Computational Design of Synthetic Enzymes

Cryo-EM and Molecular Dynamics Simulations Reveal Hidden Conformational Dynamics Controlling Ammonia Transport in Human Asparagine Synthetase

How motions in enzymes might be linked to catalytic function is of considerable general interest. Recent advances in X-ray crystallography and cryogenic electron microscopy offer the promise of elucidating functionally relevant motions in proteins that are not easily amenable to study by other biophysical methods. Here we use 3D variability analysis (3DVA) on cryo-EM maps for wild type (WT) human asparagine synthetase (ASNS) and the R142I ASNS variant to identify conformational changes in the Arg-142 side chain, which mediates the formation of a catalytically relevant intramolecular tunnel. Our 3DVA results for WT ASNS are consistent with independent molecular dynamics (MD) simulations on a model generated from the X-ray structure of human ASNS. Moreover, MD simulations of computational models for the ASNS/β-aspartyl-AMP/MgPPi and R142I/β-aspartyl-AMP/MgPPi ternary complexes, suggest that the structural integrity of the tunnel is impaired in the R142I variant when β-aspartyl-AMP is present in the synthetase active site. The kinetic properties of the R142I ASNS variant support the proposed function of Arg-142. These studies illustrate the power of cryo-EM to identify localized motions and dissect the conformational landscape of large proteins. When combined with MD simulations, 3DVA is a powerful approach to understanding how conformational dynamics might regulate function in multi-domain enzymes possessing multiple active sites.

Advances in microbial exoenzymes bioengineering for improvement of bioplastics degradation

Plastic pollution has become a major global concern, posing numerous challenges for the environment and wildlife. Most conventional ways of plastics degradation are inefficient and cause great damage to ecosystems. The development of biodegradable plastics offers a promising solution for waste management. These plastics are designed to break down under various conditions, opening up new possibilities to mitigate the negative impact of traditional plastics. Microbes, including bacteria and fungi, play a crucial role in the degradation of bioplastics by producing and secreting extracellular enzymes, such as cutinase, lipases, and proteases. However, these microbial enzymes are sensitive to extreme environmental conditions, such as temperature and acidity, affecting their functions and stability. To address these challenges, scientists have employed protein engineering and immobilization techniques to enhance enzyme stability and predict protein structures. Strategies such as improving enzyme and substrate interaction, increasing enzyme thermostability, reinforcing the bonding between the active site of the enzyme and substrate, and refining enzyme activity are being utilized to boost enzyme immobilization and functionality. Recently, bioengineering through gene cloning and expression in potential microorganisms, has revolutionized the biodegradation of bioplastics. This review aimed to discuss the most recent protein engineering strategies for modifying bioplastic-degrading enzymes in terms of stability and functionality, including enzyme thermostability enhancement, reinforcing the substrate binding to the enzyme active site, refining with other enzymes, and improvement of enzyme surface and substrate action. Additionally, discovered bioplastic-degrading exoenzymes by metagenomics techniques were emphasized.

Hydrophobicity-Based Force Field In Enzymes

The biocatalysis process takes place with the participation of enzymes, which, depending on the reaction carried out, require, apart from the appropriate arrangement of catalytic residues, an appropriate external force field. It is generated by the protein body. The relatively small size of the part directly involved in the process itself is supported by the presence of an often complex structure of the protein body, the purpose of which is to provide an appropriate local force field, eliminating the influence of water. Very often, the large size of the enzyme is an expression of the complex form of this field. In this paper, a comparative analysis of arbitrarily selected enzymes, representatives of different enzyme classes, was carried out, focusing on the measurement of the diversity of the force field provided by a given protein. This analysis was based on the fuzzy oil drop model (FOD) and its modified version (FOD-M), which takes into account the participation of nonaqueous external factors in shaping the structure and thus the force field within the protein. The degree and type of ordering of the hydrophobicity distribution in the protein molecule is the result of the influence of the environment but also the supplier of the local environment for a given process, including the catalysis process in particular. Determining the share of a nonaqueous environment is important due to the ubiquity of polar water, whose participation in processes with high specificity requires control. It can be assumed that some enzymes in their composition have a permanently built-in part, the role of which is reduced to that of a permanent chaperone. It provides a specific external force field needed for the process. The proposed model, generalized to other types of proteins, may also provide a form of recording the environment model for the simulation of the in silico protein folding process, taking into account the impact of its differentiation.

Ceria-Based Nanozymes in Point-of-Care Diagnosis: An Emerging Futuristic Approach for Biosensing

In recent years, there has been a notable increase in interest surrounding nanozymes due to their ability to imitate the functions and address the limitations of natural enzymes. The scientific community has been greatly intrigued by the study of nanoceria, primarily because of their distinctive physicochemical characteristics, which include a variety of enzyme-like activities, affordability, exceptional stability, and the ability to easily modify their surfaces. Consequently, nanoceria have found extensive use in various biosensing applications. However, the impact of its redox activity on the enzymatic catalytic mechanism remains a subject of debate, as conflicting findings in the literature have presented both pro-oxidant and antioxidant effects. Herein, we creatively propose a seesaw model to clarify the regulatory mechanism on redox balance and survey possible mechanisms of multienzyme mimetic properties of nanoceria. In addition, this review aims to showcase the latest advancements in this field by systematically discussing over 180 research articles elucidating the significance of ceria-based nanozymes in enhancing, downsizing, and enhancing the efficacy of point-of-care (POC) diagnostics. These advancements align with the ASSURED criteria established by the World Health Organization (WHO). Furthermore, this review also examines potential constraints in order to offer readers a concise overview of the emerging role of nanoceria in the advancement of POC diagnostic systems for future biosensing applications.

Substrate Positioning Dynamics Involves a Non-Electrostatic Component to Mediate Catalysis

Substrate positioning dynamics (SPD) orients the substrate in the active site, thereby influencing catalytic efficiency. However, it remains unknown whether SPD effects originate primarily from electrostatic perturbation inside the enzyme or can independently mediate catalysis with a significant non-electrostatic component. In this work, we investigated how the non-electrostatic component of SPD affects transition state (TS) stabilization. Using high-throughput enzyme modeling, we selected Kemp eliminase variants with similar electrostatics inside the enzyme but significantly different SPD. The kinetic parameters of these mutants were experimentally characterized. We observed a valley-shaped, two-segment linear correlation between the TS stabilization free energy (converted from kinetic parameters) and substrate positioning index (a metric to quantify SPD). The energy varies by approximately 2 kcal/mol. Favorable SPD was observed for the distal mutant R154W, increasing the proportion of reactive conformations and leading to the lowest activation free energy. These results indicate the substantial contribution of the non-electrostatic component of SPD to enzyme catalytic efficiency.

Anion-π catalysis on carbon allotropes

Anion-π catalysis, introduced in 2013, stands for the stabilization of anionic transition states on π-acidic aromatic surfaces. Anion-π catalysis on carbon allotropes is particularly attractive because high polarizability promises access to really strong anion-π interactions. With these expectations, anion-π catalysis on fullerenes has been introduced in 2017, followed by carbon nanotubes in 2019. Consistent with expectations from theory, anion-π catalysis on carbon allotropes generally increases with polarizability. Realized examples reach from enolate addition chemistry to asymmetric Diels-Alder reactions and autocatalytic ether cyclizations. Currently, anion-π catalysis on carbon allotropes gains momentum because the combination with electric-field-assisted catalysis promises transformative impact on organic synthesis.

Mechanisms and Opportunities for Rational In Silico Design of Enzymes to Degrade Per- and Polyfluoroalkyl Substances (PFAS)

Per and polyfluoroalkyl substances (PFAS) present a unique challenge to remediation techniques because their strong carbon-fluorine bonds make them difficult to degrade. This review explores the use of in silico enzymatic design as a potential PFAS degradation technique. The scope of the enzymes included is based on currently known PFAS degradation techniques, including chemical redox systems that have been studied for perfluorooctanesulfonic acid (PFOS) and perfluorooctanoic acid (PFOA) defluorination, such as those that incorporate hydrated electrons, sulfate, peroxide, and metal catalysts. Bioremediation techniques are also discussed, namely the laccase and horseradish peroxidase systems. The redox potential of known reactants and enzymatic radicals/metal-complexes are then considered and compared to potential enzymes for degrading PFAS. The molecular structure and reaction cycle of prospective enzymes are explored. Current knowledge and techniques of enzyme design, particularly radical-generating enzymes, and application are also discussed. Finally, potential routes for bioengineering enzymes to enable or enhance PFAS remediation are considered as well as the future outlook for computational exploration of enzymatic in situ bioremediation routes for these highly persistent and globally distributed contaminants.

Harnessing generative AI to decode enzyme catalysis and evolution for enhanced engineering

Enzymes, as paramount protein catalysts, occupy a central role in fostering remarkable progress across numerous fields. However, the intricacy of sequence-function relationships continues to obscure our grasp of enzyme behaviors and curtails our capabilities in rational enzyme engineering. Generative artificial intelligence (AI), known for its proficiency in handling intricate data distributions, holds the potential to offer novel perspectives in enzyme research. Generative models could discern elusive patterns within the vast sequence space and uncover new functional enzyme sequences. This review highlights the recent advancements in employing generative AI for enzyme sequence analysis. We delve into the impact of generative AI in predicting mutation effects on enzyme fitness, catalytic activity and stability, rationalizing the laboratory evolution of de novo enzymes, and decoding protein sequence semantics and their application in enzyme engineering. Notably, the prediction of catalytic activity and stability of enzymes using natural protein sequences serves as a vital link, indicating how enzyme catalysis shapes enzyme evolution. Overall, we foresee that the integration of generative AI into enzyme studies will remarkably enhance our knowledge of enzymes and expedite the creation of superior biocatalysts.

Enhanced active-site electric field accelerates enzyme catalysis

The design and improvement of enzymes based on physical principles remain challenging. Here we demonstrate that the principle of electrostatic catalysis can be leveraged to substantially improve a natural enzyme's activity. We enhanced the active-site electric field in horse liver alcohol dehydrogenase by replacing the serine hydrogen-bond donor with threonine and replacing the catalytic Zn2+ with Co2+. Based on the electric field enhancement, we make a quantitative prediction of rate acceleration-50-fold faster than the wild-type enzyme-which was in close agreement with experimental measurements. The effects of the hydrogen bonding and metal coordination, two distinct chemical forces, are described by a unified physical quantity-electric field, which is quantitative, and shown here to be additive and predictive. These results suggest a new design paradigm for both biological and non-biological catalysts.

Understanding Cysteine Reactivity in Protein Environments with Electric Fields

The role cysteine residues play in proteins is mediated by their protonation state, whereby the thiolate form of the side chain is highly reactive while the thiol form is more inert. However, the pKa of cysteine residues is hard to predict as it can differ widely from its reference value in solution, an effect that is accentuated by local effects in the heterogeneous protein environment. Here, we present a new approach to the prediction of cysteine reactivity based on electric field calculations at the thiol/thiolate group. We validated our approach by predicting the protonation state of cysteine residues in different protein environments (in the active site, at the protein surface, and buried within the protein interior), including Cys-25 in papaya protease omega, which was proven problematic for the more traditional constant pH molecular dynamics (MD) technique. We predict pKa shifts consistent with experimental observations, and the decomposition of the electric fields into contributions from molecular fragments provides a direct handle to rationalize local pH and pKa effects in proteins without introducing parameters other than those of the force field used for MD simulations.

Mutexa: A Computational Ecosystem for Intelligent Protein Engineering

Protein engineering holds immense promise in shaping the future of biomedicine and biotechnology. This Review focuses on our ongoing development of Mutexa, a computational ecosystem designed to enable "intelligent protein engineering". In this vision, researchers will seamlessly acquire sequences of protein variants with desired functions as biocatalysts, therapeutic peptides, and diagnostic proteins through a finely-tuned computational machine, akin to Amazon Alexa's role as a versatile virtual assistant. The technical foundation of Mutexa has been established through the development of a database that combines and relates enzyme structures and their respective functions (e.g., IntEnzyDB), workflow software packages that enable high-throughput protein modeling (e.g., EnzyHTP and LassoHTP), and scoring functions that map the sequence-structure-function relationship of proteins (e.g., EnzyKR and DeepLasso). We will showcase the applications of these tools in benchmarking the convergence conditions of enzyme functional descriptors across mutants, investigating protein electrostatics and cavity distributions in SAM-dependent methyltransferases, and understanding the role of nonelectrostatic dynamic effects in enzyme catalysis. Finally, we will conclude by addressing the future steps and fundamental challenges in our endeavor to develop new Mutexa applications that assist the identification of beneficial mutants in protein engineering.

Computational design of highly efficient thermostable MHET hydrolases and dual enzyme system for PET recycling

Recently developed enzymes for the depolymerization of polyethylene terephthalate (PET) such as FAST-PETase and LCC-ICCG are inhibited by the intermediate PET product mono(2-hydroxyethyl) terephthalate (MHET). Consequently, the conversion of PET enzymatically into its constituent monomers terephthalic acid (TPA) and ethylene glycol (EG) is inefficient. In this study, a protein scaffold (1TQH) corresponding to a thermophilic carboxylesterase (Est30) was selected from the structural database and redesigned in silico. Among designs, a double variant KL-MHETase (I171K/G130L) with a similar protein melting temperature (67.58 °C) to that of the PET hydrolase FAST-PETase (67.80 °C) exhibited a 67-fold higher activity for MHET hydrolysis than FAST-PETase. A fused dual enzyme system comprising KL-MHETase and FAST-PETase exhibited a 2.6-fold faster PET depolymerization rate than FAST-PETase alone. Synergy increased the yield of TPA by 1.64 fold, and its purity in the released aromatic products reached 99.5%. In large reaction systems with 100 g/L substrate concentrations, the dual enzyme system KL36F achieved over 90% PET depolymerization into monomers, demonstrating its potential applicability in the industrial recycling of PET plastics. Therefore, a dual enzyme system can greatly reduce the reaction and separation cost for sustainable enzymatic PET recycling.

Electric field-assisted anion-π catalysis on carbon nanotubes in electrochemical microfluidic devices

The vision to control the charges migrating during reactions with external electric fields is attractive because of the promise of general catalysis, emergent properties, and programmable devices. Here, we explore this idea with anion-π catalysis, that is the stabilization of anionic transition states on aromatic surfaces. Catalyst activation by polarization of the aromatic system is most effective. This polarization is induced by electric fields. The use of electrochemical microfluidic reactors to polarize multiwalled carbon nanotubes as anion-π catalysts emerges as essential. These reactors provide access to high fields at low enough voltage to prevent electron transfer, afford meaningful effective catalyst/substrate ratios, and avoid interference from additional electrolytes. Under these conditions, the rate of pyrene-interfaced epoxide-opening ether cyclizations is linearly voltage-dependent at positive voltages and negligible at negative voltages. While electromicrofluidics have been conceived for redox chemistry, our results indicate that their use for supramolecular organocatalysis has the potential to noncovalently electrify organic synthesis in the broadest sense.

Harnessing Generative AI to Decode Enzyme Catalysis and Evolution for Enhanced Engineering

Enzymes, as paramount protein catalysts, occupy a central role in fostering remarkable progress across numerous fields. However, the intricacy of sequence-function relationships continues to obscure our grasp of enzyme behaviors and curtails our capabilities in rational enzyme engineering. Generative artificial intelligence (AI), known for its proficiency in handling intricate data distributions, holds the potential to offer novel perspectives in enzyme research. By applying generative models, we could discern elusive patterns within the vast sequence space and uncover new functional enzyme sequences. This review highlights the recent advancements in employing generative AI for enzyme sequence analysis. We delve into the impact of generative AI in predicting mutation effects on enzyme fitness, activity, and stability, rationalizing the laboratory evolution of de novo enzymes, decoding protein sequence semantics, and its applications in enzyme engineering. Notably, the prediction of enzyme activity and stability using natural enzyme sequences serves as a vital link, indicating how enzyme catalysis shapes enzyme evolution. Overall, we foresee that the integration of generative AI into enzyme studies will remarkably enhance our knowledge of enzymes and expedite the creation of superior biocatalysts.

QM/MM Modeling Aided Enzyme Engineering in Natural Products Biosynthesis

Natural products and their derivatives are widely used across various industries, particularly pharmaceuticals. Modern engineered biosynthesis provides an alternative way of producing and meeting the growing need for diverse natural products. Natural enzymes, on the other hand, often exhibit unsatisfactory catalytic characteristics and necessitate further enzyme engineering modifications. QM/MM, as a powerful and extensively used computational tool in the field of enzyme catalysis, has been increasingly applied in rational enzyme engineering over the past decade. In this review, we summarize recent advances in QM/MM computational investigation on enzyme catalysis and enzyme engineering for natural product biosynthesis. The challenges and perspectives for future QM/MM applications aided enzyme engineering in natural product biosynthesis will also be discussed.

Non-Native Site-Selective Enzyme Catalysis

The ability to site-selectively modify equivalent functional groups in a molecule has the potential to streamline syntheses and increase product yields by lowering step counts. Enzymes catalyze site-selective transformations throughout primary and secondary metabolism, but leveraging this capability for non-native substrates and reactions requires a detailed understanding of the potential and limitations of enzyme catalysis and how these bounds can be extended by protein engineering. In this review, we discuss representative examples of site-selective enzyme catalysis involving functional group manipulation and C-H bond functionalization. We include illustrative examples of native catalysis, but our focus is on cases involving non-native substrates and reactions often using engineered enzymes. We then discuss the use of these enzymes for chemoenzymatic transformations and target-oriented synthesis and conclude with a survey of tools and techniques that could expand the scope of non-native site-selective enzyme catalysis.

Prediction of Enzyme Catalysis by Computing Reaction Energy Barriers via Steered QM/MM Molecular Dynamics Simulations and Machine Learning

The prediction of enzyme activity is one of the main challenges in catalysis. With computer-aided methods, it is possible to simulate the reaction mechanism at the atomic level. However, these methods are usually expensive if they are to be used on a large scale, as they are needed for protein engineering campaigns. To alleviate this situation, machine learning methods can help in the generation of predictive-decision models. Herein, we test different regression algorithms for the prediction of the reaction energy barrier of the rate-limiting step of the hydrolysis of mono-(2-hydroxyethyl)terephthalic acid by the MHETase ofIdeonella sakaiensis. As a training data set, we use steered quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) simulation snapshots and their corresponding pulling work values. We have explored three algorithms together with three chemical representations. As an outcome, our trained models are able to predict pulling works along the steered QM/MM MD simulations with a mean absolute error below 3 kcal mol-1 and a score value above 0.90. More challenging is the prediction of the energy maximum with a single geometry. Whereas the use of the initial snapshot of the QM/MM MD trajectory as input geometry yields a very poor prediction of the reaction energy barrier, the use of an intermediate snapshot of the former trajectory brings the score value above 0.40 with a low mean absolute error (ca. 3 kcal mol-1). Altogether, we have faced in this work some initial challenges of the final goal of getting an efficient workflow for the semiautomatic prediction of enzyme-catalyzed energy barriers and catalytic efficiencies.

A simple and consistent quantum-chemical fragmentation scheme for proteins that includes two-body contributions

The Molecular Fractionation with Conjugate Caps (MFCC) method is a popular fragmentation method for the quantum-chemical treatment of proteins. However, it does not account for interactions between the amino acid fragments, such as intramolecular hydrogen bonding. Here, we present a combination of the MFCC fragmentation scheme with a second-order many-body expansion (MBE) that consistently accounts for all fragment-fragment, fragment-cap, and cap-cap interactions, while retaining the overall simplicity of the MFCC scheme with its chemically meaningful fragments. We show that with the resulting MFCC-MBE(2) scheme, the errors in the total energies of selected polypeptides and proteins can be reduced by up to one order of magnitude and relative energies of different protein conformers can be predicted accurately.

De Novo Computational Design of a Lipase with Hydrolysis Activity towards Middle-Chained Fatty Acid Esters

Innovations in biocatalysts provide great prospects for intolerant environments or novel reactions. Due to the limited catalytic capacity and the long-term and labor-intensive characteristics of mining enzymes with the desired functions, de novo enzyme design was developed to obtain industrial application candidates in a rapid and convenient way. Here, based on the catalytic mechanisms and the known structures of proteins, we proposed a computational protein design strategy combining de novo enzyme design and laboratory-directed evolution. Starting with the theozyme constructed using a quantum-mechanical approach, the theoretical enzyme-skeleton combinations were assembled and optimized via the Rosetta "inside-out" protocol. A small number of designed sequences were experimentally screened using SDS-PAGE, mass spectrometry and a qualitative activity assay in which the designed enzyme 1a8uD₁ exhibited a measurable hydrolysis activity of 24.25 ± 0.57 U/g towards p-nitrophenyl octanoate. To improve the activity of the designed enzyme, molecular dynamics simulations and the RosettaDesign application were utilized to further optimize the substrate binding mode and amino acid sequence, thus keeping the residues of theozyme intact. The redesigned lipase 1a8uD₁-M8 displayed enhanced hydrolysis activity towards p-nitrophenyl octanoate-3.34 times higher than that of 1a8uD₁. Meanwhile, the natural skeleton protein (PDB entry 1a8u) did not display any hydrolysis activity, confirming that the hydrolysis abilities of the designed 1a8uD₁ and the redesigned 1a8uD₁-M8 were devised from scratch. More importantly, the designed 1a8uD₁-M8 was also able to hydrolyze the natural middle-chained substrate (glycerol trioctanoate), for which the activity was 27.67 ± 0.69 U/g. This study indicates that the strategy employed here has great potential to generate novel enzymes exhibiting the desired reactions.

Heme-Dependent Supramolecular Nanocatalysts: A Review

Enzymes fold into three-dimensional structures to distribute amino acid residues for catalysis, which inspired the supramolecular approach to construct enzyme-mimicking catalysts. A key concern in the development of supramolecular strategies is the ability to confine and orient functional groups to form enzyme-like active sites in artificial materials. This review introduces the design principles and construction of supramolecular nanomaterials exhibiting catalytic functions of heme-dependent enzymes, a large class of metalloproteins, which rely on a heme cofactor and spatially configured residues to catalyze diverse reactions via a complex multistep mechanism. We focus on the structure-activity relationship of the supramolecular catalysts and their applications in materials synthesis/degradation, biosensing, and therapeutics. The heme-free catalysts that catalyze reactions achieved by hemeproteins are also briefly discussed. Towards the end of the review, we discuss the outlook on the challenges related to catalyst design and future prospective, including the development of structure-resolving techniques and design concepts, with the aim of creating enzyme-mimicking materials that possess catalytic power rivaling that of natural enzymes..

Design of Artificial Enzymes: Insights into Protein Scaffolds

The design of artificial enzymes has emerged as a promising tool for the generation of potent biocatalysts able to promote new-to-nature reactions with improved catalytic performances, providing a powerful platform for wide-ranging applications and a better understanding of protein functions and structures. The selection of an appropriate protein scaffold plays a key role in the design process. This review aims to give a general overview of the most common protein scaffolds that can be exploited for the generation of artificial enzymes. Several examples are discussed and categorized according to the strategy used for the design of the artificial biocatalyst, namely the functionalization of natural enzymes, the creation of a new catalytic site in a protein scaffold bearing a wide hydrophobic pocket and de novo protein design. The review is concluded by a comparison of these different methods and by our perspective on the topic.

Strong External Electric Fields Reduce Explosive Sensitivity: A Theoretical Investigation into the Reaction Selectivity in NH2NO2∙∙∙NH3

Controlling the selectivity of a detonation initiation reaction of explosive is essential to reduce sensitivity, and it seems impossible to reduce it by strengthening the external electric field. To verify this, the effects of external electric fields on the initiation reactions in NH2NO2∙∙∙NH3, a model system of the nitroamine explosive with alkaline additive, were investigated at the MP2/6-311++G(2d,p) and CCSD(T)/6-311++G(2d,p) levels. The concerted effect in the intermolecular hydrogen exchange is characterized by an index of the imaginary vibrations. Due to the weakened concerted effects by the electric field along the −x-direction opposite to the “reaction axis”, the dominant reaction changes from the intermolecular hydrogen exchange to 1,3-intramolecular hydrogen transference with the increase in the field strengths. Furthermore, the stronger the field strengths, the higher the barrier heights become, indicating the lower sensitivities. Therefore, by increasing the field strength and adjusting the orientation between the field and “reaction axis”, not only can the reaction selectivity be controlled, but the sensitivity can also be reduced, in particular under a super-strong field. Thus, a traditional concept, in which the explosive is dangerous under the super-strong external electric field, is theoretically broken. Compared to the neutral medium, a low sensitivity of the explosive with alkaline can be achieved under the stronger field. Employing atoms in molecules, reduced density gradient, and surface electrostatic potentials, the origin of the reaction selectivity and sensitivity change is revealed. This work provides a new idea for the technical improvement regarding adding the external electric field into the explosive system.

Reactivity and Selectivity of the Diels-Alder Reaction of Anthracene in [Pd6L4]12+ Supramolecular Cages: A Computational Study

The field of supramolecular metal-organic cage catalysis has grown rapidly in recent years. However, theoretical studies regarding the reaction mechanism and reactivity and selectivity controlling factors for supramolecular catalysis are still underdeveloped. Herein, we demonstrate a detailed density functional theory study on the mechanism, catalytic efficiency, and regioselectivity of the Diels-Alder reaction in bulk solution and within two [Pd₆L₄]¹²⁺ supramolecular cages. Our calculations are consistent with experiments. The origins of the catalytic efficiency of the bowl-shaped cage 1 have been elucidated to be the host-guest stabilization of the transition states and the favorable entropy effect. The reasons for the switch of the regioselectivity from 9,10-addition to 1,4-addition within the octahedral cage 2 were attributed to the confinement effect and the noncovalent interactions. This work would shed light on the understanding of [Pd₆L₄]¹²⁺ metallocage-catalyzed reactions and provide a detailed mechanistic profile otherwise difficult to obtain from experiments. The findings of this study could also aid to the improvement and development of more efficient and selective supramolecular catalysis.

Geometry of Charge Density as a Reporter on the Role of the Protein Scaffold in Enzymatic Catalysis: Electrostatic Preorganization and Beyond

Enzymes host active sites inside protein macromolecules, which have diverse, often incredibly complex, and atom-expensive structures. It is an outstanding question what the role of these expensive scaffolds might be in enzymatic catalysis. Answering this question is essential to both enzymology and the design of artificial enzymes with proficiencies that will match those of the best natural enzymes. Protein rigidifying the active site, contrasted with the dynamics and vibrational motion promoting the reaction, as well as long-range electrostatics (also known as electrostatic preorganization) were all proposed as central contributions of the scaffold to the catalysis. Here, we show that all these effects inevitably produce changes in the quantum mechanical electron density in the active site, which in turn defines the reactivity. The phenomena are therefore fundamentally inseparable. The geometry of the electron density-a scalar field characterized by a number of mathematical features such as critical points-is a rigorous and convenient descriptor of enzymatic catalysis and a reporter on the role of the protein. We show how this geometry can be analyzed, linked to the reaction barriers, and report in particular on intramolecular electric fields in enzymes. We illustrate these tools on the studies of electrostatic preorganization in several representative enzyme classes, both natural and artificial. We highlight the forward-looking aspects of the approach.

Engineering DszC Mutants from Transition State Macrodipole Considerations and Evolutionary Sequence Analysis

We describe an approach to identify enzyme mutants with increased turnover using the enzyme DszC as a case study. Our approach is based on recalculating the barriers of alanine mutants through single-point energy calculations at the hybrid QM/MM level in the wild-type reactant and transition state geometries. We analyze the difference in the electron density between the reactant and transition state to identify sites/residues where electrostatic interactions stabilize the transition state over the reactants. We also assess the insertion of a unit probe charge to identify positions in which the introduction of charged residues lowers the barrier.

Understanding Charge Dynamics in Dense Electronic Manifolds in Complex Environments

Photoinduced charge transfer (CT) excited states and their relaxation mechanisms can be highly interdependent on the environment effects and the consequent changes in the electronic density. Providing a molecular interpretation of the ultrafast (subpicosecond) interplay between initial photoexcited states in such dense electronic manifolds in condensed phase is crucial for improving and understanding such phenomena. Real-time time-dependent density functional theory is here the method of choice to observe the charge density, explicitly propagated in an ultrafast time domain, along with all time-dependent properties that can be easily extracted from it. A designed protocol of analysis for real-time electronic dynamics to be applied to time evolving electronic density related properties to characterize both in time and in space CT dynamics in complex systems is here introduced and validated, proposing easy to be read cross-correlation maps. As case studies to test such tools, we present the photoinduced charge-transfer electronic dynamics of 5-benzyluracil, a mimic of nucleic acid/protein interactions, and the metal-to-ligand charge-transfer electronic dynamics in water solution of [Ru(dcbpy)2(NCS)2]4-, dcbpy = (4,4'-dicarboxy-2,2'-bipyridine), or "N34-", a dye sensitizer for solar cells. Electrostatic and explicit ab initio treatment of solvent molecules have been compared in the latter case, revealing the importance of the accurate modeling of mutual solute-solvent polarization on CT kinetics. We observed that explicit quantum mechanical treatment of solvent slowed down the charge carriers mobilities with respect to the gas-phase. When all water molecules were modeled instead as simpler embedded point charges, the electronic dynamics appeared enhanced, with a reduced hole-electron distance and higher mean velocities due to the close fixed charges and an artificially increased polarization effect. Such analysis tools and the presented case studies can help to unveil the influence of the electronic manifold, as well as of the finite temperature-induced structural distortions and the environment on the ultrafast charge motions.

Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery

The field of predictive chemistry relates to the development of models able to describe how molecules interact and react. It encompasses the long-standing task of computer-aided retrosynthesis, but is far more reaching and ambitious in its goals. In this review, we summarize several areas where predictive chemistry models hold the potential to accelerate the deployment, development, and discovery of organic reactions and advance synthetic chemistry.

Supramolecular enzyme-mimicking catalysts self-assembled from peptides

Natural enzymes catalyze biochemical transformations in superior catalytic efficiency and remarkable substrate specificity. The excellent catalytic repertoire of enzymes is attributed to the sophisticated chemical structures of their active sites, as a result of billions-of-years natural evolution. However, large-scale practical applications of natural enzymes are restricted due to their poor stability, difficulty in modification, and high costs of production. One viable solution is to fabricate supramolecular catalysts with enzyme-mimetic active sites. In this review, we introduce the principles and strategies of designing peptide-based artificial enzymes which display catalytic activities similar to those of natural enzymes, such as aldolases, laccases, peroxidases, and hydrolases (mainly the esterases and phosphatases). We also discuss some multifunctional enzyme-mimicking systems which are capable of catalyzing orthogonal or cascade reactions. We highlight the relationship between structures of enzyme-like active sites and the catalytic properties, as well as the significance of these studies from an evolutionary point of view.

Convergence in determining enzyme functional descriptors across Kemp eliminase variants

Molecular simulations have been extensively employed to accelerate biocatalytic discoveries. Enzyme functional descriptors derived from molecular simulations have been leveraged to guide the search for beneficial enzyme mutants. However, the ideal active-site region size for computing the descriptors over multiple enzyme variants remains untested. Here, we conducted convergence tests for dynamics-derived and electrostatic descriptors on 18 Kemp eliminase variants across six active-site regions with various boundary distances to the substrate. The tested descriptors include the root-mean-square deviation of the active-site region, the solvent accessible surface area ratio between the substrate and active site, and the projection of the electric field (EF) on the breaking C-H bond. All descriptors were evaluated using molecular mechanics methods. To understand the effects of electronic structure, the EF was also evaluated using quantum mechanics/molecular mechanics methods. The descriptor values were computed for 18 Kemp eliminase variants. Spearman correlation matrices were used to determine the region size condition under which further expansion of the region boundary does not substantially change the ranking of descriptor values. We observed that protein dynamics-derived descriptors, including RMSD_{active_site} and SASA_ratio, converge at a distance cutoff of 5 Å from the substrate. The electrostatic descriptor, EF_C-H, converges at 6 Å using molecular mechanics methods with truncated enzyme models and 4 Å using quantum mechanics/molecular mechanics methods with whole enzyme model. This study serves as a future reference to determine descriptors for predictive modeling of enzyme engineering.

Ensemble-function relationships to dissect mechanisms of enzyme catalysis

Decades of structure-function studies have established our current extensive understanding of enzymes. However, traditional structural models are snapshots of broader conformational ensembles of interchanging states. We demonstrate the need for conformational ensembles to understand function, using the enzyme ketosteroid isomerase (KSI) as an example. Comparison of prior KSI cryogenic x-ray structures suggested deleterious mutational effects from a misaligned oxyanion hole catalytic residue. However, ensemble information from room-temperature x-ray crystallography, combined with functional studies, excluded this model. Ensemble-function analyses can deconvolute effects from altering the probability of occupying a state (P-effects) and changing the reactivity of each state (k-effects); our ensemble-function analyses revealed functional effects arising from weakened oxyanion hole hydrogen bonding and substrate repositioning within the active site. Ensemble-function studies will have an integral role in understanding enzymes and in meeting the future goals of a predictive understanding of enzyme catalysis and engineering new enzymes.

MutDock: A computational docking approach for fixed-backbone protein scaffold design

Despite the successes of antibodies as therapeutic binding proteins, they still face production and design challenges. Alternative binding scaffolds of smaller size have been developed to overcome these issues. A subset of these alternative scaffolds recognizes target molecules through mutations to a set of surface resides, which does not alter their backbone structures. While the computational design of antibodies for target epitopes has been explored in depth, the same has not been done for alternative scaffolds. The commonly used dock-and-mutate approach for binding proteins, including antibodies, is limited because it uses a constant sequence and structure representation of the scaffold. Docking fixed-backbone scaffolds with a varied group of surface amino acids increases the chances of identifying superior starting poses that can be improved with subsequent mutations. In this work, we have developed MutDock, a novel computational approach that simultaneously docks and mutates fixed backbone scaffolds for binding a target epitope by identifying a minimum number of hydrogen bonds. The approach is broadly divided into two steps. The first step uses pairwise distance alignment of hydrogen bond-forming areas of scaffold residues and compatible epitope atoms. This step considers both native and mutated rotamers of scaffold residues. The second step mutates clashing variable interface residues and thermodynamically unfavorable residues to create additional strong interactions. MutDock was used to dock two scaffolds, namely, Affibodies and DARPins, with ten randomly selected antigens. The energies of the docked poses were minimized and binding energies were compared with docked poses from ZDOCK and HADDOCK. The top MutDock poses consisted of higher and comparable binding energies than the top ZDOCK and HADDOCK poses, respectively. This work contributes to the discovery of novel binders based on smaller-sized, fixed-backbone protein scaffolds.

Switchable Enzyme-mimicking catalysts Self-Assembled from de novo designed peptides and DNA G-quadruplex/hemin complex

Reconstruction of enzymatic active site in an artificial system is key to achieving high catalytic efficiency. Herein, we report the self-assembly of the lysine-containing peptides with guanine-rich DNA and hemin to form peroxidase-mimicking active sites and catalytic nanoparticles. The DNA strand self-folds into a G-quadruplex structure that provides a supramolecular scaffold and a potential axial ligand for hemin. The β-sheet forming capability of the lysine-containing peptides is found to affect the catalytic synergy between the G-quadruplex DNA and the peptide. It is hypothesized that the β-sheet formation of the peptides results in the enrichment of the lysine residues, which distribute on the distal side of hemin to promote the formation of Compound I, like distal arginine residue in natural heme pocket. Incorporation of the histidine residues into the lysine-containing peptides further enhanced the hemin activities, indicating the cooperation between the lysine and histidine. Furthermore, the peptide/DNA/hemin complexes can be switched between active and inactive state by reversible formation and deformation of the DNA G-quadruplex, which was attributed to the peptides-promoted conformational changes of the DNA components. This work opens an avenue to mimic the catalytic residues and their spatial distribution in the natural enzymes, and shed light on the design of the smart biocatalysts that can respond to the environmental stimuli.

Role of Enzyme and Active Site Conformational Dynamics in the Catalysis by α-Amylase Explored with QM/MM Molecular Dynamics

We assessed enzyme:substrate conformational dynamics and the rate-limiting glycosylation step of a human pancreatic α-amylase:maltopentose complex. Microsecond molecular dynamics simulations suggested that the distance of the catalytic Asp197 nucleophile to the anomeric carbon of the buried glucoside is responsible for most of the enzyme active site fluctuations and that both Asp197 and Asp300 interact the most with the buried glucoside unit. The buried glucoside binds either in a ⁴C₁ chair or ²S_O skew conformations, both of which can change to TS-like conformations characteristic of retaining glucosidases. Starting from four distinct enzyme:substrate complexes, umbrella sampling quantum mechanics/molecular mechanics simulations (converged within less than 1 kcal·mol^-1 within a total simulation time of 1.6 ns) indicated that the reaction occurrs with a Gibbs barrier of 13.9 kcal·mol ^-1, in one asynchronous concerted step encompassing an acid-base reaction with Glu233 followed by a loose S_N2-like nucleophilic substitution by the Asp197. The transition state is characterized by a ²H₃ half-chair conformation of the buried glucoside that quickly changes to the E₃ envelope conformation preceding the attack of the anomeric carbon by the Asp197 nucleophile. Thermodynamic analysis of the reaction supported that a water molecule tightly hydrogen bonded to the glycosidic oxygen of the substrate at the reactant state (∼1.6 Å) forms a short hydrogen bond with Glu233 at the transition state (∼1.7 Å) and lowers the Gibbs barrier in over 5 kcal·mol^-1. The resulting Asp197-glycosyl was mostly found in the ⁴C₁ conformation, although the more endergonic B_3,O conformation was also observed. Altogether, the combination of short distances for the acid-base reaction with the Glu233 and for the nucleophilic attack by the Asp197 nucleophile and the availability of water within hydrogen bonding distance of the glycosidic oxygen provides a reliable criteria to identify reactive conformations of α-amylase complexes.

Natural Evolution Provides Strong Hints about Laboratory Evolution of Designer Enzymes

Laboratory evolution combined with computational enzyme design provides the opportunity to generate novel biocatalysts. Nevertheless, it has been challenging to understand how laboratory evolution optimizes designer enzymes by introducing seemingly random mutations. A typical enzyme optimized with laboratory evolution is the abiological Kemp eliminase, initially designed by grafting active site residues into a natural protein scaffold. Here, we relate the catalytic power of laboratory-evolved Kemp eliminases to the statistical energy ([Formula: see text]) inferred from their natural homologous sequences using the maximum entropy model. The [Formula: see text] of designs generated by directed evolution is correlated with enhanced activity and reduced stability, thus displaying a stability-activity trade-off. In contrast, the [Formula: see text] for mutants in catalytic-active remote regions (in which remote residues are important for catalysis) is strongly anticorrelated with the activity. These findings provide an insight into the role of protein scaffolds in the adaption to new enzymatic functions. It also indicates that the valley in the [Formula: see text] landscape can guide enzyme design for abiological catalysis. Overall, the connection between laboratory and natural evolution contributes to understanding what is optimized in the laboratory and how new enzymatic function emerges in nature, and provides guidance for computational enzyme design.

Computational Modeling of Supramolecular Metallo-organic Cages-Challenges and Opportunities

Self-assembled metallo-organic cages have emerged as promising biomimetic platforms that can encapsulate whole substrates akin to an enzyme active site. Extensive experimental work has enabled access to a variety of structures, with a few notable examples showing catalytic behavior. However, computational investigations of metallo-organic cages are scarce, not least due to the challenges associated with their modeling and the lack of accurate and efficient protocols to evaluate these systems. In this review, we discuss key molecular principles governing the design of functional metallo-organic cages, from the assembly of building blocks through binding and catalysis. For each of these processes, computational protocols will be reviewed, considering their inherent strengths and weaknesses. We will demonstrate that while each approach may have its own specific pitfalls, they can be a powerful tool for rationalizing experimental observables and to guide synthetic efforts. To illustrate this point, we present several examples where modeling has helped to elucidate fundamental principles behind molecular recognition and reactivity. We highlight the importance of combining computational and experimental efforts to speed up supramolecular catalyst design while reducing time and resources.

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.

A two-directional vibrational probe reveals different electric field orientations in solution and an enzyme active site

The catalytic power of an electric field depends on its magnitude and orientation with respect to the reactive chemical species. Understanding and designing new catalysts for electrostatic catalysis thus requires methods to measure the electric field orientation and magnitude at the molecular scale. We demonstrate that electric field orientations can be extracted using a two-directional vibrational probe by exploiting the vibrational Stark effect of both the C=O and C-D stretches of a deuterated aldehyde. Combining spectroscopy with molecular dynamics and electronic structure partitioning methods, we demonstrate that, despite distinct polarities, solvents act similarly in their preference for electrostatically stabilizing large bond dipoles at the expense of destabilizing small ones. In contrast, we find that for an active-site aldehyde inhibitor of liver alcohol dehydrogenase, the electric field orientation deviates markedly from that found in solvents, which provides direct evidence for the fundamental difference between the electrostatic environment of solvents and that of a preorganized enzyme active site.

Molecular Dynamics-Derived Descriptor Informs the Impact of Mutation on the Catalytic Turnover Number in Lactonase Across Substrates

Molecular dynamics simulations have been extensively employed to reveal the roles of protein dynamics in mediating enzyme catalysis. However, simulation-derived predictive descriptors that inform the impacts of mutations on catalytic turnover numbers remain largely unexplored. In this work, we report the identification of molecular modeling-derived descriptors to predict mutation effect on the turnover number of lactonase SsoPox with both native and non-native substrates. The study consists of 10 enzyme-substrate complexes resulting from a combination of five enzyme variants with two substrates. For each complex, we derived 15 descriptors from molecular dynamics simulations and applied principal component analysis to rank the predictive capability of the descriptors. A top-ranked descriptor was identified, which is the solvent-accessible surface area (SASA) ratio of the substrate to the active site pocket. A uniform volcano-shaped plot was observed in the distribution of experimental activation free energy against the SASA ratio. To achieve efficient lactonase hydrolysis, a non-native substrate-bound enzyme variant needs to involve a similar range of the SASA ratio to the native substrate-bound wild-type enzyme. The descriptor reflects how well the enzyme active site pocket accommodates a substrate for reaction, which has the potential of guiding optimization of enzyme reaction turnover for non-native chemical transformations.

Structural dynamics support electrostatic interactions in the active site of Adenylate Kinase

Electrostatic preorganization as well as structural and dynamic heterogeneity are often used to rationalize the remarkable catalytic efficiency of enzymes. However, they are often presented as incompatible because the generation of permanent electrostatic effects implies that the protein structure remains rigid. Here, we use a metric, electric fields, that can treat electrostatic contributions and dynamics effects on equal footing, for a unique perspective on enzymatic catalysis. We find that the residues that contribute the most to electrostatic interactions with the substrate in the active site of Adenylate Kinase (our working example) are also the most flexible residues. Further, entropy-tuning mutations raise flexibility at the picosecond timescale where more conformations can be visited on short time periods, thereby softening the sharp heterogeneity normally visible at the microsecond timescale.

Unlocking the computational design of metal-organic cages

Metal-organic cages are macrocyclic structures that can possess an intrinsic void that can hold molecules for encapsulation, adsorption, sensing, and catalysis applications. As metal-organic cages may be comprised from nearly any combination of organic and metal-containing components, cages can form with diverse shapes and sizes, allowing for tuning toward targeted properties. Therefore, their near-infinite design space is almost impossible to explore through experimentation alone and computational design can play a crucial role in exploring new systems. Although high-throughput computational design and screening workflows have long been known as powerful tools in drug and materials discovery, their application in exploring metal-organic cages is more recent. We show examples of structure prediction and host-guest/catalytic property evaluation of metal-organic cages. These examples are facilitated by advances in methods that handle metal-containing systems with improved accuracy and are the beginning of the development of automated cage design workflows. We finally outline a scope for how high-throughput computational methods can assist and drive experimental decisions as the field pushes toward functional and complex metal-organic cages. In particular, we highlight the importance of considering realistic, flexible systems.

Bioprospecting of microbial enzymes: current trends in industry and healthcare

Microbial enzymes have an indispensable role in producing foods, pharmaceuticals, and other commercial goods. Many novel enzymes have been reported from all domains of life, such as plants, microbes, and animals. Nonetheless, industrially desirable enzymes of microbial origin are limited. This review article discusses the classifications, applications, sources, and challenges of most demanded industrial enzymes such as pectinases, cellulase, lipase, and protease. In addition, the production of novel enzymes through protein engineering technologies such as directed evolution, rational, and de novo design, for the improvement of existing industrial enzymes is also explored. We have also explored the role of metagenomics, nanotechnology, OMICs, and machine learning approaches in the bioprospecting of novel enzymes. Overall, this review covers the basics of biocatalysts in industrial and healthcare applications and provides an overview of existing microbial enzyme optimization tools. KEY POINTS: • Microbial bioactive molecules are vital for therapeutic and industrial applications. • High-throughput OMIC is the most proficient approach for novel enzyme discovery. • Comprehensive databases and efficient machine learning models are the need of the hour to fast forward de novo enzyme design and discovery.

Integrating dynamics into enzyme engineering

Enzyme engineering has become a widely adopted practice in research labs and industry. In parallel, the past decades have seen tremendous strides in characterizing the dynamics of proteins, using a growing array of methodologies. Importantly, links have been established between the dynamics of proteins and their function. Characterizing the dynamics of an enzyme prior to, and following, its engineering is beginning to inform on the potential of 'dynamic engineering', i.e. the rational modification of protein dynamics to alter enzyme function. Here we examine the state of knowledge at the intersection of enzyme engineering and protein dynamics, describe current challenges and highlight pioneering work in the nascent area of dynamic engineering.

Enhancing computational enzyme design by a maximum entropy strategy

Although computational enzyme design is of great importance, the advances utilizing physics-based approaches have been slow, and further progress is urgently needed. One promising direction is using machine learning, but such strategies have not been established as effective tools for predicting the catalytic power of enzymes. Here, we show that the statistical energy inferred from homologous sequences with the maximum entropy (MaxEnt) principle significantly correlates with enzyme catalysis and stability at the active site region and the more distant region, respectively. This finding decodes enzyme architecture and offers a connection between enzyme evolution and the physical chemistry of enzyme catalysis, and it deepens our understanding of the stability-activity trade-off hypothesis for enzymes. Overall, the strong correlations found here provide a powerful way of guiding enzyme design.