Engineering an efficient and enantioselective enzyme for the Morita-Baylis-Hillman reaction

C-H functionalization reactions catalyzed by artificial metalloenzymes

CH functionalization, a promising frontier in modern organic chemistry, facilitates the direct conversion of inert CH bonds into many valuable functional groups. Despite its merits, traditional homogeneous catalysis, often faces challenges in efficiency, selectivity, and sustainability towards this transformation. In this context, artificial metalloenzymes (ArMs), resulting from the incorporation of a catalytically-competent metal cofactor within an evolvable protein scaffold, bridges the gap between the efficiency of enzymatic transformations and the versatility of transition metal catalysis. Accordingly, ArMs have emerged as attractive tools for various challenging catalytic transformations. Additionally, the coming of age of directed evolution has unlocked unprecedented avenues for optimizing enzymatic catalysis. Taking advantage of their genetically-encoded protein scaffold, ArMs have been evolved to catalyze various CH functionalization reactions. This review delves into the recent developments of ArM-catalyzed CH functionalization reactions, highlighting the benefits of engineering the second coordination sphere around a metal cofactor within a host protein.

Design and Evolution of an Enzyme for the Asymmetric Michael Addition of Cyclic Ketones to Nitroolefins by Enamine Catalysis

Consistent introduction of novel enzymes is required for developing efficient biocatalysts for challenging biotransformations. Absorbing catalytic modes from organocatalysis may be fruitful for designing new-to-nature enzymes with novel functions. Herein we report a newly designed artificial enzyme harboring a catalytic pyrrolidine residue that catalyzes the asymmetric Michael addition of cyclic ketones to nitroolefins through enamine activation with high efficiency. Diverse chiral γ-nitro cyclic ketones with two stereocenters were efficiently prepared with excellent stereoselectivity (up to 97 % e.e., >20 : 1 d.r.) and good yield (up to 86 %). This work provides an efficient biocatalytic strategy for cyclic ketone functionalization, and highlights the usefulness of artificial enzymes for extending biocatalysis to further non-natural reactions.

Metabolic and enzymatic engineering approach for the production of 2-phenylethanol in engineered Escherichia coli

2-Phenylethanol, known for its rose-like odor and antibacterial activity, is synthesized via exogenous phenylpyruvate by the sequential reaction of phenylpyruvate decarboxylase (PDC) and aldehyde reductase. We first targeted ARO10, a phenylpyruvate decarboxylase gene from Saccharomyces cerevisiae, and identified a suitable aldehyde reductase gene. Co-expression of ARO10 and yahK in E. coli transformants yielded 1.1 g/L of 2-phenylethanol in batch culture. We hypothesized that there might be a bottleneck in PDC activity. The computer-based enzyme evolution was utilized to enhance production. The introduction of an amino acid substitution in ARO10 (ARO10 I544W) stabilized the aromatic ring of the phenylpyruvate substrate, increasing 2-phenylethanol yield 4.1-fold compared to wild-type ARO10. Cultivation of ARO10 I544W-expressing E. coli produced 2.5 g/L of 2-phenylethanol with a yield from glucose of 0.16 g/g after 72 h. This approach represents a significant advancement, achieving the highest yield of 2-phenylethanol from glucose using microbes to date.

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.

Spiers Memorial Lecture: Engineering biocatalysts

Enzymes are being engineered to catalyze chemical reactions for many practical applications in chemistry and biotechnology. The approaches used are surveyed in this short review, emphasizing methods for accessing reactivities not expressed by native protein scaffolds. The successful generation of completely de novo enzymes that rival the rates and selectivities of their natural counterparts highlights the potential role that designer enzymes may play in the coming years in research, industry, and medicine. Some challenges that need to be addressed to realize this ambitious dream are considered together with possible solutions.

Unlocking the function promiscuity of old yellow enzyme to catalyze asymmetric Morita-Baylis-Hillman reaction

Exploring the promiscuity of native enzymes presents a promising strategy for expanding their synthetic applications, particularly for catalyzing challenging reactions in non-native contexts. In this study, we explore the promiscuous potential of old yellow enzymes (OYEs) to facilitate the Morita-Baylis-Hillman reaction (MBH reaction), leveraging substrate similarities between MBH reaction and reduction reaction. Using mass spectrometry and spectroscopic techniques, we confirm promiscuity of GkOYE in both MBH and reduction reactions. By blocking H- and H+ transfer pathways, we engineer GkOYE.8, which loses its reduction ability but enhances its MBH activity. The structural basis of MBH reaction catalyzed by GkOYE.8 is obtained through mutation studies and kinetic simulations. Furthermore, enantiocomplementary mutants GkOYE.11 and GkOYE.13 are obtained by directed evolution, exhibiting the ability to accept various aromatic aldehydes and alkenes as substrates. This study demonstrates the potential of leveraging substrate similarities to unlock enzyme functionalities, enabling the catalysis of new-to-nature reactions.

Evolution of Pyrrolysyl-tRNA Synthetase: From Methanogenesis to Genetic Code Expansion

Over 20 years ago, the pyrrolysine encoding translation system was discovered in specific archaea. Our Review provides an overview of how the once obscure pyrrolysyl-tRNA synthetase (PylRS) tRNA pair, originally responsible for accurately translating enzymes crucial in methanogenic metabolic pathways, laid the foundation for the burgeoning field of genetic code expansion. Our primary focus is the discussion of how to successfully engineer the PylRS to recognize new substrates and exhibit higher in vivo activity. We have compiled a comprehensive list of ncAAs incorporable with the PylRS system. Additionally, we also summarize recent successful applications of the PylRS system in creating innovative therapeutic solutions, such as new antibody-drug conjugates, advancements in vaccine modalities, and the potential production of new antimicrobials.

Unlocking the potential of enzyme engineering via rational computational design strategies

Enzymes play a pivotal role in various industries by enabling efficient, eco-friendly, and sustainable chemical processes. However, the low turnover rates and poor substrate selectivity of enzymes limit their large-scale applications. Rational computational enzyme design, facilitated by computational algorithms, offers a more targeted and less labor-intensive approach. There has been notable advancement in employing rational computational protein engineering strategies to overcome these issues, it has not been comprehensively reviewed so far. This article reviews recent developments in rational computational enzyme design, categorizing them into three types: structure-based, sequence-based, and data-driven machine learning computational design. Case studies are presented to demonstrate successful enhancements in catalytic activity, stability, and substrate selectivity. Lastly, the article provides a thorough analysis of these approaches, highlights existing challenges and potential solutions, and offers insights into future development directions.

A New Age of Biocatalysis Enabled by Generic Activation Modes

Biocatalysis is currently undergoing a profound transformation. The field moves from relying on nature's chemical logic to a discipline that exploits generic activation modes, allowing for novel biocatalytic reactions and, in many instances, entirely new chemistry. Generic activation modes enable a wide range of reaction types and played a pivotal role in advancing the fields of organo- and photocatalysis. This perspective aims to summarize the principal activation modes harnessed in enzymes to develop new biocatalysts. Although extensively researched in the past, the highlighted activation modes, when applied within enzyme active sites, facilitate chemical transformations that have largely eluded efficient and selective catalysis. This advance is attributed to multiple tunable interactions in the substrate binding pocket that precisely control competing reaction pathways and transition states. We will highlight cases of new synthetic methodologies achieved by engineered enzymes and will provide insights into potential future developments in this rapidly evolving field.

Secondary Amine Catalysis in Enzyme Design: Broadening Protein Template Diversity through Genetic Code Expansion

Secondary amines, due to their reactivity, can transform protein templates into catalytically active entities, accelerating the development of artificial enzymes. However, existing methods, predominantly reliant on modified ligands or N-terminal prolines, impose significant limitations on template selection. In this study, genetic code expansion was used to break this boundary, enabling secondary amines to be incorporated into alternative proteins and positions of choice. Pyrrolysine analogues carrying different secondary amines could be incorporated into superfolder green fluorescent protein (sfGFP), multidrug-binding LmrR and nucleotide-binding dihydrofolate reductase (DHFR). Notably, the analogue containing a D-proline moiety demonstrated both proteolytic stability and catalytic activity, conferring LmrR and DHFR with the desired transfer hydrogenation activity. While the LmrR variants were confined to the biomimetic 1-benzyl-1,4-dihydronicotinamide (BNAH) as the hydride source, the optimal DHFR variant favorably used the pro-R hydride from NADPH for stereoselective reactions (e.r. up to 92 : 8), highlighting that a switch of protein template could broaden the nucleophile option for catalysis. Owing to the cofactor compatibility, the DHFR-based secondary amine catalysis could be integrated into an enzymatic recycling scheme. This established method shows substantial potential in enzyme design, applicable from studies on enzyme evolution to the development of new biocatalysts.

Automated in vivo enzyme engineering accelerates biocatalyst optimization

Achieving cost-competitive bio-based processes requires development of stable and selective biocatalysts. Their realization through in vitro enzyme characterization and engineering is mostly low throughput and labor-intensive. Therefore, strategies for increasing throughput while diminishing manual labor are gaining momentum, such as in vivo screening and evolution campaigns. Computational tools like machine learning further support enzyme engineering efforts by widening the explorable design space. Here, we propose an integrated solution to enzyme engineering challenges whereby ML-guided, automated workflows (including library generation, implementation of hypermutation systems, adapted laboratory evolution, and in vivo growth-coupled selection) could be realized to accelerate pipelines towards superior biocatalysts.

Multifunctional Biocatalysts for Organic Synthesis

Biocatalysis is becoming an indispensable tool in organic synthesis due to high enzymatic catalytic efficiency as well as exquisite chemo- and stereoselectivity. Some biocatalysts display great promiscuity including a broad substrate scope as well as the ability to catalyze more than one type of transformation. These promiscuous activities have been applied individually to efficiently access numerous valuable target molecules. However, systems in which enzymes possessing multiple different catalytic activities are applied in the synthesis are less well developed. Such multifunctional biocatalysts (MFBs) would simplify chemical synthesis by reducing the number of operational steps and enzyme count, as well as simplifying the sequence space that needs to be engineered to develop an efficient biocatalyst. In this Perspective, we highlight recently reported MFBs focusing on their synthetic utility and mechanism. We also offer insight into their origin as well as comment on potential strategies for their discovery and engineering.

Transition Path Sampling Study of Engineered Enzymes That Catalyze the Morita-Baylis-Hillman Reaction: Why Is Enzyme Design so Difficult?

It is hoped that artificial enzymes designed in laboratories can be efficient alternatives to chemical catalysts that have been used to synthesize organic molecules. However, the design of artificial enzymes is challenging and requires a detailed molecular-level analysis to understand the mechanism they promote in order to design efficient variants. In this study, we computationally investigate the mechanism of proficient Morita-Baylis-Hillman enzymes developed using a combination of computational design and directed evolution. The powerful transition path sampling method coupled with in-depth post-processing analysis has been successfully used to elucidate the different chemical pathways, transition states, protein dynamics, and free energy barriers of reactions catalyzed by such laboratory-optimized enzymes. This research provides an explanation for how different chemical modifications in an enzyme affect its catalytic activity in ways that are not predictable by static design algorithms.

Strategies for designing biocatalysts with new functions

The engineering of natural enzymes has led to the availability of a broad range of biocatalysts that can be used for the sustainable manufacturing of a variety of chemicals and pharmaceuticals. However, for many important chemical transformations there are no known enzymes that can serve as starting templates for biocatalyst development. These limitations have fuelled efforts to build entirely new catalytic sites into proteins in order to generate enzymes with functions beyond those found in Nature. This bottom-up approach to enzyme development can also reveal new fundamental insights into the molecular origins of efficient protein catalysis. In this tutorial review, we will survey the different strategies that have been explored for designing new protein catalysts. These methods will be illustrated through key selected examples, which demonstrate how highly proficient and selective biocatalysts can be developed through experimental protein engineering and/or computational design. Given the rapid pace of development in the field, we are optimistic that designer enzymes will begin to play an increasingly prominent role as industrial biocatalysts in the coming years.

A non-canonical nucleophile unlocks a new mechanistic pathway in a designed enzyme

Directed evolution of computationally designed enzymes has provided new insights into the emergence of sophisticated catalytic sites in proteins. In this regard, we have recently shown that a histidine nucleophile and a flexible arginine can work in synergy to accelerate the Morita-Baylis-Hillman (MBH) reaction with unrivalled efficiency. Here, we show that replacing the catalytic histidine with a non-canonical Nδ-methylhistidine (MeHis23) nucleophile leads to a substantially altered evolutionary outcome in which the catalytic Arg124 has been abandoned. Instead, Glu26 has emerged, which mediates a rate-limiting proton transfer step to deliver an enzyme (BHMeHis1.8) that is more than an order of magnitude more active than our earlier MBHase. Interestingly, although MeHis23 to His substitution in BHMeHis1.8 reduces activity by 4-fold, the resulting His containing variant is still a potent MBH biocatalyst. However, analysis of the BHMeHis1.8 evolutionary trajectory reveals that the MeHis nucleophile was crucial in the early stages of engineering to unlock the new mechanistic pathway. This study demonstrates how even subtle perturbations to key catalytic elements of designed enzymes can lead to vastly different evolutionary outcomes, resulting in new mechanistic solutions to complex chemical transformations.

Enzymatic Synthesis of Noncanonical α-Amino Acids Containing γ-Tertiary Alcohols

Noncanonical amino acids (ncAAs) containing tertiary alcohols are valuable as precursors of natural products and active pharmaceutical ingredients. However, the assembly of such ncAA scaffolds from simple material by C-C bond formation remains a challenging task due to the presence of multiple stereocenters and large steric hindrance. In this study, we present a novel solution to this problem through highly selective enzymatic decarboxylative aldol addition. This method allows for the streamlined assembly of multifunctionalized ncAAs with γ-tertiary alcohols from readily available materials, such as L -aspartatic acid and isatins, vicinal diones and keto esters. The modularity of electrophiles furnished four classes of ncAAs with decent efficiency as well as excellent site and stereocontrol. Computational modeling was employed to gain detailed insight into the catalytic mechanism and to provide a rationale for the observed selectivities. The method offers a single-step approach to producing multifunctionalized ncAAs, which can be directly utilized in peptide synthesis and bioactivity assessment.

Diastereodivergent α-Homoallylation of Cyclic Enones

α,β-Unsaturated carbonyls are essential structural motifs for nucleophiles of disease-related proteins. Methods for stereoselective functionalizations at the α-position include the Morita-Baylis-Hillman, Negishi, Sonogashira, Stille, and Rauhut-Currier reactions. Described here is a method for the diastereodivergent α-homoallylation of cyclic enones via a sequence of conjugate addition, aldol condensation, and diastereoselective [3,3]-sigmatropic rearrangement. Mechanistic investigations revealed that the [3,3]-sigmatropic rearrangement proceeds with transfer of chirality. These inspire a photocatalyzed olefin isomerization of the aldol condensation product leading to a highly diastereoselective [3,3]-sigmatropic rearrangement to furnish the α-homoallylation of cyclic enones. Importantly, this photocatalyzed olefin isomerization/diastereoselective [3,3]-sigmatropic rearrangement reaction sequence permits a full stereocontrol of the exo-β-position featuring an allyl group as a synthetic functional handle.

From nature to industry: Harnessing enzymes for biocatalysis

Biocatalysis harnesses enzymes to make valuable products. This green technology is used in countless applications from bench scale to industrial production and allows practitioners to access complex organic molecules, often with fewer synthetic steps and reduced waste. The last decade has seen an explosion in the development of experimental and computational tools to tailor enzymatic properties, equipping enzyme engineers with the ability to create biocatalysts that perform reactions not present in nature. By using (chemo)-enzymatic synthesis routes or orchestrating intricate enzyme cascades, scientists can synthesize elaborate targets ranging from DNA and complex pharmaceuticals to starch made in vitro from CO2-derived methanol. In addition, new chemistries have emerged through the combination of biocatalysis with transition metal catalysis, photocatalysis, and electrocatalysis. This review highlights recent key developments, identifies current limitations, and provides a future prospect for this rapidly developing technology.

Engineering of Therapeutic and Detoxifying Enzymes

Therapeutic enzymes present excellent opportunities for the treatment of human disease, modulation of metabolic pathways and system detoxification. However, current use of enzyme therapy in the clinic is limited as naturally occurring enzymes are seldom optimal for such applications and require substantial improvement by protein engineering. Engineering strategies such as design and directed evolution that have been successfully implemented for industrial biocatalysis can significantly advance the field of therapeutic enzymes, leading to biocatalysts with new-to-nature therapeutic activities, high selectivity, and suitability for medical applications. This minireview highlights case studies of how state-of-the-art and emerging methods in protein engineering are explored for the generation of therapeutic enzymes and discusses gaps and future opportunities in the field of enzyme therapy.

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.

Kaleidoscope megamolecules synthesis and application using self-assembly technology

The megamolecules with high ordered structures play an important role in chemical biology and biomedical engineering. Self-assembly, a long-discovered but very appealing technique, could induce many reactions between biomacromolecules and organic linking molecules, such as an enzyme domain and its covalent inhibitors. Enzyme and its small-molecule inhibitors have achieved many successes in medical application, which realize the catalysis process and theranostic function. By employing the protein engineering technology, the building blocks of enzyme fusion protein and small molecule linker can be assembled into a novel architecture with the specified organization and conformation. Molecular level recognition of enzyme domain could provide both covalent reaction sites and structural skeleton for the functional fusion protein. In this review, we will discuss the range of tools available to combine functional domains by using the recombinant protein technology, which can assemble them into precisely specified architectures/valences and develop the kaleidoscope megamolecules for catalytic and medical application.

Using High-Throughput Molecular Dynamics Simulation to Enhance the Computational Design of Kemp Elimination Enzymes

Computational enzyme design has been successfully applied to identify new alternatives to natural enzymes for the biosynthesis of important compounds. However, the moderate catalytic activities of de novo designed enzymes indicate that the modeling accuracy of current computational enzyme design methods should be improved. Here, high-throughput molecular dynamics simulations were used to enhance computational enzyme design, thus allowing the identification of variants with higher activities in silico. Different time schemes of high-throughput molecular dynamics simulations were tested to identify the catalytic features of evolved Kemp eliminases. The 20 × 1 ns molecular dynamics simulation scheme was sufficiently accurate and computationally viable to screen the computationally designed massive variants of Kemp elimination enzymes. The developed hybrid computational strategy was used to redesign the most active Kemp eliminase, HG3.17, and five variants were generated and experimentally confirmed to afford higher catalytic efficiencies than that of HG3.17, with one double variant (D52Q/A53S) exhibiting a 55% increase. The hybrid computational enzyme design strategy is general and computationally economical, with which we anticipate the efficient creation of practical enzymes for industrial biocatalysis.

Mechanistic Insights into the Ene-Reductase-Catalyzed Promiscuous Reduction of Oximes to Amines

The biocatalytic reduction of the oxime moiety to the corresponding amine group has only recently been found to be a promiscuous activity of ene-reductases transforming α-oximo β-keto esters. However, the reaction pathway of this two-step reduction remained elusive. By studying the crystal structures of enzyme oxime complexes, analyzing molecular dynamics simulations, and investigating biocatalytic cascades and possible intermediates, we obtained evidence that the reaction proceeds via an imine intermediate and not via the hydroxylamine intermediate. The imine is reduced further by the ene-reductase to the amine product. Remarkably, a non-canonical tyrosine residue was found to contribute to the catalytic activity of the ene-reductase OPR3, protonating the hydroxyl group of the oxime in the first reduction step.

The Time and Place for Nature in Drug Discovery

The case for a renewed focus on Nature in drug discovery is reviewed; not in terms of natural product screening, but how and why biomimetic molecules, especially those produced by natural processes, should deliver in the age of artificial intelligence and screening of vast collections both in vitro and in silico. The declining natural product-likeness of licensed drugs and the consequent physicochemical implications of this trend in the context of current practices are noted. To arrest these trends, the logic of seeking new bioactive agents with enhanced natural mimicry is considered; notably that molecules constructed by proteins (enzymes) are more likely to interact with other proteins (e.g., targets and transporters), a notion validated by natural products. Nature's finite number of building blocks and their interactions necessarily reduce potential numbers of structures, yet these enable expansion of chemical space with their inherent diversity of physical characteristics, pertinent to property-based design. The feasible variations on natural motifs are considered and expanded to encompass pseudo-natural products, leading to the further logical step of harnessing bioprocessing routes to access them. Together, these offer opportunities for enhancing natural mimicry, thereby bringing innovation to drug synthesis exploiting the characteristics of natural recognition processes. The potential for computational guidance to help identifying binding commonalities in the route map is a logical opportunity to enable the design of tailored molecules, with a focus on "organic/biological" rather than purely "synthetic" structures. The design and synthesis of prototype structures should pay dividends in the disposition and efficacy of the molecules, while inherently enabling greener and more sustainable manufacturing techniques.

Engineering enzyme activity using an expanded amino acid alphabet

Enzyme design and engineering strategies are typically constrained by the limited size of nature's genetic alphabet, comprised of only 20 canonical amino acids. In recent years, site-selective incorporation of non-canonical amino acids (ncAAs) via an expanded genetic code has emerged as a powerful means of inserting new functional components into proteins, with hundreds of structurally diverse ncAAs now available. Here, we highlight how the emergence of an expanded repertoire of amino acids has opened new avenues in enzyme design and engineering. ncAAs have been used to probe complex biological mechanisms, augment enzyme function and, most ambitiously, embed new catalytic mechanisms into protein active sites that would be challenging to access within the constraints of nature's genetic code. We predict that the studies reviewed in this article, along with further advances in genetic code expansion technology, will establish ncAA incorporation as an increasingly important tool for biocatalysis in the coming years.

Efficient Base-Catalyzed Kemp Elimination in an Engineered Ancestral Enzyme

The routine generation of enzymes with completely new active sites is a major unsolved problem in protein engineering. Advances in this field have thus far been modest, perhaps due, at least in part, to the widespread use of modern natural proteins as scaffolds for de novo engineering. Most modern proteins are highly evolved and specialized and, consequently, difficult to repurpose for completely new functionalities. Conceivably, resurrected ancestral proteins with the biophysical properties that promote evolvability, such as high stability and conformational diversity, could provide better scaffolds for de novo enzyme generation. Kemp elimination, a non-natural reaction that provides a simple model of proton abstraction from carbon, has been extensively used as a benchmark in de novo enzyme engineering. Here, we present an engineered ancestral β-lactamase with a new active site that is capable of efficiently catalyzing Kemp elimination. The engineering of our Kemp eliminase involved minimalist design based on a single function-generating mutation, inclusion of an extra polypeptide segment at a position close to the de novo active site, and sharply focused, low-throughput library screening. Nevertheless, its catalytic parameters (k_cat/K_M~2·10⁵ M⁻¹ s⁻¹, k_cat~635 s⁻¹) compare favorably with the average modern natural enzyme and match the best proton-abstraction de novo Kemp eliminases that are reported in the literature. The general implications of our results for de novo enzyme engineering are discussed.

Redesigning Enzymes for Biocatalysis: Exploiting Structural Understanding for Improved Selectivity

The discovery of new enzymes, alongside the push to make chemical processes more sustainable, has resulted in increased industrial interest in the use of biocatalytic processes to produce high-value and chiral precursor chemicals. Huge strides in protein engineering methodology and in silico tools have facilitated significant progress in the discovery and production of enzymes for biocatalytic processes. However, there are significant gaps in our knowledge of the relationship between enzyme structure and function. This has demonstrated the need for improved computational methods to model mechanisms and understand structure dynamics. Here, we explore efforts to rationally modify enzymes toward changing aspects of their catalyzed chemistry. We highlight examples of enzymes where links between enzyme function and structure have been made, thus enabling rational changes to the enzyme structure to give predictable chemical outcomes. We look at future directions the field could take and the technologies that will enable it.

The road to fully programmable protein catalysis

The ability to design efficient enzymes from scratch would have a profound effect on chemistry, biotechnology and medicine. Rapid progress in protein engineering over the past decade makes us optimistic that this ambition is within reach. The development of artificial enzymes containing metal cofactors and noncanonical organocatalytic groups shows how protein structure can be optimized to harness the reactivity of nonproteinogenic elements. In parallel, computational methods have been used to design protein catalysts for diverse reactions on the basis of fundamental principles of transition state stabilization. Although the activities of designed catalysts have been quite low, extensive laboratory evolution has been used to generate efficient enzymes. Structural analysis of these systems has revealed the high degree of precision that will be needed to design catalysts with greater activity. To this end, emerging protein design methods, including deep learning, hold particular promise for improving model accuracy. Here we take stock of key developments in the field and highlight new opportunities for innovation that should allow us to transition beyond the current state of the art and enable the robust design of biocatalysts to address societal needs.

Morita-Baylis-Hillman Reaction: How Do Optimal Enzyme Active Sites Compare with Organocatalysts

Morita-Baylis-Hillman reaction attracts significant attention for the synthesis of highly functionalized compounds. It requires multiple catalytic elements for efficient catalysis, making it an appealing target for the design of a...