The road to fully programmable protein catalysis

A Dual-Purpose Non-Canonical Amino Acid for the Expanded Genetic Code: Combining Metal-Binding and Click Chemistry

A rationally designed dual-purpose non-canonical amino acid (Trz) has been synthesised and successfully incorporated into a protein scaffold by genetic code expansion. Trz contains a 5-pyridyl-1,2,4-triazine system, which allows for inverse-electron-demand Diels-Alder (IEDDA) reactions to occur on the triazine ring and for metal ions to be chelated both before and after the click reaction. Trz was successfully incorporated into a protein scaffold and the IEDDA utility of Trz demonstrated through the site-specific labelling of the purified protein with a bicyclononyne. Additionally, Trz was shown to successfully coordinate a cyclometallated iridium(III) centre, providing access to a bioorthogonal luminogenic probe. The luminescent properties of the Ir(III)-bound protein blue-shift upon IEDDA click reaction with bicyclononyne, providing a unique method for monitoring the extent and location of the labelling reaction. In summary, Trz is a new dual-purpose non-canonical amino acid with great potential for myriad bioapplications where metal-based functionality is required, for example in imaging, catalysis, and photo-dynamic therapy, in conjunction with a bioorthogonal reactive handle to impart additional functionalities, such as dual-modality imaging or therapeutic payloads.

Organophosphate Hydrolysis by a Designed Metalloenzyme: Impact of Mutations Explained

The efficient design of novel enzymes has been attainable only by a combination of theoretical approaches and experimental refinement, suggesting inadequate performance of de novo design protocols. Based on the analysis of the evolutionary trajectory of a designed organophosphate hydrolase, this work aimed at developing and validating the improved theoretical models describing the catalytic activity of five enzyme variants (including wild-type as well as theoretically derived and experimentally refined enzymes) performing the hydrolysis of diethyl 7-hydroxycoumarinyl phosphate. The following aspects possibly important for enzyme design were addressed: the level of theory sufficient for a reliable description of enzyme-reactant interactions, the issue of ground state (GS) destabilization versus transition state (TS) stabilization, and the derivation of the proper side chain rotamers of amino acid residues. For enzyme variants analyzed herein, differential transition state stabilization (DTSS, i.e., preferential TS binding by an enzyme over the GS binding) calculated with a non-empirical model of the interaction energy (i.e., multipole electrostatic plus approximate dispersion terms, MED) displayed a superior performance in ranking the enzyme catalytic activity. The MED DTSS-based systematic rotamer refinement performed with an efficient scanning procedure and accounting for long-range interaction energy terms is an important step capable of unlocking the full potential impact of the given residue that could be otherwise overlooked with a conventional static approach featuring optimization to the nearby minimum. While TS stabilization is the main factor contributing to the increased catalytic activity of the de novo-designed variant studied in this work, directed evolution refinement appears to impact the catalytic activity of another enzyme variant analyzed herein via GS destabilization.

Understanding the Directed Evolution of a Natural-like Efficient Artificial Metalloenzyme

The artificial metalloenzyme containing iridium in place of iron along with four directed evolution mutations C317G, T213G, L69V, and V254L in a natural cytochrome P450 presents an important milestone in merging the extraordinary efficiency of biocatalysts with the versatility of small molecule chemical catalysts in catalyzing a new-to-nature carbene insertion reaction. This is a show-stopper enzyme, as it exhibits a catalytic efficiency similar to that of natural enzymes. Despite this remarkable discovery, there is no mechanistic and structural understanding as to why it displays extraordinary efficiency after the incorporation of the four active site mutations by directed evolution methods, which so far has been intractable to any experimental methods. In this study, we have deciphered how directed evolution mutations gradually alter the protein conformational ensemble to populate a catalytically active conformation to boost a multistep catalysis in a natural-like artificial metalloenzyme using large-scale molecular dynamics simulations, rigorous quantum chemical (QM), and multiscale quantum chemical/molecular mechanics (QM/MM) calculations. It reveals how evolution precisely positions the cofactor-substrate in an unusual but effective orientation within a reshaped active site in the catalytically active conformation stabilized by C-H···π interactions from more ordered mutated L69V and V254L residues to achieve preferential transition state stabilization compared to the ground state. This work essentially tracks down in atomistic detail the shift in the conformational ensemble of the highly active conformation from the less efficient single mutant to the most efficient quadruple mutant and offers valuable insights for designing better enzymes. The active conformation correctly reproduces the experimental barrier height and also accounts for the catalytic effect, which is in good agreement with experimental observations. Moreover, this conformation features an unusual bonding interaction in a metal-carbene species that preferentially stabilizes the rate-determining formation of an iridium porphyrin carbene intermediate to render the observed high catalytic rate acceleration. Our study provides crucial insights into the underlying rationale for directed evolution, reports the major catalytic role of nonelectrostatic interactions in enzyme catalysis different from the electrostatic model, and suggests a crucial principle toward designing enzymes with natural efficiency.

Biocatalytic synthesis of ribonucleoside analogues using nucleoside transglycosylase-2

Ribonucleosides are essential building blocks used extensively in antiviral and oligonucleotide therapeutics. A major challenge in the further development of nucleoside analogues for therapeutic applications is access to scalable and environmentally sustainable synthetic strategies. This study uses the type II nucleoside 2'-deoxyribosyltransferase from Lactobacillus leichmannii (LlNDT-2) to prepare a suite of ribonucleoside analogues using naturally-occurring uridine and cytidine sugar donors. Crystal structure and mutational analyses are used to define the substrate tolerance of the nucleobase exchange and the 2'-substituent of the nucleoside sugar donor. Nucleobase profiling identified acceptance of both purine and pyrimidine nucleobases. Finally, the scalability of the approach is showcased, enabling the preparation of ribonucleosides on millimolar scales. This biocatalytic strategy opens up opportunities to establish chemoenzymatic routes to prepare nucleoside analogues incorporating 2' modifications that are of therapeutic importance.

A general temperature-guided language model to design proteins of enhanced stability and activity

Designing protein mutants with both high stability and activity is a critical yet challenging task in protein engineering. Here, we introduce PRIME, a deep learning model, which can suggest protein mutants with improved stability and activity without any prior experimental mutagenesis data for the specified protein. Leveraging temperature-aware language modeling, PRIME demonstrated superior predictive ability compared to current state-of-the-art models on the public mutagenesis dataset across 283 protein assays. Furthermore, we validated PRIME's predictions on five proteins, examining the impact of the top 30 to 45 single-site mutations on various protein properties, including thermal stability, antigen-antibody binding affinity, and the ability to polymerize nonnatural nucleic acid or resilience to extreme alkaline conditions. More than 30% of PRIME-recommended mutants exhibited superior performance compared to their premutation counterparts across all proteins and desired properties. We developed an efficient and effective method based on PRIME to rapidly obtain multisite mutants with enhanced activity and stability. Hence, PRIME demonstrates broad applicability in protein engineering.

Single-Electron Oxidation Triggered by Visible-Light-Excited Enzymes for Asymmetric Biocatalysis

By integrating enzymatic catalysis with photocatalysis, photoenzymatic catalysis emerges as a powerful strategy to enhance enzyme catalytic capabilities and provide superior stereocontrol in reactions involving reactive intermediates. Repurposing naturally occurring enzymes using visible light is among the most active directions of photoenzymatic catalysis. This Minireview focuses on a cutting-edge strategy in this direction, namely single-electron-oxidation-triggered non-natural biotransformations catalyzed by photoexcited enzymes. These straightforward transformations feature a unique radical mechanism initiated by single-electron oxidation, achieving redox-neutral non-natural C-C, C-O, and C-S bond formation, and expanding the chemical toolbox of enzymes. By highlighting recent advances in this field and emphasizing their catalytic mechanisms and synthetic potential, innovative approaches for photobiomanufacturing are anticipated.

Using the Theozyme Model to Study the Dynamical Mechanism of the Post-Transition State Bifurcation Reaction by NgnD Enzyme

Post-transition state bifurcation (PTSB) is a fundamental process in which a single transition state leads to multiple products. This phenomenon is important in both biological and chemical contexts and offers valuable insights into reaction mechanisms and their applications. The theozyme model, which focuses on key residues within enzymes, offers a computationally efficient method for studying these processes while preserving the enzyme's catalytic properties. This approach enhances our understanding of how enzymes stabilize and direct the transition state, thereby influencing product distribution and selectivity. In this study, we investigate the dynamics and regulatory mechanisms of the PTSB reaction catalyzed by the enzyme NgnD. The enzyme NgnD facilitates a cycloaddition reaction that produces both [6 + 4] and [4 + 2] adducts, with a preference for the [6 + 4] adduct. By analyzing the potential energy surface, bond length distribution, and interactions between the theozyme and the ambimodal transition state, we elucidate the role of the enzyme's active site residues in determining product selectivity. We illustrate how these key residues contribute to the formation of different adducts, providing insights from various perspectives. Using theozyme models, we propose how the four most influential active residues collectively might control the direction of adduct formation through their cumulative effects.

Installation of an organocatalyst into a protein scaffold creates an artificial Stetterase

Using a protein scaffold covalently functionalised with a thiamine-inspired N-heterocyclic carbene (NHC), we created an artificial Stetterase (ArtiSt) which catalyses a stereoselective, intramolecular Stetter reaction. We demonstrate that ArtiSt functions under ambient conditions with low catalyst loading. Furthermore, activity can be increased >20 fold by altering the protein scaffold.

Reaching New Heights in Genetic Code Manipulation with High Throughput Screening

The chemical and physical properties of proteins are limited by the 20 canonical amino acids. Genetic code manipulation allows for the incorporation of noncanonical amino acids (ncAAs) that enhance or alter protein functionality. This review explores advances in the three main strategies for introducing ncAAs into biosynthesized proteins, focusing on the role of high throughput screening in these advancements. The first section discusses engineering aminoacyl-tRNA synthetases (aaRSs) and tRNAs, emphasizing how novel selection methods improve characteristics including ncAA incorporation efficiency and selectivity. The second section examines high-throughput techniques for improving protein translation machinery, enabling accommodation of alternative genetic codes. This includes opportunities to enhance ncAA incorporation through engineering cellular components unrelated to translation. The final section highlights various discovery platforms for high-throughput screening of ncAA-containing proteins, showcasing innovative binding ligands and enzymes that are challenging to create with only canonical amino acids. These advances have led to promising drug leads and biocatalysts. Overall, the ability to discover unexpected functionalities through high-throughput methods significantly influences ncAA incorporation and its applications. Future innovations in experimental techniques, along with advancements in computational protein design and machine learning, are poised to further elevate this field.

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 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.

A resurrected ancestor of Cas12a expands target access and substrate recognition for nucleic acid editing and detection

The properties of Cas12a nucleases constrict the range of accessible targets and their applications. In this study, we applied ancestral sequence reconstruction (ASR) to a set of Cas12a orthologs from hydrobacteria to reconstruct a common ancestor, ReChb, characterized by near-PAMless targeting and the recognition of diverse nucleic acid activators and collateral substrates. ReChb shares 53% sequence identity with the closest Cas12a ortholog but no longer requires a T-rich PAM and can achieve genome editing in human cells at sites inaccessible to the natural FnCas12a or the engineered and PAM-flexible enAsCas12a. Furthermore, ReChb can be triggered not only by double-stranded DNA but also by single-stranded RNA and DNA targets, leading to non-specific collateral cleavage of all three nucleic acid substrates with similar efficiencies. Finally, tertiary and quaternary structures of ReChb obtained by cryogenic electron microscopy reveal the molecular details underlying its expanded biophysical activities. Overall, ReChb expands the application space of Cas12a nucleases and underscores the potential of ASR for enhancing CRISPR technologies.

Machine learning-assisted amidase-catalytic enantioselectivity prediction and rational design of variants for improving enantioselectivity

Biocatalysis is an attractive approach for the synthesis of chiral pharmaceuticals and fine chemicals, but assessing and/or improving the enantioselectivity of biocatalyst towards target substrates is often time and resource intensive. Although machine learning has been used to reveal the underlying relationship between protein sequences and biocatalytic enantioselectivity, the establishment of substrate fitness space is usually disregarded by chemists and is still a challenge. Using 240 datasets collected in our previous works, we adopt chemistry and geometry descriptors and build random forest classification models for predicting the enantioselectivity of amidase towards new substrates. We further propose a heuristic strategy based on these models, by which the rational protein engineering can be efficiently performed to synthesize chiral compounds with higher ee values, and the optimized variant results in a 53-fold higher E-value comparing to the wild-type amidase. This data-driven methodology is expected to broaden the application of machine learning in biocatalysis research.

Noncanonical Amino Acids: Bringing New-to-Nature Functionalities to Biocatalysis

Biocatalysis has become an important component of modern organic chemistry, presenting an efficient and environmentally friendly approach to synthetic transformations. Advances in molecular biology, computational modeling, and protein engineering have unlocked the full potential of enzymes in various industrial applications. However, the inherent limitations of the natural building blocks have sparked a revolutionary shift. In vivo genetic incorporation of noncanonical amino acids exceeds the conventional 20 amino acids, opening new avenues for innovation. This review provides a comprehensive overview of applications of noncanonical amino acids in biocatalysis. We aim to examine the field from multiple perspectives, ranging from their impact on enzymatic reactions to the creation of novel active sites, and subsequent catalysis of new-to-nature reactions. Finally, we discuss the challenges, limitations, and promising opportunities within this dynamic research domain.

Rational Design of an Artificial Metalloenzyme by Constructing a Metal-Binding Site Close to the Heme Cofactor in Myoglobin

In this study, we constructed a metal-binding site close to the heme cofactor in myoglobin (Mb) by covalently attaching a nonnative metal-binding ligand of bipyridine to Cys46 through the F46C mutation in the heme distal site. The X-ray structure of the designed enzyme, termed F46C-mBpy Mb, was solved in the Cu(II)-bound form, which revealed the formation of a heterodinuclear center of Cu-His-H2O-heme. Cu(II)-F46C-mBpy Mb exhibits not only nitrite reductase reactivity but also cascade reaction activity involving both hydrolysis and oxidation. Furthermore, F46C-mBpy Mb displays Mn-peroxidase activity by the oxidation of Mn2+ to Mn3+ using H2O2 as an oxidant. This study shows that the construction of a nonnative metal-binding site close to the heme cofactor is a convenient approach to creating an artificial metalloenzyme with a heterodinuclear center that confers multiple functions.

Design and Evolution of an Artificial Friedel-Crafts Alkylation Enzyme Featuring an Organoboronic Acid Residue

Creating artificial enzymes by the genetic incorporation of noncanonical amino acids with catalytic side chains would expand the enzyme chemistries that have not been discovered in nature. Here, we report the design of an artificial enzyme that uses p-boronophenylalanine as the catalytic residue. The artificial enzyme catalyzes Michael-type Friedel-Crafts alkylation through covalent activation. The designer enzyme was further engineered to afford high yields with excellent enantioselectivities. We next developed a practical method for preparative-scale reactions by whole-cell catalysis. This enzymatic C-C bond formation reaction was combined with palladium-catalyzed dearomative arylation to achieve the efficient synthesis of spiroindolenine compounds.

Enzymatic production of xylooligosaccharide from lignocellulosic and marine biomass: A review of current progress, challenges, and its applications in food sectors

Over the last decade, xylooligosaccharides (XOS) have attracted great attentions because of their unique chemical properties and excellent prebiotic effects. Among the current strategies for XOS production, enzymatic hydrolysis is preferred due to its green and safe process, simplicity in equipment, and high control of the degrees of polymerization. This paper comprehensively summarizes various lignocellulosic biomass and marine biomass employed in enzymatic production of XOS. The importance and advantages of enzyme immobilization in XOS production are also discussed. Many novel immobilization techniques for xylanase are presented. In addition, bioinformatics techniques for the mining and designing of new xylanase are also described. Moreover, XOS has exhibited great potential applications in the food industry as diverse roles, such as a sugar replacer, a fat replacer, and cryoprotectant. This review systematically summarizes the current research progress on the applications of XOS in food sectors, including beverages, bakery products, dairy products, meat products, aquatic products, food packaging film, wall materials, and others. It is anticipated that this paper will act as a reference for the further development and application of XOS in food sectors and other fields.

Cellular Site-Specific Incorporation of Noncanonical Amino Acids in Synthetic Biology

Over the past two decades, genetic code expansion (GCE)-enabled methods for incorporating noncanonical amino acids (ncAAs) into proteins have significantly advanced the field of synthetic biology while also reaping substantial benefits from it. On one hand, they provide synthetic biologists with a powerful toolkit to enhance and diversify biological designs beyond natural constraints. Conversely, synthetic biology has not only propelled the development of ncAA incorporation through sophisticated tools and innovative strategies but also broadened its potential applications across various fields. This Review delves into the methodological advancements and primary applications of site-specific cellular incorporation of ncAAs in synthetic biology. The topics encompass expanding the genetic code through noncanonical codon addition, creating semiautonomous and autonomous organisms, designing regulatory elements, and manipulating and extending peptide natural product biosynthetic pathways. The Review concludes by examining the ongoing challenges and future prospects of GCE-enabled ncAA incorporation in synthetic biology and highlighting opportunities for further advancements in this rapidly evolving field.

Characterization and rational engineering of an alkaline-tolerant azoreductase derived from Roseibium sp. H3510 for enhanced decolorization of azo dyes

rAzoR2326, an azoreductase derived from Roseibium sp. H3510, functions as an FMN-dependent homodimer utilizing NADH as cofactor. It demonstrated maximum activity at 45 °C and retained moderate activity above 50 °C, exhibiting stability from pH 7-10. Evolution and structure guided rational design of wild-type rAzoR2326 (WT) efficiently yielded 6 single-point mutants with improved thermostability and activity from a 22-variant library. Further combinatorial mutation led to mutant M20 with substantially enhanced thermostability (15-fold longer half-life at 50 °C) and activity (3.24-fold higher kcat/Km). M20 exhibited superior catalytic properties for decolorizing Allura Red compared to WT. Specifically, its decolorization capacity at pH 10.0 was 4.26-fold higher than WT. Additionally, M20 demonstrated remarkable thermostability, retaining 76.83 % decolorization activity for Allura Red after 120 min at 50 °C, whereas WT nearly lost all catalytic activity under the same conditions. Molecular dynamics simulations revealed the structural changes in M20, such as improved hydrogen bonding and a new C-H···π interaction, led to a more compact and rigid enzyme structure. This resulted in a more stable FMN-binding pocket and substrate tunnel, thereby improving the catalytic stability and activity of M20. Given its enhanced dye decolorization ability and alkaline tolerance, M20 shows promise as a biocatalyst for treating azo dye effluents.

BhrPETase catalyzed polyethylene terephthalate depolymerization: A quantum mechanics/molecular mechanics approach

Polyethylene terephthalate (PET) is a widely used material in our daily life, particularly in areas such as packaging, fibers, and engineering plastics. However, PET waste can accumulate in the environment and pose a great threat to our ecosystem. Recently enzymatic conversion has emerged as an efficient and green strategy to address the PET crisis. Here, using a theoretical approach combining molecular dynamics simulation and quantum mechanics/molecular mechanics calculations, the depolymerization mechanism of the thermophilic cutinase BhrPETase was fully deciphered. Surprisingly, unlike the previously studied cutinase LCCICCG, our results indicate that the first step, catalytic triad assisted nucleophilic attack, is the rate-determining step. The corresponding Boltzmann weighted average energy barrier is 18.2 kcal/mol. Through extensive comparison between BhrPETase and LCCICCG, we evidence that key features like charge CHis@N1 and angle APET@C1-Ser@O1-His@H1 significantly impact the depolymerization efficiency of BhrPETase. Non-covalent bond interaction and distortion/interaction analysis inform new insights on enzyme engineer and may aid the recycling of enzymatic PET waste. This study will aid the advancement of the plastic bio-recycling economy and promote resource conservation and reuse.

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.

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.

Concluding remarks: biocatalysis

Biocatalysis is a rapidly evolving field with increasing impact in organic synthesis, chemical manufacturing and medicine. The Faraday Discussion reflected the current state of biocatalysis, covering the design of de novo enzymatic activities, but especially methods for the improvement of enzymes targeting a broad range of applications (i.e., hydroxylations by P450 monooxygenases, enzymatic deprotection of organic compounds under mild conditions, synthesis of chiral intermediates, plastic degradation, silicone polymer synthesis, and peptide synthesis). Central themes have been how to improve an enzyme using methods of rational design and directed evolution, informed by computer modelling and machine learning, and the incorporation of new catalytic functionalities to create hybrid and artificial enzymes.

An efficient pyrrolysyl-tRNA synthetase for economical production of MeHis-containing enzymes

Genetic code expansion has emerged as a powerful tool in enzyme design and engineering, providing new insights into sophisticated catalytic mechanisms and enabling the development of enzymes with new catalytic functions. In this regard, the non-canonical histidine analogue Nδ-methylhistidine (MeHis) has proven especially versatile due to its ability to serve as a metal coordinating ligand or a catalytic nucleophile with a similar mode of reactivity to small molecule catalysts such as 4-dimethylaminopyridine (DMAP). Here we report the development of a highly efficient aminoacyl tRNA synthetase (G1PylRSMIFAF) for encoding MeHis into proteins, by transplanting five known active site mutations from Methanomethylophilus alvus (MaPylRS) into the single domain PylRS from Methanogenic archaeon ISO4-G1. In contrast to the high concentrations of MeHis (5-10 mM) needed with the Ma system, G1PylRSMIFAF can operate efficiently using MeHis concentrations of ∼0.1 mM, allowing more economical production of a range of MeHis-containing enzymes in high titres. Interestingly G1PylRSMIFAF is also a 'polyspecific' aminoacyl tRNA synthetase (aaRS), enabling incorporation of five different non-canonical amino acids (ncAAs) including 3-pyridylalanine and 2-fluorophenylalanine. This study provides an important step towards scalable production of engineered enzymes that contain non-canonical amino acids such as MeHis as key catalytic elements.

Learning from Protein Engineering by Deconvolution of Multi-Mutational Variants

This review analyzes a development in biochemistry, enzymology and biotechnology that originally came as a surprise. Following the establishment of directed evolution of stereoselective enzymes in organic chemistry, the concept of partial or complete deconvolution of selective multi-mutational variants was introduced. Early deconvolution experiments of stereoselective variants led to the finding that mutations can interact cooperatively or antagonistically with one another, not just additively. During the past decade, this phenomenon was shown to be general. In some studies, molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM) computations were performed in order to shed light on the origin of non-additivity at all stages of an evolutionary upward climb. Data of complete deconvolution can be used to construct unique multi-dimensional rugged fitness pathway landscapes, which provide mechanistic insights different from traditional fitness landscapes. Along a related line, biochemists have long tested the result of introducing two point mutations in an enzyme for mechanistic reasons, followed by a comparison of the respective double mutant in so-called double mutant cycles, which originally showed only additive effects, but more recently also uncovered cooperative and antagonistic non-additive effects. We conclude with suggestions for future work, and call for a unified overall picture of non-additivity and epistasis.

Advancing microbial production through artificial intelligence-aided biology

Microbial cell factories (MCFs) have been leveraged to construct sustainable platforms for value-added compound production. To optimize metabolism and reach optimal productivity, synthetic biology has developed various genetic devices to engineer microbial systems by gene editing, high-throughput protein engineering, and dynamic regulation. However, current synthetic biology methodologies still rely heavily on manual design, laborious testing, and exhaustive analysis. The emerging interdisciplinary field of artificial intelligence (AI) and biology has become pivotal in addressing the remaining challenges. AI-aided microbial production harnesses the power of processing, learning, and predicting vast amounts of biological data within seconds, providing outputs with high probability. With well-trained AI models, the conventional Design-Build-Test (DBT) cycle has been transformed into a multidimensional Design-Build-Test-Learn-Predict (DBTLP) workflow, leading to significantly improved operational efficiency and reduced labor consumption. Here, we comprehensively review the main components and recent advances in AI-aided microbial production, focusing on genome annotation, AI-aided protein engineering, artificial functional protein design, and AI-enabled pathway prediction. Finally, we discuss the challenges of integrating novel AI techniques into biology and propose the potential of large language models (LLMs) in advancing microbial production.

Hosts engineering and in vitro enzymatic synthesis for the discovery of novel natural products and their derivatives

Novel natural products (NPs) and their derivatives are important sources for drug discovery, which have been broadly applied in the fields of agriculture, livestock, and medicine, making the synthesis of NPs and their derivatives necessarily important. In recent years, biosynthesis technology has received increasing attention due to its high efficiency in the synthesis of high value-added novel products and its advantages of green, environmental protection, and controllability. In this review, the technological advances of biosynthesis strategies in the discovery of novel NPs and their derivatives are outlined, with an emphasis on two areas of host engineering and in vitro enzymatic synthesis. In terms of hosts engineering, multiple microorganisms, including Streptomyces, Aspergillus, and Penicillium, have been used as the biosynthetic gene clusters (BGCs) provider and host strain for the expression of BGCs to discover new compounds over the past years. In addition, the use of in vitro enzymatic synthesis strategy to generate novel compounds such as triterpenoid saponins and flavonoids is also hereby described.

Computational design of serine hydrolases

Enzymes that proceed through multistep reaction mechanisms often utilize complex, polar active sites positioned with sub-angstrom precision to mediate distinct chemical steps, which makes their de novo construction extremely challenging. We sought to overcome this challenge using the classic catalytic triad and oxyanion hole of serine hydrolases as a model system. We used RFdiffusion1 to generate proteins housing catalytic sites of increasing complexity and varying geometry, and a newly developed ensemble generation method called ChemNet to assess active site geometry and preorganization at each step of the reaction. Experimental characterization revealed novel serine hydrolases that catalyze ester hydrolysis with catalytic efficiencies (k cat /K m ) up to 3.8 x 103 M-1 s-1, closely match the design models (Cα RMSDs < 1 Å), and have folds distinct from natural serine hydrolases. In silico selection of designs based on active site preorganization across the reaction coordinate considerably increased success rates, enabling identification of new catalysts in screens of as few as 20 designs. Our de novo buildup approach provides insight into the geometric determinants of catalysis that complements what can be obtained from structural and mutational studies of native enzymes (in which catalytic group geometry and active site makeup cannot be so systematically varied), and provides a roadmap for the design of industrially relevant serine hydrolases and, more generally, for designing complex enzymes that catalyze multi-step transformations.

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.

Navigating the landscape of enzyme design: from molecular simulations to machine learning

Global environmental issues and sustainable development call for new technologies for fine chemical synthesis and waste valorization. Biocatalysis has attracted great attention as the alternative to the traditional organic synthesis. However, it is challenging to navigate the vast sequence space to identify those proteins with admirable biocatalytic functions. The recent development of deep-learning based structure prediction methods such as AlphaFold2 reinforced by different computational simulations or multiscale calculations has largely expanded the 3D structure databases and enabled structure-based design. While structure-based approaches shed light on site-specific enzyme engineering, they are not suitable for large-scale screening of potential biocatalysts. Effective utilization of big data using machine learning techniques opens up a new era for accelerated predictions. Here, we review the approaches and applications of structure-based and machine-learning guided enzyme design. We also provide our view on the challenges and perspectives on effectively employing enzyme design approaches integrating traditional molecular simulations and machine learning, and the importance of database construction and algorithm development in attaining predictive ML models to explore the sequence fitness landscape for the design of admirable biocatalysts.

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.

Recent advances in the design and optimization of artificial metalloenzymes

Embedding a catalytically competent transition metal into a protein scaffold affords an artificial metalloenzyme (ArM). Such hybrid catalysts display features that are reminiscent of both homogeneous and enzymatic catalysts. Pioneered by Whitesides and Kaiser in the late 1970s, this field of ArMs has expanded over the past two decades, marked by ever-increasing diversity in reaction types, cofactors, and protein scaffolds. Recent noteworthy developments include i) the use of earth-abundant metal cofactors, ii) concurrent cascade reactions, iii) synergistic catalysis, and iv) in vivo catalysis. Thanks to significant progress in computational protein design, ArMs based on de novo-designed proteins and tailored chimeric proteins promise a bright future for this exciting field.

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.

Machine learning-assisted substrate binding pocket engineering based on structural information

Engineering enzyme-substrate binding pockets is the most efficient approach for modifying catalytic activity, but is limited if the substrate binding sites are indistinct. Here, we developed a 3D convolutional neural network for predicting protein-ligand binding sites. The network was integrated by DenseNet, UNet, and self-attention for extracting features and recovering sample size. We attempted to enlarge the dataset by data augmentation, and the model achieved success rates of 48.4%, 35.5%, and 43.6% at a precision of ≥50% and 52%, 47.6%, and 58.1%. The distance of predicted and real center is ≤4 Å, which is based on SC6K, COACH420, and BU48 validation datasets. The substrate binding sites of Klebsiella variicola acid phosphatase (KvAP) and Bacillus anthracis proline 4-hydroxylase (BaP4H) were predicted using DUnet, showing high competitive performance of 53.8% and 56% of the predicted binding sites that critically affected the catalysis of KvAP and BaP4H. Virtual saturation mutagenesis was applied based on the predicted binding sites of KvAP, and the top-ranked 10 single mutations contributed to stronger enzyme-substrate binding varied while the predicted sites were different. The advantage of DUnet for predicting key residues responsible for enzyme activity further promoted the success rate of virtual mutagenesis. This study highlighted the significance of correctly predicting key binding sites for enzyme engineering.

Genetically encoded Nδ-vinyl histidine for the evolution of enzyme catalytic center

Genetic code expansion has emerged as a powerful tool for precisely introducing unnatural chemical structures into proteins to improve their catalytic functions. Given the high catalytic propensity of histidine in the enzyme pocket, increasing the chemical diversity of catalytic histidine could result in new characteristics of biocatalysts. Herein, we report the genetically encoded Nδ-Vinyl Histidine (δVin-H) and achieve the wild-type-like incorporation efficiency by the evolution of pyrrolysyl tRNA synthetase. As histidine usually acts as the nucleophile or the metal ligand in the catalytic center, we replace these two types of catalytic histidine to δVin-H to improve the performance of the histidine-involved catalytic center. Additionally, we further demonstrate the improvements of the hydrolysis activity of a previously reported organocatalytic esterase (the OE1.3 variant) in the acidic condition and myoglobin (Mb) catalyzed carbene transfer reactions under the aerobic condition. As histidine is one of the most frequently used residues in the enzyme catalytic center, the derivatization of the catalytic histidine by δVin-H holds a great potential to promote the performance of biocatalysts.

Carbohydrate-active enzyme (CAZyme) discovery and engineering via (Ultra)high-throughput screening

Carbohydrate-active enzymes (CAZymes) constitute a diverse set of enzymes that catalyze the assembly, degradation, and modification of carbohydrates. These enzymes have been fashioned into potent, selective catalysts by millennia of evolution, and yet are also highly adaptable and readily evolved in the laboratory. To identify and engineer CAZymes for different purposes, (ultra)high-throughput screening campaigns have been frequently utilized with great success. This review provides an overview of the different approaches taken in screening for CAZymes and how mechanistic understandings of CAZymes can enable new approaches to screening. Within, we also cover how cutting-edge techniques such as microfluidics, advances in computational approaches and synthetic biology, as well as novel assay designs are leading the field towards more informative and effective screening approaches.

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.

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.

Chemo-Enzymatic Generation of Highly Fluorescent Nucleoside Analogs Using Purine-Nucleoside Phosphorylase

Chemo-enzymatic syntheses of strongly fluorescent nucleoside analogs, potentially applicable in analytical biochemistry and cell biology are reviewed. The syntheses and properties of fluorescent ribofuranosides of several purine, 8-azapurine, and etheno-purine derivatives, obtained using various types of purine nucleoside phosphorylase (PNP) as catalysts, as well as α-ribose-1-phosphate (r1P) as a second substrate, are described. In several instances, the ribosylation sites are different to the canonical purine N9. Some of the obtained ribosides show fluorescence yields close to 100%. Possible applications of the new analogs include assays of PNP, nucleoside hydrolases, and other enzyme activities both in vitro and within living cells using fluorescence microscopy.

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.

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.

Ancestral terpene cyclases: From fundamental science to applications in biosynthesis

Terpenes constitute one of the largest family of natural products with potent applications as renewable platform chemicals and medicines. The low activity, selectivity and stability displayed by terpene biosynthetic machineries can constitute an obstacle towards achieving expedient biosynthesis of terpenoids in processes that adhere to the 12 principles of green chemistry. Accordingly, engineering of terpene synthase enzymes is a prerequisite for industrial biotechnology applications, but obstructed by their complex catalysis that depend on reactive carbocationic intermediates that are prone to undergo bifurcation mechanisms. Rational redesign of terpene synthases can be tedious and requires high-resolution structural information, which is not always available. Furthermore, it has proven difficult to link sequence space of terpene synthase enzymes to specific product profiles. Herein, the author shows how ancestral sequence reconstruction (ASR) can favorably be used as a protein engineering tool in the redesign of terpene synthases without the need of a structure, and without excessive screening. A detailed workflow of ASR is presented along with associated limitations, with a focus on applying this methodology on terpene synthases. From selected examples of both class I and II enzymes, the author advocates that ancestral terpene cyclases constitute valuable assets to shed light on terpene-synthase catalysis and in enabling accelerated biosynthesis.

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.

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.