The road to fully programmable protein catalysis

Engineering the next-generation synthetic cell factory driven by protein engineering

Synthetic cell factory offers substantial advantages in economically efficient production of biofuels, chemicals, and pharmaceutical compounds. However, to create a high-performance synthetic cell factory, precise regulation of cellular material and energy flux is essential. In this context, protein components including enzymes, transcription factor-based biosensors and transporters play pivotal roles. Protein engineering aims to create novel protein variants with desired properties by modifying or designing protein sequences. This review focuses on summarizing the latest advancements of protein engineering in optimizing various aspects of synthetic cell factory, including: enhancing enzyme activity to eliminate production bottlenecks, altering enzyme selectivity to steer metabolic pathways towards desired products, modifying enzyme promiscuity to explore innovative routes, and improving the efficiency of transporters. Furthermore, the utilization of protein engineering to modify protein-based biosensors accelerates evolutionary process and optimizes the regulation of metabolic pathways. The remaining challenges and future opportunities in this field are also discussed.

Directed Evolution of Protoglobin Optimizes the Enzyme Electric Field

To unravel why computational design fails in creating viable enzymes, while directed evolution (DE) succeeds, our research delves into the laboratory evolution of protoglobin. DE has adapted this protein to efficiently catalyze carbene transfer reactions. We show that the previously proposed enhanced substrate access and binding alone cannot account for increased yields during DE. The 3D electric field in the entire active site is tracked through protein dynamics, clustered using the affinity propagation algorithm, and subjected to principal component analysis. This analysis reveals notable changes in the electric field with DE, where distinct field topologies influence transition state energetics and mechanism. A chemically meaningful field component emerges and takes the lead during DE and facilitates crossing the barrier to carbene transfer. Our findings underscore intrinsic electric field dynamic's influence on enzyme function, the ability of the field to switch mechanisms within the same protein, and the crucial role of the field in enzyme design.

Computation-guided engineering of distal mutations in an artificial enzyme

Artificial enzymes are valuable biocatalysts able to perform new-to-nature transformations with the precision and (enantio-)selectivity of natural enzymes. Although they are highly engineered biocatalysts, they often cannot reach catalytic rates akin those of their natural counterparts, slowing down their application in real-world industrial processes. Typically, their designs only optimise the chemistry inside the active site, while overlooking the role of protein dynamics on catalysis. In this work, we show how the catalytic performance of an already engineered artificial enzyme can be further improved by distal mutations that affect the conformational equilibrium of the protein. To this end, we subjected a specialised artificial enzyme based on the lactococcal multidrug resistance regulator (LmrR) to an innovative algorithm that quickly inspects the whole protein sequence space for hotpots which affect the protein dynamics. From an initial predicted selection of 73 variants, two variants with mutations distant by more than 11 Å from the catalytic pAF residue showed increased catalytic activity towards the new-to-nature hydrazone formation reaction. Their recombination displayed a 66% higher turnover number and 14 °C higher thermostability. Microsecond time scale molecular dynamics simulations evidenced a shift in the distribution of productive enzyme conformations, which are the result of a cascade of interactions initiated by the introduced mutations.

Modular Access to Chiral Amines via Imine Reductase-Based Photoenzymatic Catalysis

The development of catalysts serves as the cornerstone of innovation in synthesis, as exemplified by the recent discovery of photoenzymes. However, the repertoire of naturally occurring enzymes repurposed by direct light excitation to catalyze new-to-nature photobiotransformations is currently limited to flavoproteins and keto-reductases. Herein, we shed light on imine reductases (IREDs) that catalyze the remote C(sp3)-C(sp3) bond formation, providing a previously elusive radical hydroalkylation of enamides for accessing chiral amines (45 examples with up to 99% enantiomeric excess). Beyond their natural function in catalyzing two-electron reductive amination reactions, upon direct visible-light excitation or in synergy with a synthetic photoredox catalyst, IREDs are repurposed to tune the non-natural photoinduced single-electron radical processes. By conducting wet mechanistic experiments and computational simulations, we unravel how engineered IREDs direct radical intermediates toward the productive and enantioselective pathway. This work represents a promising paradigm for harnessing nature's catalysts for new-to-nature asymmetric transformations that remain challenging through traditional chemocatalytic methods.

Redox-reversible siderophore-based catalyst anchoring within cross-linked artificial metalloenzyme aggregates enables enantioselectivity switching

The immobilisation of artificial metalloenzymes (ArMs) holds promise for the implementation of new biocatalytic reactions. We present the synthesis of cross-linked artificial metalloenzyme aggregates (CLArMAs) with excellent recyclability, as an alternative to carrier-based immobilisation strategies. Furthermore, iron-siderophore supramolecular anchoring facilitates redox-triggered cofactor release, enabling CLArMAs to be recharged with alternative cofactors for diverse selectivity.

A role for conformational changes in enzyme catalysis

The role played by conformational changes in enzyme catalysis is controversial. In addition to examining specific enzymes, studying formal models can help identify the conditions under which conformational changes promote catalysis. Here, we present a model demonstrating how conformational changes can break a generic trade-off due to the conflicting requirements of successive steps in catalytic cycles, namely high specificity for the transition state to accelerate the chemical transformation and low affinity for the products to favor their release. The mechanism by which the trade-off is broken is a transition between conformations with different affinities for the substrate. The role of the effector that induces the transition is played by a substrate "handle," a part of the substrate that is not chemically transformed but whose interaction with the enzyme is nevertheless essential to rapidly complete the catalytic cycle. A key element of the model is the formalization of the constraints causing the trade-off that the presence of multiple states breaks, which we attribute to the strong chemical similarity between successive reaction states-substrates, transition states, and products. For the sake of clarity, we present our model for irreversible one-step unimolecular reactions. In this context, we demonstrate how the different forms that chemical similarities between reaction states can take impose limits on the overall catalytic turnover. We first analyze catalysts without internal degrees of freedom and then show how two-state catalysts can overcome their limitations. Our results recapitulate previous proposals concerning the role of conformational changes and substrate handles in a formalism that makes explicit the constraints that elicit these features. In addition, our approach establishes links with studies in the field of heterogeneous catalysis, where the same trade-offs are observed and where overcoming them is a well-recognized challenge.

Boron catalysis in a designer enzyme

Enzymes play an increasingly important role in improving the benignity and efficiency of chemical production, yet the diversity of their applications lags heavily behind chemical catalysts as a result of the relatively narrow range of reaction mechanisms of enzymes. The creation of enzymes containing non-biological functionalities facilitates reaction mechanisms outside nature's canon and paves the way towards fully programmable biocatalysis1-3. Here we present a completely genetically encoded boronic-acid-containing designer enzyme with organocatalytic reactivity not achievable with natural or engineered biocatalysts4,5. This boron enzyme catalyses the kinetic resolution of hydroxyketones by oxime formation, in which crucial interactions with the protein scaffold assist in the catalysis. A directed evolution campaign led to a variant with natural-enzyme-like enantioselectivities for several different substrates. The unique activation mode of the boron enzyme was confirmed using X-ray crystallography, high-resolution mass spectrometry (HRMS) and 11B NMR spectroscopy. Our study demonstrates that genetic-code expansion can be used to create evolvable enantioselective enzymes that rely on xenobiotic catalytic moieties such as boronic acids and access reaction mechanisms not reachable through catalytic promiscuity of natural or engineered enzymes.

Polymorphic Self-Assembly with Procedural Flexibility for Monodisperse Quaternary Protein Structures of DegQ Enzymes

As large molecular tertiary structures, some proteins can act as small robots that find, bind, and chaperone target protein clients, showing the potential to serve as smart building blocks in self-assembly fields. Instead of using such intrinsic functions, most self-assembly methodologies for proteins aim for de novo-designed structures with accurate geometric assemblies, which can limit procedural flexibility. Here, a strategy enabling polymorphic clustering of quaternary proteins, exhibiting simplicity and flexibility of self-assembling paths for proteins in forming monodisperse quaternary cage particles is presented. It is proposed that the enzyme protomer DegQ, previously solved at low resolution, may potentially be usable as a threefold symmetric building block, which can form polyhedral cages incorporated by the chaperone action of DegQ in the presence of protein clients. To obtain highly monodisperse cage particles, soft, and hence, less resistive client proteins, which can program the inherent chaperone activity of DegQ to efficient formations of polymorphic cages, depending on the size of clients are utilized. By reconstructing the atomic resolution cryogenic electron microscopy DegQ structures using obtained 12- and 24-meric clusters, the polymorphic clustering of DegQ enzymes is validated in terms of soft and rigid domains, which will provide effective routes for protein self-assemblies with procedural flexibility.

Protein engineering using circular permutation - structure, function, stability, and applications

Protein engineering is important for creating novel variants from natural proteins, enabling a wide range of applications. Approaches such as rational design and directed evolution are routinely used to make new protein variants. Computational tools like de novo design can introduce new protein folds. Expanding the amino acid repertoire to include unnatural amino acids with non-canonical side chains in vitro by native chemical ligation and in vivo via codon expansion methods broadens sequence and structural possibilities. Circular permutation (CP) is an invaluable approach to redesigning a protein by rearranging the amino acid sequence, where the connectivity of the secondary structural elements is altered without changing the overall structure of the protein. Artificial CP proteins (CPs) are employed in various applications such as biocatalysis, sensing of small molecules by fluorescence, genome editing, ligand-binding protein switches, and optogenetic engineering. Many studies have shown that CP can lead to either reduced or enhanced stability or catalytic efficiency. The effects of CP on a protein's energy landscape cannot be predicted a priori. Thus, it is important to understand how CP can affect the thermodynamic and kinetic stability of a protein. In this review, we discuss the discovery and advancement of techniques to create protein CP, and existing reviews on CP. We delve into the plethora of biological applications for designed CP proteins. We subsequently discuss the experimental and computational reports on the effects of CP on the thermodynamic and kinetic stabilities of proteins of various topologies. An understanding of the various aspects of CP will allow the reader to design robust CP proteins for their specific purposes.

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.

The genetic landscape of a metabolic interaction

While much prior work has explored the constraints on protein sequence and evolution induced by physical protein-protein interactions, the sequence-level constraints emerging from non-binding functional interactions in metabolism remain unclear. To quantify how variation in the activity of one enzyme constrains the biochemical parameters and sequence of another, we focus on dihydrofolate reductase (DHFR) and thymidylate synthase (TYMS), a pair of enzymes catalyzing consecutive reactions in folate metabolism. We use deep mutational scanning to quantify the growth rate effect of 2696 DHFR single mutations in 3 TYMS backgrounds under conditions selected to emphasize biochemical epistasis. Our data are well-described by a relatively simple enzyme velocity to growth rate model that quantifies how metabolic context tunes enzyme mutational tolerance. Together our results reveal the structural distribution of epistasis in a metabolic enzyme and establish a foundation for the design of multi-enzyme systems.

Carving out a Glycoside Hydrolase Active Site for Incorporation into a New Protein Scaffold Using Deep Network Hallucination

Enzymes are indispensable biocatalysts for numerous industrial applications, yet stability, selectivity, and restricted substrate recognition present limitations for their use. Despite the importance of enzyme engineering in overcoming these limitations, success is often challenged by the intricate architecture of enzymes derived from natural sources. Recent advances in computational methods have enabled the de novo design of simplified scaffolds with specific functional sites. Such scaffolds may be advantageous as platforms for enzyme engineering. Here, we present a strategy for the de novo design of a simplified scaffold of an endo-α-N-acetylgalactosaminidase active site, a glycoside hydrolase from the GH101 enzyme family. Using a combination of trRosetta hallucination, iterative cycles of deep-learning-based structure prediction, and ProteinMPNN sequence design, we designed proteins with 290 amino acids incorporating the active site while reducing the molecular weight by over 100 kDa compared to the initial endo-α-N-acetylgalactosaminidase. Of 11 tested designs, six were expressed as soluble monomers, displaying similar or increased thermostabilities compared to the natural enzyme. Despite lacking detectable enzymatic activity, the experimentally determined crystal structures of a representative design closely matched the design with a root-mean-square deviation of 1.0 Å, with most catalytically important side chains within 2.0 Å. The results highlight the potential of scaffold hallucination in designing proteins that may serve as a foundation for subsequent enzyme engineering.

An artificial protein modulator reprogramming neuronal protein functions

Reversible protein phosphorylation, regulated by protein phosphatases, fine-tunes target protein function and plays a vital role in biological processes. Dysregulation of this process leads to aberrant post-translational modifications (PTMs) and contributes to disease development. Despite the widespread use of artificial catalysts as enzyme mimetics, their direct modulation of proteins remains largely unexplored. To address this gap and enable the reversal of aberrant PTMs for disease therapy, we present the development of artificial protein modulators (APROMs). Through atomic-level engineering of heterogeneous catalysts with asymmetric catalytic centers, these modulators bear structural similarities to protein phosphatases and exhibit remarkable ability to destabilize the bridging μ3-hydroxide. This activation of catalytic centers enables spontaneous hydrolysis of phospho-substrates, providing precise control over PTMs. Notably, APROMs, with protein phosphatase-like characteristics, catalytically reprogram the biological function of α-synuclein by directly hydrolyzing hyperphosphorylated α-synuclein. Consequently, synaptic function is reinforced in Parkinson's disease. Our findings offer a promising avenue for reprogramming protein function through de novo PTMs strategy.

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.

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

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

EvoAI enables extreme compression and reconstruction of the protein sequence space

Designing proteins with improved functions requires a deep understanding of how sequence and function are related, a vast space that is hard to explore. The ability to efficiently compress this space by identifying functionally important features is extremely valuable. Here, we first establish a method called EvoScan to comprehensively segment and scan the high-fitness sequence space to obtain anchor points that capture its essential features, especially in high dimensions. Our approach is compatible with any biomolecular function that can be coupled to a transcriptional output. We then develop deep learning and large language models to accurately reconstruct the space from these anchors, allowing computational prediction of novel, highly fit sequences without prior homology-derived or structural information. We apply this hybrid experimental-computational method, which we call EvoAI, to a repressor protein and find that only 82 anchors are sufficient to compress the high-fitness sequence space with a compression ratio of 1048. The extreme compressibility of the space informs both applied biomolecular design and understanding of natural evolution.

Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development

Understanding enzyme mechanisms is essential for unraveling the complex molecular machinery of life. In this review, we survey the field of computational enzymology, highlighting key principles governing enzyme mechanisms and discussing ongoing challenges and promising advances. Over the years, computer simulations have become indispensable in the study of enzyme mechanisms, with the integration of experimental and computational exploration now established as a holistic approach to gain deep insights into enzymatic catalysis. Numerous studies have demonstrated the power of computer simulations in characterizing reaction pathways, transition states, substrate selectivity, product distribution, and dynamic conformational changes for various enzymes. Nevertheless, significant challenges remain in investigating the mechanisms of complex multistep reactions, large-scale conformational changes, and allosteric regulation. Beyond mechanistic studies, computational enzyme modeling has emerged as an essential tool for computer-aided enzyme design and the rational discovery of covalent drugs for targeted therapies. Overall, enzyme design/engineering and covalent drug development can greatly benefit from our understanding of the detailed mechanisms of enzymes, such as protein dynamics, entropy contributions, and allostery, as revealed by computational studies. Such a convergence of different research approaches is expected to continue, creating synergies in enzyme research. This review, by outlining the ever-expanding field of enzyme research, aims to provide guidance for future research directions and facilitate new developments in this important and evolving field.

Reagent Engineering for Group Transfer Biocatalysis

Biocatalysis has become a major driver in the innovation of preparative chemistry. Enzyme discovery, engineering and computational design have matured to reliable strategies in the development of biocatalytic processes. By comparison, substrate engineering has received much less attention. In this Minireview, we highlight the idea that the design of synthetic reagents may be an equally fruitful and complementary approach to develop novel enzyme-catalysed group transfer chemistry. This Minireview discusses key examples from the literature that illustrate how synthetic substrates can be devised to improve the efficiency, scalability and sustainability, as well as the scope of such reactions. We also provide an opinion as to how this concept might be further developed in the future, aspiring to replicate the evolutionary success story of natural group transfer reagents, such as adenosine triphosphate (ATP) and S-adenosyl methionine (SAM).

Mass spectrometry imaging of metals in tissues and cells: Methods and biological applications

BACKGROUND

Metals are pervasive throughout biological processes, where they play essential structural and catalytic roles. Metals can also exhibit deleterious effects on human health. Powerful analytical techniques, such as mass spectrometry imaging (MSI), are required to map metals due to their low concentrations within biological tissue.

SCOPE OF REVIEW

This Mini Review focuses on key MSI technology that can image metal distributions in situ, describing considerations for each technique (e.g., resolution, sensitivity, etc.). We highlight recent work using MSI for mapping trace metals in tissues, detecting metal-based drugs, and simultaneously imaging metals and biomolecules.

MAJOR CONCLUSIONS

MSI has enabled significant advances in locating bioactive metals at high spatial resolution and correlating their distributions with that of biomolecules. The use of metal-based immunochemistry has enabled simultaneous high-throughput protein and biomolecule imaging.

GENERAL SIGNIFICANCE

The techniques and examples described herein can be applied to many biological questions concerning the important biological roles of metals, metal toxicity, and localization of metal-based drugs.

Data Storage Using DNA

The exponential growth of global data has outpaced the storage capacities of current technologies, necessitating innovative storage strategies. DNA, as a natural medium for preserving genetic information, has emerged as a highly promising candidate for next-generation storage medium. Storing data in DNA offers several advantages, including ultrahigh physical density and exceptional durability. Facilitated by significant advancements in various technologies, such as DNA synthesis, DNA sequencing, and DNA nanotechnology, remarkable progress has been made in the field of DNA data storage over the past decade. However, several challenges still need to be addressed to realize practical applications of DNA data storage. In this review, the processes and strategies of in vitro DNA data storage are first introduced, highlighting recent advancements. Next, a brief overview of in vivo DNA data storage is provided, with a focus on the various writing strategies developed to date. At last, the challenges encountered in each step of DNA data storage are summarized and promising techniques are discussed that hold great promise in overcoming these obstacles.

Data-Driven Protein Engineering for Improving Catalytic Activity and Selectivity

Protein engineering is essential for altering the substrate scope, catalytic activity and selectivity of enzymes for applications in biocatalysis. However, traditional approaches, such as directed evolution and rational design, encounter the challenge in dealing with the experimental screening process of a large protein mutation space. Machine learning methods allow the approximation of protein fitness landscapes and the identification of catalytic patterns using limited experimental data, thus providing a new avenue to guide protein engineering campaigns. In this concept article, we review machine learning models that have been developed to assess enzyme-substrate-catalysis performance relationships aiming to improve enzymes through data-driven protein engineering. Furthermore, we prospect the future development of this field to provide additional strategies and tools for achieving desired activities and selectivities.

De novo protein design-From new structures to programmable functions

Methods from artificial intelligence (AI) trained on large datasets of sequences and structures can now "write" proteins with new shapes and molecular functions de novo, without starting from proteins found in nature. In this Perspective, I will discuss the state of the field of de novo protein design at the juncture of physics-based modeling approaches and AI. New protein folds and higher-order assemblies can be designed with considerable experimental success rates, and difficult problems requiring tunable control over protein conformations and precise shape complementarity for molecular recognition are coming into reach. Emerging approaches incorporate engineering principles-tunability, controllability, and modularity-into the design process from the beginning. Exciting frontiers lie in deconstructing cellular functions with de novo proteins and, conversely, constructing synthetic cellular signaling from the ground up. As methods improve, many more challenges are unsolved.

Computational Design of Peptide Assemblies

With the ongoing development of peptide self-assembling materials, there is growing interest in exploring novel functional peptide sequences. From short peptides to long polypeptides, as the functionality increases, the sequence space is also expanding exponentially. Consequently, attempting to explore all functional sequences comprehensively through experience and experiments alone has become impractical. By utilizing computational methods, especially artificial intelligence enhanced molecular dynamics (MD) simulation and de novo peptide design, there has been a significant expansion in the exploration of sequence space. Through these methods, a variety of supramolecular functional materials, including fibers, two-dimensional arrays, nanocages, etc., have been designed by meticulously controlling the inter- and intramolecular interactions. In this review, we first provide a brief overview of the current main computational methods and then focus on the computational design methods for various self-assembled peptide materials. Additionally, we introduce some representative protein self-assemblies to offer guidance for the design of self-assembling peptides.

Photocatalyzed Enantioselective Functionalization of C(sp3)-H Bonds

Owing to its diverse activation processes including single-electron transfer (SET) and hydrogen-atom transfer (HAT), visible-light photocatalysis has emerged as a sustainable and efficient platform for organic synthesis. These processes provide a powerful avenue for the direct functionalization of C(sp3)-H bonds under mild conditions. Over the past decade, there have been remarkable advances in the enantioselective functionalization of the C(sp3)-H bond via photocatalysis combined with conventional asymmetric catalysis. Herein, we summarize the advances in asymmetric C(sp3)-H functionalization involving visible-light photocatalysis and discuss two main pathways in this emerging field: (a) SET-driven carbocation intermediates are followed by stereospecific nucleophile attacks; and (b) photodriven alkyl radical intermediates are further enantioselectively captured by (i) chiral π-SOMOphile reagents, (ii) stereoselective transition-metal complexes, and (iii) another distinct stereoscopic radical species. We aim to summarize key advances in reaction design, catalyst development, and mechanistic understanding, to provide new insights into this rapidly evolving area of research.

Microbial alcohol dehydrogenases: recent developments and applications in asymmetric synthesis

Alcohol dehydrogenases are a well-known group of enzymes in the class of oxidoreductases that use electron transfer cofactors such as NAD(P)+/NAD(P)H for oxidation or reduction reactions of alcohols or carbonyl compounds respectively. These enzymes are utilized mainly as purified enzymes and offer some advantages in terms of green chemistry. They are environmentally friendly and a sustainable alternative to traditional chemical synthesis of bulk and fine chemicals. Industry has implemented several whole-cell biocatalytic processes to synthesize pharmaceutically active ingredients by exploring the high selectivity of enzymes. Unlike the whole cell system where cofactor regeneration is well conserved within the cellular environment, purified enzymes require additional cofactors or a cofactor recycling system in the reaction, even though cleaner reactions can be carried out with fewer downstream work-up problems. The challenge of producing purified enzymes in large quantities has been solved in large part by the use of recombinant enzymes. Most importantly, recombinant enzymes find applications in many cascade biotransformations to produce several important chiral precursors. Inevitably, several dehydrogenases were engineered as mere recombinant enzymes could not meet the industrial requirements for substrate and stereoselectivity. In recent years, a significant number of engineered alcohol dehydrogenases have been employed in asymmetric synthesis in industry. In a parallel development, several enzymatic and non-enzymatic methods have been established for regenerating expensive cofactors (NAD+/NADP+) to make the overall enzymatic process more efficient and economically viable. In this review article, recent developments and applications of microbial alcohol dehydrogenases are summarized by emphasizing notable examples.

An artificial protein cage made from a 12-membered ring

Artificial protein cages have great potential in diverse fields including as vaccines and drug delivery vehicles. TRAP-cage is an artificial protein cage notable for the way in which the interface between its ring-shaped building blocks can be modified such that the conditions under which cages disassemble can be controlled. To date, TRAP-cages have been constructed from homo-11mer rings, i.e., hendecamers. This is interesting as convex polyhedra with identical regular faces cannot be formed from hendecamers. TRAP-cage overcomes this limitation due to intrinsic flexibility, allowing slight deformation to absorb any error. The resulting TRAP-cage made from 24 TRAP 11mer rings is very close to regular with only very small errors necessary to allow the cage to form. The question arises as to the limits of the error that can be absorbed by a protein structure in this way before the formation of an apparently regular convex polyhedral becomes impossible. Here we use a naturally occurring TRAP variant consisting of twelve identical monomers (i.e., a dodecamer) to probe these limits. We show that it is able to form an apparently regular protein cage consisting of twelve TRAP rings. Comparison of the cryo-EM structure of the new cage with theoretical models and related cages gives insight into the rules of cage formation and allows us to predict other cages that may be formed given TRAP-rings consisting of different numbers of monomers.

Enzyme engineering for functional lipids synthesis: recent advance and perspective

Functional lipids, primarily derived through the modification of natural lipids by various processes, are widely acknowledged for their potential to impart health benefits. In contrast to chemical methods for lipid modification, enzymatic catalysis offers distinct advantages, including high selectivity, mild operating conditions, and reduced byproduct formation. Nevertheless, enzymes face challenges in industrial applications, such as low activity, stability, and undesired selectivity. To address these challenges, protein engineering techniques have been implemented to enhance enzyme performance in functional lipid synthesis. This article aims to review recent advances in protein engineering, encompassing approaches from directed evolution to rational design, with the goal of improving the properties of lipid-modifying enzymes. Furthermore, the article explores the future prospects and challenges associated with enzyme-catalyzed functional lipid synthesis.

Artificial carbon assimilation: From unnatural reactions and pathways to synthetic autotrophic systems

Synthetic biology is being increasingly used to establish novel carbon assimilation pathways and artificial autotrophic strains that can be used in low-carbon biomanufacturing. Currently, artificial pathway design has made significant progress from advocacy to practice within a relatively short span of just over ten years. However, there is still huge scope for exploration of pathway diversity, operational efficiency, and host suitability. The accelerated research process will bring greater opportunities and challenges. In this paper, we provide a comprehensive summary and interpretation of representative one-carbon assimilation pathway designs and artificial autotrophic strain construction work. In addition, we propose some feasible design solutions based on existing research results and patterns to promote the development and application of artificial autotrophy.

Choice of fusion proteins, expression host, and analytics solves difficult-to-produce protein challenges in discovery research

High quality biological reagents are a prerequisite for pharmacological research. Herein a protein production screening approach, including quality assessment methods, for protein-based discovery research is presented. Trends from 2895 expression constructs representing 253 proteins screened in mammalian and bacterial hosts-91% of which are successfully expressed and purified-are discussed. Mammalian expression combined with the use of solubility-promoting fusion proteins is deemed suitable for most targets. Furthermore, cases utilizing stable cell line generation and choice of fusion protein for higher yield and quality of difficult-to-produce proteins (Leucine-rich repeat-containing G-protein coupled receptor 4 (LGR4) and Neurturin) are presented and discussed. In the case of Neurturin, choice of fusion protein impacted the target binding 80-fold. These results highlight the need for exploration of construct designs and careful Quality Control (QC) of difficult-to-produce protein reagents.

Encapsulation of lipase in zeolitic imidazolate framework-8 induced by polyethyleneimine to form a honeycomb structure with enhanced activity

Embedding an enzyme in the metal-organic frameworks (MOFs) gives good protection to the fragile enzyme. However, this may also restrain the enzyme activity because of the decreased substrate accessibility. Encapsulation of lipase AK from Pseudomonas fluorescens for preparing the enzyme-MOF composite (AK@ZIF-8-PEI) was performed through a new strategy based on polyethyleneimine and enzyme induced in-situ growth of zeolitic imidazolate framework-8 (ZIF-8). Characterizations indicate that AK@ZIF-8-PEI has a honeycomb structure and the hierarchical porosity formed during the preparation, which provides adequate mass transfer channels for catalytic applications. Activity evaluation shows that specific activity of AK@ZIF-8-PEI is 8-fold than the commercial lipase powder. AK@ZIF-8-PEI is demonstrated as an efficient catalyst in kinetic resolution of α-naphthol enantiomers through enantioselective transesterification. Within 12 h, the conversion and substrate enantiomeric excess (ees) reaches 49.8 % and 96.4 %, achieving an improved resolution than previous researches.

Engineering Enzymes for Environmental Sustainability

The development and implementation of sustainable catalytic technologies is key to delivering our net-zero targets. Here we review how engineered enzymes, with a focus on those developed using directed evolution, can be deployed to improve the sustainability of numerous processes and help to conserve our environment. Efficient and robust biocatalysts have been engineered to capture carbon dioxide (CO2) and have been embedded into new efficient metabolic CO2 fixation pathways. Enzymes have been refined for bioremediation, enhancing their ability to degrade toxic and harmful pollutants. Biocatalytic recycling is gaining momentum, with engineered cutinases and PETases developed for the depolymerization of the abundant plastic, polyethylene terephthalate (PET). Finally, biocatalytic approaches for accessing petroleum-based feedstocks and chemicals are expanding, using optimized enzymes to convert plant biomass into biofuels or other high value products. Through these examples, we hope to illustrate how enzyme engineering and biocatalysis can contribute to the development of cleaner and more efficient chemical industry.

Engineering Enzymes for Environmental Sustainability

The development and implementation of sustainable catalytic technologies is key to delivering our net-zero targets. Here we review how engineered enzymes, with a focus on those developed using directed evolution, can be deployed to improve the sustainability of numerous processes and help to conserve our environment. Efficient and robust biocatalysts have been engineered to capture carbon dioxide (CO2 ) and have been embedded into new efficient metabolic CO2 fixation pathways. Enzymes have been refined for bioremediation, enhancing their ability to degrade toxic and harmful pollutants. Biocatalytic recycling is gaining momentum, with engineered cutinases and PETases developed for the depolymerization of the abundant plastic, polyethylene terephthalate (PET). Finally, biocatalytic approaches for accessing petroleum-based feedstocks and chemicals are expanding, using optimized enzymes to convert plant biomass into biofuels or other high value products. Through these examples, we hope to illustrate how enzyme engineering and biocatalysis can contribute to the development of cleaner and more efficient chemical industry.

Advances in ligand-specific biosensing for structurally similar molecules

The specificity of biological systems makes it possible to develop biosensors targeting specific metabolites, toxins, and pollutants in complex medical or environmental samples without interference from structurally similar compounds. For the last two decades, great efforts have been devoted to creating proteins or nucleic acids with novel properties through synthetic biology strategies. Beyond augmenting biocatalytic activity, expanding target substrate scopes, and enhancing enzymes' enantioselectivity and stability, an increasing research area is the enhancement of molecular specificity for genetically encoded biosensors. Here, we summarize recent advances in the development of highly specific biosensor systems and their essential applications. First, we describe the rational design principles required to create libraries containing potential mutants with less promiscuity or better specificity. Next, we review the emerging high-throughput screening techniques to engineer biosensing specificity for the desired target. Finally, we examine the computer-aided evaluation and prediction methods to facilitate the construction of ligand-specific biosensors.

Changes in Coding and Efficiency through Modular Modifications to a One Pot PURE System for In Vitro Transcription and Translation

The incorporation of unnatural amino acids is an attractive method for improving or bringing new and novel functions in peptides and proteins. Cell-free protein synthesis using the Protein Synthesis Using Recombinant Elements (PURE) system is an attractive platform for efficient unnatural amino acid incorporation. In this work, we further adapted and modified the One Pot PURE to obtain a robust and modular system for enzymatic single-site-specific incorporation of an unnatural amino acid. We demonstrated the flexibility of this system through the introduction of two different orthogonal aminoacyl tRNA synthetase:tRNA pairs that suppressed two distinctive stop codons in separate reaction mixtures.

Recent advances in systems metabolic engineering

Systems metabolic engineering, which integrates metabolic engineering with systems biology, synthetic biology, and evolutionary engineering, has revolutionized the sustainable production of fuels and materials through the creation of efficient microbial cell factories. Recent advancements in systems metabolic engineering targeting different biological components of the host cell have enabled the creation of highly productive microbial cell factories. This article provides a review of the recent tools and strategies used for enzyme-, genetic module-, pathway-, flux-, genome-, and cell-level engineering, supported by illustrative examples. Furthermore, we highlight recent trends in systems metabolic engineering, which involve the application of multiple tools discussed in this review. Finally, the paper addresses the challenges and perspectives of transitioning academic-level metabolic engineering studies to commercial-scale production.

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.

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.

A designed Copper Histidine-brace enzyme for oxidative depolymerization of polysaccharides as a model of lytic polysaccharide monooxygenase

The "Histidine-brace" (His-brace) copper-binding site, composed of Cu(His)2 with a backbone amine, is found in metalloproteins with diverse functions. A primary example is lytic polysaccharide monooxygenase (LPMO), a class of enzymes that catalyze the oxidative depolymerization of polysaccharides, providing not only an energy source for native microorganisms but also a route to more effective industrial biomass conversion. Despite its importance, how the Cu His-brace site performs this unique and challenging oxidative depolymerization reaction remains to be understood. To answer this question, we have designed a biosynthetic model of LPMO by incorporating the Cu His-brace motif into azurin, an electron transfer protein. Spectroscopic studies, including ultraviolet-visible (UV-Vis) absorption and electron paramagnetic resonance, confirm copper binding at the designed His-brace site. Moreover, the designed protein is catalytically active towards both cellulose and starch, the native substrates of LPMO, generating degraded oligosaccharides with multiturnovers by C1 oxidation. It also performs oxidative cleavage of the model substrate 4-nitrophenyl-D-glucopyranoside, achieving a turnover number ~9% of that of a native LPMO assayed under identical conditions. This work presents a rationally designed artificial metalloenzyme that acts as a structural and functional mimic of LPMO, which provides a promising system for understanding the role of the Cu His-brace site in LPMO activity and potential application in polysaccharide degradation.

Synthesis of Metabolites and Metabolite-like Compounds Using Biocatalytic Systems

Methodologies for the synthesis and purification of metabolites, which have been developed following their discovery, analysis, and structural identification, have been involved in numerous life science milestones. The renewed focus on the small molecule domain of biological cells has also created an increasing awareness of the rising gap between the metabolites identified and the metabolites which have been prepared as pure compounds. The design and engineering of resource-efficient and straightforward synthetic methodologies for the production of the diverse and numerous metabolites and metabolite-like compounds have attracted much interest. The variety of metabolic pathways in biological cells provides a wonderful blueprint for designing simplified and resource-efficient synthetic routes to desired metabolites. Therefore, biocatalytic systems have become key enabling tools for the synthesis of an increasing number of metabolites, which can then be utilized as standards, enzyme substrates, inhibitors, or other products, or for the discovery of novel biological functions.

A larger basis set describes atomization energy core-valence correction better than a higher-order coupled-cluster method

The accuracy of coupled-cluster methods for the computation of core-valence correction to atomization energy was assessed. Truncation levels up to CCSDTQP were considered together with (aug-)cc-pwCVnZ (n = D, T, Q, 5) basis sets and three different extrapolation techniques (canonical and flexible Helgaker formula and Riemann zeta function extrapolation). With the exception of CCSD, a more accurate correction can be obtained from a larger basis set using a lower-level coupled-cluster method, and not vice versa. For the CCSD(T) level, it also implies faster computations with modern codes. We also discussed the importance of moving to higher-order or all-electron methods for geometry optimizations. The present study provides the general knowledge needed for the most accurate state-of-the-art computations.

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.

Artificial Intelligence Aided Lipase Production and Engineering for Enzymatic Performance Improvement

With the development of artificial intelligence (AI), tailoring methods for enzyme engineering have been widely expanded. Additional protocols based on optimized network models have been used to predict and optimize lipase production as well as properties, namely, catalytic activity, stability, and substrate specificity. Here, different network models and algorithms for the prediction and reforming of lipase, focusing on its modification methods and cases based on AI, are reviewed in terms of both their advantages and disadvantages. Different neural networks coupled with various algorithms are usually applied to predict the maximum yield of lipase by optimizing the external cultivations for lipase production, while one part is used to predict the molecule variations affecting the properties of lipase. However, few studies have directly utilized AI to engineer lipase by affecting the structure of the enzyme, and a set of research gaps needs to be explored. Additionally, future perspectives of AI application in enzymes, including lipase engineering, are deduced to help the redesign of enzymes and the reform of new functional biocatalysts. This review provides a new horizon for developing effective and innovative AI tools for lipase production and engineering and facilitating lipase applications in the food industry and biomass conversion.

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.

Reactive oxygen nanobiocatalysts: activity-mechanism disclosures, catalytic center evolutions, and changing states

Benefiting from low costs, structural diversities, tunable catalytic activities, feasible modifications, and high stability compared to the natural enzymes, reactive oxygen nanobiocatalysts (RONBCs) have become dominant materials in catalyzing and mediating reactive oxygen species (ROS) for diverse biomedical and biological applications. Decoding the catalytic mechanism and structure-reactivity relationship of RONBCs is critical to guide their future developments. Here, this timely review comprehensively summarizes the recent breakthroughs and future trends in creating and decoding RONBCs. First, the fundamental classification, activity, detection method, and reaction mechanism for biocatalytic ROS generation and elimination have been systematically disclosed. Then, the merits, modulation strategies, structure evolutions, and state-of-art characterisation techniques for designing RONBCs have been briefly outlined. Thereafter, we thoroughly discuss different RONBCs based on the reported major material species, including metal compounds, carbon nanostructures, and organic networks. In particular, we offer particular insights into the coordination microenvironments, bond interactions, reaction pathways, and performance comparisons to disclose the structure-reactivity relationships and mechanisms. In the end, the future challenge and perspectives for RONBCs are also carefully summarised. We envision that this review will provide a comprehensive understanding and guidance for designing ROS-catalytic materials and stimulate the wide utilisation of RONBCs in diverse biomedical and biological applications.

Enzymes of the crotonase superfamily: Diverse assembly and diverse function

The crotonase fold is generated by a framework of four repeats of a ββα-unit, extended by two helical regions. The active site of crotonase superfamily (CS) enzymes is located at the N-terminal end of the helix of the third repeat, typically being covered by a C-terminal helix. A major subset of CS-enzymes catalyzes acyl-CoA-dependent reactions, allowing for a diverse range of acyl-tail modifications. Most of these enzymes occur as trimers or hexamers (dimers of trimers), but monomeric forms are also observed. A common feature of the active sites of CS-enzymes is an oxyanion hole, formed by two peptide-NH hydrogen bond donors, which stabilises the negatively charged thioester oxygen atom of the reaction intermediate. Structural properties and possible use of these enzymes for biotechnological applications are discussed.

Acid-resistant enzymes: the acquisition strategies and applications

Enzymes have promising applications in chemicals, food, pharmaceuticals, and other variety products because of their high efficiency, specificity, and environmentally friendly properties. However, due to the complexity of raw materials, pH, temperature, solvents, etc., the application range of enzymes is greatly limited in the industry. Protein engineering and enzyme immobilization are classical strategies to overcome the limitations of industrial applications. Although the pH tendency of enzymes has been extensively researched, the mechanism underlying enzyme acid resistance is unclear, and a less practical strategy for altering the pH propensity of enzymes has been suggested. This review proposes that the optimum pH of enzyme is determined by the pKa values of active center ionizable amino acid residues. Three levels of acquiring acid-resistant enzymes are summarized: mining from extreme environments and enzyme databases, modification with protein engineering and enzyme microenvironment engineering, and de novo synthesis. The industrial applications of acid-resistant enzymes in chemicals, food, and pharmaceuticals are also summarized. KEY POINTS: • The mechanism of enzyme acid resistance is fundamentally determined. • The three aspects of the method for acquiring acid-resistant enzymes are summarized. • Computer-aided strategies and artificial intelligence are used to obtain acid-resistant enzymes.

Recent Synthetic Advances on the Use of Diazo Compounds Catalyzed by Metalloporphyrins

Diazo compounds are organic substances that are often used as precursors in organic synthesis like cyclization reactions, olefinations, cyclopropanations, cyclopropenations, rearrangements, and carbene or metallocarbene insertions into C-H, N-H, O-H, S-H, and Si-H bonds. Typically, reactions from diazo compounds are catalyzed by transition metals with various ligands that modulate the capacity and selectivity of the catalyst. These ligands can modify and enhance chemoselectivity in the substrate, regioselectivity and enantioselectivity by reflecting these preferences in the products. Porphyrins have been used as catalysts in several important reactions for organic synthesis and also in several medicinal applications. In the chemistry of diazo compounds, porphyrins are very efficient as catalysts when complexed with low-cost metals (e.g., Fe and Co) and, therefore, in recent years, this has been the subject of significant research. This review will summarize the advances in the studies involving the field of diazo compounds catalyzed by metalloporphyrins (M-Porph, M = Fe, Ru, Os, Co, Rh, Ir) in the last five years to provide a clear overview and possible opportunities for future applications. Also, at the end of this review, the properties of artificial metalloenzymes and hemoproteins as biocatalysts for a broad range of applications, namely those concerning carbene-transfer reactions, will be considered.

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.

How Flexibility Can Enhance Catalysis

Conformational changes are observed in many enzymes, but their role in catalysis is highly controversial. Here we present a theoretical model that illustrates how rigid catalysts can be fundamentally limited and how a conformational change induced by substrate binding can overcome this limitation, ultimately enabling barrier-free catalysis. The model is deliberately minimal, but the principle it illustrates is general and consistent with unique features of proteins as well as with previous informal proposals to explain the superiority of enzymes over other classes of catalysts. Implementing the discriminative switch suggested by the model could help overcome limitations currently encountered in the design of artificial catalysts.