioselectivities for a P-chiral phenylphosphonate toxin. That is defined by crystallographic analysis of phenylphosphonylated reaction products for antibodies A5 and WTIgP. DFT-b analysis using QM regions based on these structures identifies transition states for the favored and disfavored reactions with surprising results. This stereoselection analysis is extended by funnel metadynamics to a range of WTIgP variants whose predicted stereoselectivity is endorsed by experimental analysis. The algorithms used here offer prospects for tailored design of highly evolved, genetically encoded organophosphorus scavengers and for broader functionalities of members of the Ig superfamily, including cell surface-exposed receptors.

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.

Kemp Elimination Reaction Catalyzed by Electric Fields

The Kemp elimination reaction is the most widely used in the de novo design of new enzymes. The effect of two different kinds of electric fields in the reactions of acetate as a base with benzisoxazole and 5-nitrobenzisoxazole as substrates have been theoretically studied. The effect of the solvent reaction field has been calculated using the SMD continuum model for several solvents; we have shown that solvents inhibit both reactions, the decrease of the reaction rate being larger as far as the dielectric constant is increased. The diminution of the reaction rate is especially remarkable between aprotic organic solvents and protic solvents as water, the electrostatic term of the hydrogen bonds being the main factor for the large inhibitory effect of water. The presence of an external electric field oriented in the direction of the charge transfer (z axis) increases it and, so, the reaction rate. In the reaction of the nitro compound, if the electric field is oriented in an orthogonal direction (x axis) the charge transfer to the NO₂ group is favored and there is a subsequent increase of the reaction rate. However, this increase is smaller than the one produced by the field in the z axis. It is worthwhile mentioning that one of the main effects of external electric fields of intermediate intensity is the reorientation of the reactants. Finally, the implications of our results in the de novo design of enzymes are discussed.

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.

User-Friendly Quantum Mechanics: Applications for Drug Discovery

Quantum mechanics (QM) methods provide a fine description of receptor-ligand interactions and of chemical reactions. Their use in drug design and drug discovery is increasing, especially for complex systems including metal ions in the binding sites, for the design of highly selective inhibitors, for the optimization of bi-specific compounds, to understand enzymatic reactions, and for the study of covalent ligands and prodrugs. They are also used for generating molecular descriptors for predictive QSAR/QSPR models and for the parameterization of force fields. Thanks to the continuous increase of computational power offered by GPUs and to the development of sophisticated algorithms, QM methods are becoming part of the standard tools used in computer-aided drug design (CADD). We present the most used QM methods and software packages, and we discuss recent representative applications in drug design and drug discovery.

Dissecting the evolvability landscape of the CalB active site toward aromatic substrates

A key event in the directed evolution of enzymes is the systematic use of mutagenesis and selection, a process that can give rise to mutant libraries containing millions of protein variants. To this day, the functional analysis and identification of active variants among such high numbers of mutational possibilities is not a trivial task. Here, we describe a combinatorial semi-rational approach to partly overcome this challenge and help design smaller and smarter mutant libraries. By adapting a liquid medium transesterification assay in organic solvent conditions with a combination of virtual docking, iterative saturation mutagenesis, and residue interaction network (RIN) analysis, we engineered lipase B from P. antarctica (CalB) to improve enzyme recognition and activity against the bulky aromatic substrates and flavoring agents methyl cinnamate and methyl salicylate. Substrate-imprinted docking was used to target active-site positions involved in enzyme-substrate and enzyme-product complexes, in addition to identifying 'hot spots' most likely to yield active variants. This iterative semi-rational design strategy allowed selection of CalB variants exhibiting increased activity in just two rounds of site-saturation mutagenesis. Beneficial replacements were observed by screening only 0.308% of the theoretical library size, illustrating how semi-rational approaches with targeted diversity can quickly facilitate the discovery of improved activity variants relevant to a number of biotechnological applications.

Biocatalysts for the pharmaceutical industry created by structure-guided directed evolution of stereoselective enzymes

Enzymes have been used for a long time as catalysts in the asymmetric synthesis of chiral intermediates needed in the production of therapeutic drugs. However, this alternative to man-made catalysts has suffered traditionally from distinct limitations, namely the often observed wrong or insufficient enantio- and/or regioselectivity, low activity, narrow substrate range, and insufficient thermostability. With the advent of directed evolution, these problems can be generally solved. The challenge is to develop and apply the most efficient mutagenesis methods which lead to highest-quality mutant libraries requiring minimal screening. Structure-guided saturation mutagenesis and its iterative form have emerged as the method of choice for evolving stereo- and regioselective mutant enzymes needed in the asymmetric synthesis of chiral intermediates. The number of (industrial) applications in the preparation of chiral pharmaceuticals is rapidly increasing. This review features and analyzes typical case studies.

CADEE: Computer-Aided Directed Evolution of Enzymes

The tremendous interest in enzymes as biocatalysts has led to extensive work in enzyme engineering, as well as associated methodology development. Here, a new framework for computer-aided directed evolution of enzymes (CADEE) is presented which allows a drastic reduction in the time necessary to prepare and analyze in silico semi-automated directed evolution of enzymes. A pedagogical example of the application of CADEE to a real biological system is also presented in order to illustrate the CADEE workflow.

Computational tools for the evaluation of laboratory-engineered biocatalysts

Biocatalysis is based on the application of natural catalysts for new purposes, for which enzymes were not designed. Although the first examples of biocatalysis were reported more than a century ago, biocatalysis was revolutionized after the discovery of an in vitro version of Darwinian evolution called Directed Evolution (DE). Despite the recent advances in the field, major challenges remain to be addressed. Currently, the best experimental approach consists of creating multiple mutations simultaneously while limiting the choices using statistical methods. Still, tens of thousands of variants need to be tested experimentally, and little information is available on how these mutations lead to enhanced enzyme proficiency. This review aims to provide a brief description of the available computational techniques to unveil the molecular basis of improved catalysis achieved by DE. An overview of the strengths and weaknesses of current computational strategies is explored with some recent representative examples. The understanding of how this powerful technique is able to obtain highly active variants is important for the future development of more robust computational methods to predict amino-acid changes needed for activity.

Computational modeling and molecular dynamics simulations of mammalian cytoplasmic tyrosyl-tRNA synthetase and its complexes with substrates

Cytoplasmic tyrosyl-tRNA synthetase (TyrRS) is one of the key enzymes of protein biosynthesis. TyrRSs of pathogenic organisms have gained attention as potential targets for drug development. Identifying structural differences between various TyrRSs will facilitate the development of specific inhibitors for the TyrRSs of pathogenic organisms. However, there is a deficiency in structural data for mammalian cytoplasmic TyrRS in complexes with substrates. In this work, we constructed spatial structure of full-length Bos taurus TyrRS (BtTyrRS) and its complexes with substrates using the set of computational modeling techniques. Special attention was paid to BtTyrRS complexes with substrates [L-tyrosine, K⁺ and ATP:Mg²⁺] and intermediate products [tyrosyl-adenylate (Tyr-AMP), K⁺ and PP_i:Mg²⁺] with the different catalytic loop conformations. In order to analyze their dynamical properties, we performed 100 ns of molecular dynamics (MD) simulations. MD simulations revealed new structural data concerning the tyrosine activation reaction in mammalian TyrRS. Formation of strong interaction between Lys154 and γ-phosphate suggests the additional role of CP1 insertion as an important factor for ATP binding. The presence of a potassium-binding pocket within the active site of mammalian TyrRS compensates the absence of the second lysine in the KMSKS motif. Our data provide new details concerning a role of K⁺ ions at different stages of the first step of the tyrosylation reaction, including the coordination of substrates and involvement in the PP_i releasing. The results of this work suggest that differences between ATP-binding sites of mammalian and bacterial TyrRSs are meaningful and could be exploited in the drug design.

Design of Catalytic Peptides and Proteins Through Rational and Combinatorial Approaches

This review focuses on recent progress in noncomputational methods to introduce catalytic function into proteins, peptides, and peptide assemblies. We discuss various approaches to creating catalytic activity and classification of noncomputational methods into rational and combinatorial classes. The section on rational design covers recent progress in the development of short peptides and oligomeric peptide assemblies for various natural and unnatural reactions. The section on combinatorial design describes recent advances in the discovery of catalytic peptides. We present the future prospects of these and other new approaches in a broader context, including implications for functional material design.

Computational Studies of Candida Antarctica Lipase B to Test Its Capability as a Starting Point To Redesign New Diels-Alderases

The design of new biocatalysts is a target that is receiving increasing attention. One of the most popular reactions in this regard is the Diels-Alder cycloaddition because of its applications in organic synthesis and the absence of efficient natural enzymes that catalyze it. In this paper, the possibilities of using the highly promiscuous Candida Antarctica lipase B as a protein scaffold to redesign a Diels-Alderase has been explored by means of theoretical quantum mechanics/molecular mechanics (QM/MM) molecular dynamics simulations. Free energy surfaces have been computed for two reactions in the wild-type and in several mutants with hybrid AM1/MM potentials with corrections at M06-2X/MM level. The study of the counterpart reactions in solution has allowed performing comparative analysis that render interesting conclusions. Since the dienophile anchors very well in the oxyanion hole of all tested protein variants, the slight electronic changes from reactant complex to the transition state suggest that mutations should be focused in favoring the formation of reactive conformations of a reactant complex that, in turn, would reduce the energy barrier.

De novo design of stereochemically-bent sixteen-residue β-hairpin as a hydrolase mimic

Stepwise design of sixteen-residue β-hairpin as a hydrolase mimic involving fold design by stereochemical mutation followed by inverse-design of sequence.