From nature to industry: Harnessing enzymes for biocatalysis

Functional metalloenzymes based on myoglobin and neuroglobin that exploit covalent interactions

Globins, such as myoglobin (Mb) and neuroglobin (Ngb), are ideal protein scaffolds for the design of functional metalloenzymes. To date, numerous approaches have been developed for enzyme design. This review presents a summary of the progress made in the design of functional metalloenzymes based on Mb and Ngb, with a focus on the exploitation of covalent interactions, including coordination bonds and covalent modifications. These include the construction of a metal-binding site, the incorporation of a non-native metal cofactor, the formation of Cys/Tyr-heme covalent links, and the design of disulfide bonds, as well as other Cys-covalent modifications. As exemplified by recent studies from our group and others, the designed metalloenzymes have potential applications in biocatalysis and bioconversions. Furthermore, we discuss the current trends in the design of functional metalloenzymes and highlight the importance of covalent interactions in the design of functional metalloenzymes.

Efficient pathway-driven scyllo-inositol production from myo-inositol using thermophilic cells and mesophilic inositol dehydrogenases: a novel strategy for pathway control

Mesophilic enzymes, which are active at moderate temperatures, may dominate enzymatic reactions even in the presence of thermophilic crude enzymes. This study was conducted to investigate this hypothesis with mesophilic inositol dehydrogenases (IolG and IolX) produced in Geobacillus kaustophilus HTA426. To ensure the efficient production of mesophilic enzymes, we first screened for promoters induced at moderate temperatures using transcriptome analysis and identified four genes highly expressed at 30°C in the thermophile. We further characterized these promoters using fluorescent reporter assays to determine that the mti3 promoter could direct efficient gene expression at 40°C. We cloned the promoter into an Escherichia coli-Geobacillus shuttle plasmid and confirmed that the resulting vector functioned in G. kaustophilus and other thermophiles. We then used this vector for the cooperative expression of the iolG and iolX genes from Bacillus subtilis 168. G. kaustophilus cells carrying the expression vector were incubated at 60°C for cellular propagation and then at 40°C for the production of IolG and IolX. When the cells were permeabilized, IolG and IolX acted as catalysts to convert exogenous myo-inositol into scyllo-inositol at 30°C. In a scaled-up reaction, 10 g of myo-inositol was converted to 1.8 g of scyllo-inositol, which was further purified to yield 970 mg of pure powder. Notably, myo-inositol was degraded by intrinsic enzymes of G. kaustophilus at 60°C but not at 30°C, supporting our initial hypothesis. We indicate that this approach is useful for preparing enzyme cocktails without the need for purification.

IMPORTANCE

Enzyme cocktails are commonly employed for cell-free chemical synthesis; however, their preparation involves cumbersome processes. This study affirms that mesophilic enzymes in thermophilic crude extracts can function as specific catalysts at moderate temperatures, akin to enzyme cocktails. The catalyst was prepared by permeabilizing cells without the need for concentration, extraction, or purification processes; hence, its preparation was considerably simpler compared with conventional methods for enzyme cocktails. This approach was employed to produce pure scyllo-inositol from an economical substrate. Notably, this marks the first large-scale preparation of pure scyllo-inositol, holding potential pharmaceutical significance as scyllo-inositol serves as a promising agent for certain diseases but is currently expensive. Moreover, this approach holds promise for application in pathway engineering within living cells. The envisioned pathway is designed without chromosomal modification and is simply regulated by switching culture temperatures. Consequently, this study introduces a novel platform for both whole-cell and cell-free synthetic systems.

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.

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.

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.

The Development and Opportunities of Predictive Biotechnology

Recent advances in bioeconomy allow a holistic view of existing and new process chains and enable novel production routines continuously advanced by academia and industry. All this progress benefits from a growing number of prediction tools that have found their way into the field. For example, automated genome annotations, tools for building model structures of proteins, and structural protein prediction methods such as AlphaFold2TM or RoseTTAFold have gained popularity in recent years. Recently, it has become apparent that more and more AI-based tools are being developed and used for biocatalysis and biotechnology. This is an excellent opportunity for academia and industry to accelerate advancements in the field further. Biotechnology, as a rapidly growing interdisciplinary field, stands to benefit greatly from these developments.

Repurposing Visible-Light-Excited Ene-Reductases for Diastereo- and Enantioselective Lactones Synthesis

Repurposing enzymes to catalyze non-natural asymmetric transformations that are difficult to achieve using traditional chemical methods is of significant importance. Although radical C-O bond formation has emerged as a powerful approach for constructing oxygen-containing compounds, controlling the stereochemistry poses a great challenge. Here we present the development of a dual bio-/photo-catalytic system comprising an ene-reductase and an organic dye for achieving stereoselective lactonizations. By integrating directed evolution and photoinduced single electron oxidation, we repurposed engineered ene-reductases to steer non-natural radical C-O formations (one C-O bond for hydrolactonizations and lactonization-alkylations while two C-O bonds for lactonization-oxygenations). This dual catalysis gave a new approach to a diverse array of enantioenhanced 5- and 6-membered lactones with vicinal stereocenters, part of which bears a quaternary stereocenter (up to 99 % enantiomeric excess, up to 12.9 : 1 diastereomeric ratio). Detailed mechanistic studies, including computational simulations, uncovered the synergistic effect of the enzyme and the externally added organophotoredox catalyst Rh6G.

Cryptic enzymatic assembly of peptides armed with β-lactone warheads

Nature has evolved biosynthetic pathways to molecules possessing reactive warheads that inspired the development of many therapeutic agents, including penicillin antibiotics. Peptides armed with electrophilic warheads have proven to be particularly effective covalent inhibitors, providing essential antimicrobial, antiviral and anticancer agents. Here we provide a full characterization of the pathways that nature deploys to assemble peptides with β-lactone warheads, which are potent proteasome inhibitors with promising anticancer activity. Warhead assembly involves a three-step cryptic methylation sequence, which is likely required to reduce unfavorable electrostatic interactions during the sterically demanding β-lactonization. Amide-bond synthetase and adenosine triphosphate (ATP)-grasp enzymes couple amino acids to the β-lactone warhead, generating the bioactive peptide products. After reconstituting the entire pathway to β-lactone peptides in vitro, we go on to deliver a diverse range of analogs through enzymatic cascade reactions. Our approach is more efficient and cleaner than the synthetic methods currently used to produce clinically important warhead-containing peptides.

Rational redesign of the loop dynamics of carbonyl reductase LfSDR1 to improve the stereoselectivity for asymmetric synthesis of bulky chiral alcohols

Engineering biocatalysts with enhanced stereoselectivity is highly desirable, and active-site loop dynamics play an important role in its regulation. However, knowledge of their precise roles in catalysis and evolution is limited. Here, we used the strategy of Rosetta enzyme design combined molecular dynamic simulations (MDs) to reprogram the landscapes of the key active-site loop dynamics of the carbonyl reductase LfSDR1 to improve stereoselectivity. The key flexible loop in the active site showed the potential to regulate the catalytic properties. A library of virtual variants was produced using the Rosetta design and assessed dynamic effect of the loop with the aid of MDs. A potential candidate was obtained with significant stereoselectivity (ee > 99 %) compared to the wild-type (ee = 42 %) without loss of catalytic activity or thermostability. The molecular basis of the catalytic property enhancement was flanked by MDs, which revealed the role of the G92L mutation in regulating loop dynamics to stabilize the environment of the active site. Finally, a series of the challenge bulky substrate derivatives were assessed using the G92L variant, and all showed improved stereoselectivity ee > 99 %. This study provides novel insights for improving stereoselectivity through rational engineering of the loop dynamics of biocatalysts.

Enabling high-throughput enzyme discovery and engineering with a low-cost, robot-assisted pipeline

As genomic databases expand and artificial intelligence tools advance, there is a growing demand for efficient characterization of large numbers of proteins. To this end, here we describe a generalizable pipeline for high-throughput protein purification using small-scale expression in E. coli and an affordable liquid-handling robot. This low-cost platform enables the purification of 96 proteins in parallel with minimal waste and is scalable for processing hundreds of proteins weekly per user. We demonstrate the performance of this method with the expression and purification of the leading poly(ethylene terephthalate) hydrolases reported in the literature. Replicate experiments demonstrated reproducibility and enzyme purity and yields (up to 400 µg) sufficient for comprehensive analyses of both thermostability and activity, generating a standardized benchmark dataset for comparing these plastic-degrading enzymes. The cost-effectiveness and ease of implementation of this platform render it broadly applicable to diverse protein characterization challenges in the biological sciences.

An Extremely Sensitive Ultra-High Throughput Growth Selection Assay for the Identification of Amidase Activity

In the last decades, biocatalysis has offered new perspectives for the synthesis of (chiral) amines, which are essential building blocks for pharmaceuticals, fine and bulk chemicals. In this regard, amidases have been employed due to their broad substrate scope and their independence from expensive cofactors. To expand the repertoire of amidases, tools for their rapid identification and characterization are greatly demanded. In this work an ultra-high throughput growth selection assay based on the production of the folate precursor p-aminobenzoic acid (PABA) is introduced to identify amidase activity. PABA-derived amides structurally mimic the broad class of commonly used chromogenic substrates derived from p-nitroaniline. This suggests that the assay should be broadly applicable for the identification of amidases. Unlike conventional growth selection assays that rely on substrates as nitrogen or carbon source, our approach requires PABA in sub-nanomolar concentrations, making it exceptionally sensitive and ideal for engineering campaigns that aim at enhancing amidase activities from minimally active starting points, for example. The presented assay offers flexibility in the adjustment of sensitivity to suit project-specific needs using different expression systems and fine-tuning with the antimetabolite sulfathiazole. Application of this PABA-based assay facilitates the screening of millions of enzyme variants on a single agar plate within two days, without the need for laborious sample preparation or expensive instruments, with transformation efficiency being the only limiting factor. KEY POINTS: • Ultra-high throughput assay (tens of millions on one agar plate) for amidase screening • High sensitivity by coupling selection to folate instead of carbon or nitrogen source • Highly adjustable in terms of sensitivity and expression of the engineering target.

Immobilized Multi-Enzyme/Nanozyme Biomimetic Cascade Catalysis for Biosensing Applications

Multiple enzyme-induced cascade catalysis has an indispensable role in the process of complex life activities, and is widely used to construct robust biosensors for analyzing various targets. The immobilized multi-enzyme cascade catalysis system is a novel biomimetic catalysis strategy that immobilizes various enzymes with different functions in stable carriers to simulate the synergistic catalysis of multiple enzymes in biological systems, which enables high stability of enzymes and efficiency enzymatic cascade catalysis. Nanozymes, a type of nanomaterial with intrinsic enzyme-like characteristics and excellent stabilities, are also widely applied instead of enzymes to construct immobilized cascade systems, achieving better catalytic performance and reaction stability. Due to good stability, reusability, and remarkably high efficiency, the immobilized multi-enzyme/nanozyme biomimetic cascade catalysis systems show distinct advantages in promoting signal transduction and amplification, thereby attracting vast research interest in biosensing applications. This review focuses on the research progress of the immobilized multi-enzyme/nanozyme biomimetic cascade catalysis systems in recent years. The construction approaches, factors affecting the efficiency, and applications for sensitive biosensing are discussed in detail. Further, their challenges and outlooks for future study are also provided.

Controlled enzymatic synthesis of oligonucleotides

Oligonucleotides are advancing as essential materials for the development of new therapeutics, artificial genes, or in storage of information applications. Hitherto, our capacity to write (i.e., synthesize) oligonucleotides is not as efficient as that to read (i.e., sequencing) DNA/RNA. Alternative, biocatalytic methods for the de novo synthesis of natural or modified oligonucleotides are in dire need to circumvent the limitations of traditional synthetic approaches. This Perspective article summarizes recent progress made in controlled enzymatic synthesis, where temporary blocked nucleotides are incorporated into immobilized primers by polymerases. While robust protocols have been established for DNA, RNA or XNA synthesis is more challenging. Nevertheless, using a suitable combination of protected nucleotides and polymerase has shown promises to produce RNA oligonucleotides even though the production of long DNA/RNA/XNA sequences (>1000 nt) remains challenging. We surmise that merging ligase- and polymerase-based synthesis would help to circumvent the current shortcomings of controlled enzymatic synthesis.

A refined picture of the native amine dehydrogenase family revealed by extensive biodiversity screening

Native amine dehydrogenases offer sustainable access to chiral amines, so the search for scaffolds capable of converting more diverse carbonyl compounds is required to reach the full potential of this alternative to conventional synthetic reductive aminations. Here we report a multidisciplinary strategy combining bioinformatics, chemoinformatics and biocatalysis to extensively screen billions of sequences in silico and to efficiently find native amine dehydrogenases features using computational approaches. In this way, we achieve a comprehensive overview of the initial native amine dehydrogenase family, extending it from 2,011 to 17,959 sequences, and identify native amine dehydrogenases with non-reported substrate spectra, including hindered carbonyls and ethyl ketones, and accepting methylamine and cyclopropylamine as amine donor. We also present preliminary model-based structural information to inform the design of potential (R)-selective amine dehydrogenases, as native amine dehydrogenases are mostly (S)-selective. This integrated strategy paves the way for expanding the resource of other enzyme families and in highlighting enzymes with original features.

Designing Michaelases: exploration of novel protein scaffolds for iminium biocatalysis

Biocatalysis is becoming a powerful and sustainable alternative for asymmetric catalysis. However, enzymes are often restricted to metabolic and less complex reactivities. This can be addressed by protein engineering, such as incorporating new-to-nature functional groups into proteins through the so-called expansion of the genetic code to produce artificial enzymes. Selecting a suitable protein scaffold is a challenging task that plays a key role in designing artificial enzymes. In this work, we explored different protein scaffolds for an abiological model of iminium-ion catalysis, Michael addition of nitromethane into E-cinnamaldehyde. We studied scaffolds looking for open hydrophobic pockets and enzymes with described binding sites for the targeted substrate. The proteins were expressed and variants harboring functional amine groups - lysine, p-aminophenylalanine, or N6-(D-prolyl)-L-lysine - were analyzed for the model reaction. Among the newly identified scaffolds, a thermophilic ene-reductase from Thermoanaerobacter pseudethanolicus was shown to be the most promising biomolecular scaffold for this reaction.

Machine learning guided rational design of a non-heme iron-based lysine dioxygenase improves its total turnover number

Highly selective C-H functionalization remains an ongoing challenge in organic synthetic methodologies. Biocatalysts are robust tools for achieving these difficult chemical transformations. Biocatalyst engineering has often required directed evolution or structure-based rational design campaigns to improve their activities. In recent years, machine learning has been integrated into these workflows to improve the discovery of beneficial enzyme variants. In this work, we combine a structure-based machine-learning algorithm with classical molecular dynamics simulations to down select mutations for rational design of a non-heme iron-dependent lysine dioxygenase, LDO. This approach consistently resulted in functional LDO mutants and circumvents the need for extensive study of mutational activity before-hand. Our rationally designed single mutants purified with up to 2-fold higher yields than WT and displayed higher total turnover numbers (TTN). Combining five such single mutations into a pentamutant variant, LPNYI LDO, leads to a 40% improvement in the TTN (218±3) as compared to WT LDO (TTN = 160±2). Overall, this work offers a low-barrier approach for those seeking to synergize machine learning algorithms with pre-existing protein engineering strategies.

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.

Reshaping Phosphatase Substrate Preference for Controlled Biosynthesis Using a "Design-Build-Test-Learn" Framework

Biosynthesis is the application of enzymes in microbial cell factories and has emerged as a promising alternative to chemical synthesis. However, natural enzymes with limited catalytic performance often need to be engineered to meet specific needs through a time-consuming trial-and-error process. This study presents a quantum mechanics (QM)-incorporated design-build-test-learn (DBTL) framework to rationally design phosphatase BT4131, an enzyme with an ambiguous substrate spectrum involved in N-acetylglucosamine (GlcNAc) biosynthesis. First, mutant M1 (L129Q) is designed using force field-based methods, resulting in a 1.4-fold increase in substrate preference (kcat/Km) toward GlcNAc-6-phosphate (GlcNAc6P). QM calculations indicate that the shift in substrate preference is caused by a 13.59 kcal mol-1 reduction in activation energy. Furthermore, an iterative computer-aided design is conducted to stabilize the transition state. As a result, mutant M4 (I49Q/L129Q/G172L) with a 9.5-fold increase in kcat-GlcNAc6P/Km-GlcNAc6P and a 59% decrease in kcat-Glc6P/Km-Glc6P is highly desirable compared to the wild type in the GlcNAc-producing chassis. The GlcNAc titer increases to 217.3 g L-1 with a yield of 0.597 g (g glucose)-1 in a 50-L bioreactor, representing the highest reported level. Collectively, this DBTL framework provides an easy yet fascinating approach to the rational design of enzymes for industrially viable biocatalysts.

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.

The Impact of Metagenomics on Biocatalysis

In the ever-growing demand for sustainable ways to produce high-value small molecules, biocatalysis has come to the forefront of greener routes to these chemicals. As such, the need to constantly find and optimise suitable biocatalysts for specific transformations has never been greater. Metagenome mining has been shown to rapidly expand the toolkit of promiscuous enzymes needed for new transformations, without requiring protein engineering steps. If protein engineering is needed, the metagenomic candidate can often provide a better starting point for engineering than a previously discovered enzyme on the open database or from literature, for instance. In this review, we highlight where metagenomics has made substantial impact on the area of biocatalysis in recent years. We review the discovery of enzymes in previously unexplored or 'hidden' sequence space, leading to the characterisation of enzymes with enhanced properties that originate from natural selection pressures in native environments.

An In Vitro Hybrid Biocatalytic System Enabled by a Combination of Surface-Displayed, Purified, and Cell-Free Expressed Enzymes

Enzymatic cascades have become a green and sustainable approach for the synthesis of valuable chemicals and pharmaceuticals. Using sequential enzymes to construct a multienzyme complex is an effective way to enhance the overall performance of biosynthetic routes. Here we report the design of an efficient in vitro hybrid biocatalytic system by assembling three enzymes that can convert styrene to (S)-1-phenyl-1,2-ethanediol. Specifically, we prepared the three enzymes in different ways, which were cell surface-displayed, purified, and cell-free expressed. To assemble them, we fused two orthogonal peptide-protein pairs (i.e., SpyTag/SpyCatcher and SnoopTag/SnoopCatcher) to the three enzymes, allowing their spatial organization by covalent assembly. By doing this, we constructed a multienzyme complex, which could enhance the production of (S)-1-phenyl-1,2-ethanediol by 3 times compared to the free-floating enzyme system without assembly. After optimization of the reaction system, the final product yield reached 234.6 μM with a substrate conversion rate of 46.9% (based on 0.5 mM styrene). Taken together, our strategy integrates the merits of advanced biochemical engineering techniques, including cellular surface display, spatial enzyme organization, and cell-free expression, which offers a new solution for chemical biosynthesis by enzymatic cascade biotransformation. We, therefore, anticipate that our approach will hold great potential for designing and constructing highly efficient systems to synthesize chemicals of agricultural, industrial, and pharmaceutical significance.

Engineering the activity and thermostability of a carboxylic acid reductase in the conversion of vanillic acid to vanillin

Vanillin is a valuable natural product that can be used as a fragrance and additive. Recent research in the biosynthesis of vanillin has brought attention to a key enzyme, carboxylic acid reductase (CAR), which catalyzes the reduction of vanillic acid to vanillin. Nevertheless, the biosynthesis of vanillin is hampered by the low activity and stability of CAR. As such, a rational design campaign was conducted on a well-documented carboxylic acid reductase from Segniliparus rugosus (SrCAR), using vanillic acid as the model substrate. After combined active site saturation and iterative site-specific mutagenesis, the best quadruple mutant N292H/K524S/A627L/E1121W (M3) was successfully obtained. In comparison to the wildtype SrCAR, M3 demonstrated a 4.2-fold increase in catalytic efficiency (kcat/Km), and its half-life (t1/2) was enhanced by 3.8 times up to 385.08 minutes at 40 °C. In silico docking and molecular dynamics simulation provided insights into the improved activity and stability. In the subsequent preparative-scale reaction with 100 mM (16.8 g L-1) vanillic acid, the whole cell catalysis utilizing M3 produced 10.15 g·L-1 of vanillin and 1.11 g·L-1 of vanillyl alcohol, respectively. This work demonstrates a dual improvement in the activity and thermal stability of SrCAR, thereby potentially facilitating the application of carboxylic acid reductase in the biosynthesis of vanillin.

Enzymatic synthesis of mono- and trifluorinated alanine enantiomers expands the scope of fluorine biocatalysis

Fluorinated amino acids serve as an entry point for establishing new-to-Nature chemistries in biological systems, and novel methods are needed for the selective synthesis of these building blocks. In this study, we focused on the enzymatic synthesis of fluorinated alanine enantiomers to expand fluorine biocatalysis. The alanine dehydrogenase from Vibrio proteolyticus and the diaminopimelate dehydrogenase from Symbiobacterium thermophilum were selected for in vitro production of (R)-3-fluoroalanine and (S)-3-fluoroalanine, respectively, using 3-fluoropyruvate as the substrate. Additionally, we discovered that an alanine racemase from Streptomyces lavendulae, originally selected for setting an alternative enzymatic cascade leading to the production of these non-canonical amino acids, had an unprecedented catalytic efficiency in β-elimination of fluorine from the monosubstituted fluoroalanine. The in vitro enzymatic cascade based on the dehydrogenases of V. proteolyticus and S. thermophilum included a cofactor recycling system, whereby a formate dehydrogenase from Pseudomonas sp. 101 (either native or engineered) coupled formate oxidation to NAD(P)H formation. Under these conditions, the reaction yields for (R)-3-fluoroalanine and (S)-3-fluoroalanine reached >85% on the fluorinated substrate and proceeded with complete enantiomeric excess. The selected dehydrogenases also catalyzed the conversion of trifluoropyruvate into trifluorinated alanine as a first-case example of fluorine biocatalysis with amino acids carrying a trifluoromethyl group.

From Ground-State to Excited-State Activation Modes: Flavin-Dependent "Ene"-Reductases Catalyzed Non-natural Radical Reactions

Enzymes are desired catalysts for chemical synthesis, because they can be engineered to provide unparalleled levels of efficiency and selectivity. Yet, despite the astonishing array of reactions catalyzed by natural enzymes, many reactivity patterns found in small molecule catalysts have no counterpart in the living world. With a detailed understanding of the mechanisms utilized by small molecule catalysts, we can identify existing enzymes with the potential to catalyze reactions that are currently unknown in nature. Over the past eight years, our group has demonstrated that flavin-dependent "ene"-reductases (EREDs) can catalyze various radical-mediated reactions with unparalleled levels of selectivity, solving long-standing challenges in asymmetric synthesis.This Account presents our development of EREDs as general catalysts for asymmetric radical reactions. While we have developed multiple mechanisms for generating radicals within protein active sites, this account will focus on examples where flavin mononucleotide hydroquinone (FMNhq) serves as an electron transfer radical initiator. While our initial mechanistic hypotheses were rooted in electron-transfer-based radical initiation mechanisms commonly used by synthetic organic chemists, we ultimately uncovered emergent mechanisms of radical initiation that are unique to the protein active site. We will begin by covering intramolecular reactions and discussing how the protein activates the substrate for reduction by altering the redox-potential of alkyl halides and templating the charge transfer complex between the substrate and flavin-cofactor. Protein engineering has been used to modify the fundamental photophysics of these reactions, highlighting the opportunity to tune these systems further by using directed evolution. This section highlights the range of coupling partners and radical termination mechanisms available to intramolecular reactions.The next section will focus on intermolecular reactions and the role of enzyme-templated ternary charge transfer complexes among the cofactor, alkyl halide, and coupling partner in gating electron transfer to ensure that it only occurs when both substrates are bound within the protein active site. We will highlight the synthetic applications available to this activation mode, including olefin hydroalkylation, carbohydroxylation, arene functionalization, and nitronate alkylation. This section also discusses how the protein can favor mechanistic steps that are elusive in solution for the asymmetric reductive coupling of alkyl halides and nitroalkanes. We are aware of several recent EREDs-catalyzed photoenzymatic transformations from other groups. We will discuss results from these papers in the context of understanding the nuances of radical initiation with various substrates.These biocatalytic asymmetric radical reactions often complement the state-of-the-art small-molecule-catalyzed reactions, making EREDs a valuable addition to a chemist's synthetic toolbox. Moreover, the underlying principles studied with these systems are potentially operative with other cofactor-dependent proteins, opening the door to different types of enzyme-catalyzed radical reactions. We anticipate that this Account will serve as a guide and inspire broad interest in repurposing existing enzymes to access new transformations.

Atomic Engineering of Single-Atom Nanozymes for Biomedical Applications

Single-atom nanozymes (SAzymes) showcase not only uniformly dispersed active sites but also meticulously engineered coordination structures. These intricate architectures bestow upon them an exceptional catalytic prowess, thereby captivating numerous minds and heralding a new era of possibilities in the biomedical landscape. Tuning the microstructure of SAzymes on the atomic scale is a key factor in designing targeted SAzymes with desirable functions. This review first discusses and summarizes three strategies for designing SAzymes and their impact on reactivity in biocatalysis. The effects of choices of carrier, different synthesis methods, coordination modulation of first/second shell, and the type and number of metal active centers on the enzyme-like catalytic activity are unraveled. Next, a first attempt is made to summarize the biological applications of SAzymes in tumor therapy, biosensing, antimicrobial, anti-inflammatory, and other biological applications from different mechanisms. Finally, how SAzymes are designed and regulated for further realization of diverse biological applications is reviewed and prospected. It is envisaged that the comprehensive review presented within this exegesis will furnish novel perspectives and profound revelations regarding the biomedical applications of SAzymes.

Enhancing the catalytic efficiency of M32 carboxypeptidase by semi-rational design and its applications in food taste improvement

BACKGROUND

Carboxypeptidase is an exopeptidase that hydrolyzes amino acids at the C-terminal end of the peptide chain and has a wide range of applications in food. However, in industrial applications, the relatively low catalytic efficiency of carboxypeptidases is one of the main limiting factors for industrialization.

RESULTS

The study has enhanced the catalytic efficiency of Bacillus megaterium M32 carboxypeptidase (BmeCPM32) through semi-rational design. Firstly, the specific activity of the optimal mutant, BmeCPM32-M2, obtained through single-site mutagenesis and combinatorial mutagenesis, was 2.2-fold higher than that of the wild type (187.9 versus 417.8 U mg-1), and the catalytic efficiency was 2.9-fold higher (110.14 versus 325.75 s-1 mmol-1). Secondly, compared to the wild type, BmeCPM32-M2 exhibited a 1.8-fold increase in half-life at 60 °C, with no significant changes in its enzymatic properties (optimal pH, optimal temperature). Finally, BmeCPM32-M2 significantly increased the umami intensity of soy protein isolate hydrolysate by 55% and reduced bitterness by 83%, indicating its potential in developing tasty protein components.

CONCLUSION

Our research has revealed that the strategy based on protein sequence evolution and computational residue mutation energy led to an improved catalytic efficiency of BmeCPM32. Molecular dynamics simulations have revealed that a smaller substrate binding pocket and increased enzyme-substrate affinity are the reasons for the enhanced catalytic efficiency. Furthermore the number of hydrogen bonds and solvent and surface area may contribute to the improvement of thermostability. Finally, the de-bittering effect of BmeCPM32-M2 in soy protein isolate hydrolysate suggests its potential in developing palatable protein components. © 2024 Society of Chemical Industry.

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.

Choose Your Own Adventure: A Comprehensive Database of Reactions Catalyzed by Cytochrome P450 BM3 Variants

Cytochrome P450 BM3 monooxygenase is the topic of extensive research as many researchers have evolved this enzyme to generate a variety of products. However, the abundance of information on increasingly diversified variants of P450 BM3 that catalyze a broad array of chemistry is not in a format that enables easy extraction and interpretation. We present a database that categorizes variants by their catalyzed reactions and includes details about substrates to provide reaction context. This database of >1500 P450 BM3 variants is downloadable and machine-readable and includes instructions to maximize ease of gathering information. The database allows rapid identification of commonly reported substitutions, aiding researchers who are unfamiliar with the enzyme in identifying starting points for enzyme engineering. For those actively engaged in engineering P450 BM3, the database, along with this review, provides a powerful and user-friendly platform to understand, predict, and identify the attributes of P450 BM3 variants, encouraging the further engineering of this enzyme.

Directed Evolution and Immobilization of Lactobacillus brevis Alcohol Dehydrogenase for Chemo-Enzymatic Synthesis of Rivastigmine

Rivastigmine is one of the several pharmaceuticals widely prescribed for the treatment of Alzheimer's disease. However, its practical synthesis still faces many issues, such as the involvement of toxic metals and harsh reaction conditions. Herein, we report a chemo-enzymatic synthesis of Rivastigmine. The key chiral intermediate was synthesized by an engineered alcohol dehydrogenase from Lactobacillus brevis (LbADH). A semi-rational approach was employed to improve its catalytic activity and thermal stability. Several LbADH variants were obtained with a remarkable increase in activity and melting temperature. Exploration of the substrate scope of these variants demonstrated improved activities toward various ketones, especially acetophenone analogs. To further recycle and reuse the biocatalyst, one LbADH variant and glucose dehydrogenase were co-immobilized on nanoparticles. By integrating enzymatic and chemical steps, Rivastigmine was successfully synthesized with an overall yield of 66 %. This study offers an efficient chemo-enzymatic route for Rivastigmine and provides several efficient LbADH variants with a broad range of potential applications.

Near-infrared light-driven asymmetric photolytic reduction of ketone using inorganic-enzyme hybrid biocatalyst

Effective photolytic regeneration of the NAD(P)H cofactor in enzymatic reductions is an important and elusive goal in biocatalysis. It can, in principle, be achieved using a near-infrared light (NIR) driven artificial photosynthesis system employing H2O as the sacrificial reductant. To this end we utilized TiO2/reduced graphene quantum dots (r-GQDs), combined with a novel rhodium electron mediator, to continuously supply NADPH in situ for aldo-keto reductase (AKR) mediated asymmetric reductions under NIR irradiation. This upconversion system, in which the Ti-O-C bonds formed between r-GQDs and TiO2 enabled efficient interfacial charge transfer, was able to regenerate NADPH efficiently in 64 % yield in 105 min. Based on this, the pharmaceutical intermediate (R)-1-(3,5-bis(trifluoromethyl)phenyl)ethan-1-ol was obtained, in 84 % yield and 99.98 % ee, by reduction of the corresponding ketone. The photo-enzymatic system is recyclable with a polymeric electron mediator, which maintained 66 % of its original catalytic efficiency and excellent enantioselectivity (99.9 % ee) after 6 cycles.

Recent advances in the synthesis of single-stranded DNA in vitro

Single-stranded DNA (ssDNA) is the foundation of modern biology, with wide applications in gene editing, sequencing, DNA information storage, and materials science. However, synthesizing ssDNA with high efficiency, high throughput, and low error rate in vitro remains a major challenge. Various methods have been developed for ssDNA synthesis, and some significant results have been achieved. In this review, six main methods were introduced, including solid-phase oligonucleotide synthesis, terminal deoxynucleotidyl transferase-based ssDNA synthesis, reverse transcription, primer exchange reaction, asymmetric polymerase chain reaction, and rolling circle amplification. The advantages and limitations of each method were compared, as well as illustrate their representative achievements and applications. Especially, rolling circle amplification has received significant attention, including ssDNA synthesis, assembly, and application based on recent work. Finally, the future challenges and opportunities of ssDNA synthesis were summarized and discussed. Envisioning the development of new methods and significant progress will be made in the near future with the efforts of scientists around the world.

Stereodivergent Protein Engineering of Fatty Acid Photodecarboxylase for Light-Driven Kinetic Resolution of Sec-Alcohol Oxalates

Stereodivergent engineering of one enzyme to create stereocomplementary variants for synthesizing optically pure molecules with tailor-made (R) or (S) configurations on an optional basis is highly desirable and challenging. This study aimed to engineer fatty acid photodecarboxylase from Chlorella variabilis (CvFAP) using the focused rational iterative site-specific mutagenesis (FRISM) strategy to obtain two highly stereocomplementary variants with excellent selectivity (both giving products with up to 99 % e.e.). These variants were used for the CvFAP-catalyzed light-driven kinetic resolution of oxalates or oxamic acids prepared from the corresponding sec-alcohols or amines, providing a new biotransformation process for preparing chiral sec-alcohols and amines. Molecular dynamics simulation, kinetic data and transient spectra revealed the source of selectivity. This study represents the first example of the kinetic resolution of sec-alcohols or amines catalyzed by a pair of stereocomplementary CvFAPs.

Effective engineering of a ketoreductase for the biocatalytic synthesis of an ipatasertib precursor

Semi-rational enzyme engineering is a powerful method to develop industrial biocatalysts. Profiting from advances in molecular biology and bioinformatics, semi-rational approaches can effectively accelerate enzyme engineering campaigns. Here, we present the optimization of a ketoreductase from Sporidiobolus salmonicolor for the chemo-enzymatic synthesis of ipatasertib, a potent protein kinase B inhibitor. Harnessing the power of mutational scanning and structure-guided rational design, we created a 10-amino acid substituted variant exhibiting a 64-fold higher apparent kcat and improved robustness under process conditions compared to the wild-type enzyme. In addition, the benefit of algorithm-aided enzyme engineering was studied to derive correlations in protein sequence-function data, and it was found that the applied Gaussian processes allowed us to reduce enzyme library size. The final scalable and high performing biocatalytic process yielded the alcohol intermediate with ≥ 98% conversion and a diastereomeric excess of 99.7% (R,R-trans) from 100 g L-1 ketone after 30 h. Modelling and kinetic studies shed light on the mechanistic factors governing the improved reaction outcome, with mutations T134V, A238K, M242W and Q245S exerting the most beneficial effect on reduction activity towards the target ketone.

Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering

Enzymes can be engineered at the level of their amino acid sequences to optimize key properties such as expression, stability, substrate range, and catalytic efficiency-or even to unlock new catalytic activities not found in nature. Because the search space of possible proteins is vast, enzyme engineering usually involves discovering an enzyme starting point that has some level of the desired activity followed by directed evolution to improve its "fitness" for a desired application. Recently, machine learning (ML) has emerged as a powerful tool to complement this empirical process. ML models can contribute to (1) starting point discovery by functional annotation of known protein sequences or generating novel protein sequences with desired functions and (2) navigating protein fitness landscapes for fitness optimization by learning mappings between protein sequences and their associated fitness values. In this Outlook, we explain how ML complements enzyme engineering and discuss its future potential to unlock improved engineering outcomes.

Enzymes in "Green" Synthetic Chemistry: Laccase and Lipase

Enzymes play an important role in numerous natural processes and are increasingly being utilized as environmentally friendly substitutes and alternatives to many common catalysts. Their essential advantages are high catalytic efficiency, substrate specificity, minimal formation of byproducts, and low energy demand. All of these benefits make enzymes highly desirable targets of academic research and industrial development. This review has the modest aim of briefly overviewing the classification, mechanism of action, basic kinetics and reaction condition effects that are common across all six enzyme classes. Special attention is devoted to immobilization strategies as the main tools to improve the resistance to environmental stress factors (temperature, pH and solvents) and prolong the catalytic lifecycle of these biocatalysts. The advantages and drawbacks of methods such as macromolecular crosslinking, solid scaffold carriers, entrapment, and surface modification (covalent and physical) are discussed and illustrated using numerous examples. Among the hundreds and possibly thousands of known and recently discovered enzymes, hydrolases and oxidoreductases are distinguished by their relative availability, stability, and wide use in synthetic applications, which include pharmaceutics, food and beverage treatments, environmental clean-up, and polymerizations. Two representatives of those groups-laccase (an oxidoreductase) and lipase (a hydrolase)-are discussed at length, including their structure, catalytic mechanism, and diverse usage. Objective representation of the current status and emerging trends are provided in the main conclusions.

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.

The shortest path method (SPM) webserver for computational enzyme design

SPMweb is the online webserver of the Shortest Path Map (SPM) tool for identifying the key conformationally-relevant positions of a given enzyme structure and dynamics. The server is built on top of the DynaComm.py code and enables the calculation and visualization of the SPM pathways. SPMweb is easy-to-use as it only requires three input files: the three-dimensional structure of the protein of interest, and the two matrices (distance and correlation) previously computed from a Molecular Dynamics simulation. We provide in this publication information on how to generate the files for SPM construction even for non-expert users and discuss the most relevant parameters that can be modified. The tool is extremely fast (it takes less than one minute per job), thus allowing the rapid identification of distal positions connected to the active site pocket of the enzyme. SPM applications expand from computational enzyme design, especially if combined with other tools to identify the preferred substitution at the identified position, but also to rationalizing allosteric regulation, and even cryptic pocket identification for drug discovery. The simple user interface and setup make the SPM tool accessible to the whole scientific community. SPMweb is freely available for academia at http://spmosuna.com/.