ACS Catalysis

In Anabaena variabilis (Trichormus variabilis) phenylalanine ammonia-lyase (AvPAL), a conserved lid-like loop sits over the active site and has been studied both for its role in positioning a catalytic tyrosine and for its contribution to phenylalanine aminomutase (PAM) activity. While the active site architecture and substrate specificity of AvPAL have been extensively characterized, the dynamic behavior of this unstructured loop beyond its role in catalysis remains poorly understood. Here, we investigate the functional role of this loop by restricting its mobility through targeted interchain disulfide bond engineering. Three in-house approaches were designed to predict ideal cysteine residue pairs: (i) quantifying pair interaction energies via electrostatic and van der Waals forces, (ii) generating a contact map of residues within 5 [A] proximity, and (iii) implementing a machine-learning model trained on datasets from PDBCYS, SPX, and an internal database to rank cysteine pair likelihood within disulfide bond geometric constraints. Our machine-learning-guided strategy yielded a successful variant with complete oxidation efficiency in E. coli. Rigidification of this loop reveals that it also functions as a regulator of substrate specificity. Multi-scale molecular simulation analyses (molecular dynamics, metadynamics, quantum/molecular mechanics) reveal that this modification alters the active-site pocket by reducing the conformational dynamics of substrate binding. Our findings underscore the delicate balance between enzyme flexibility and catalytic efficiency, providing novel insights into the role of this understudied dynamic loop region in AvPAL.

The ability of SAM-dependent enzymes to accept S-adenosyl-D-methionine [D-SAM, (SS,RC)-SAM] instead of the native cofactor S-adenosyl-L-methionine [L-SAM, (SS,SC)-SAM] remains largely unexplored. Challenging the stereochemical preference of SAM-dependent enzymes, we investigated the ability of different enzyme classes to accept D-SAM. Contrary to common assumptions, the tested N- and O-methyl transferases (MTs), as well as one of the examined C-MTs accepted D-SAM. Docking studies suggest that acceptance of D-SAM by C-MTs may be influenced by the angle between the transferable methyl group of SAM and the nucleophilic carbon of the substrate, along with enzyme and substrate flexibility. In addition to conventional MTs, the radical SAM glutamine C-MT QCMT showed low but detectable methylation activity with D-SAM. Furthermore, the azetidine-2-carboxylic acid synthase AzeJ not only uses D-SAM but also incorporates the stereocentre of D-methionine into the cyclic amino acid product. The pyridoxal 5'-phosphate (PLP)-dependent enzyme 1-aminocyclopropyl-1-carboxylic acid synthase (ACCS) also showed detectable turnover with D-SAM. These findings broaden the understanding of enzyme stereoselectivity, provide an overview of D-SAM-utilising enzymes, and identify first enzyme systems that may serve as starting points for engineering efforts aimed at shifting cofactor preference towards D-SAM.

Multi-step enzymatic reaction cascades often involve cofactors that serve as electron donors/acceptors in addition to the primary substrates. The co-localization of cascades can lead to cross-talk and competition, which can be unfavorable for the production of a targeted product. Orthogonal pathways allow reactions of interest to operate independently from the metabolic reactions within a cell; non-canonical cofactor analogs have been explored as a means to create these orthogonal pathways. Here, we aimed to engineer the formate dehydrogenase from Candid boidinii (CbFDH) for activity with the non-canonical cofactor nicotinamide adenine mononucleotide (NMN(H)). We used PyRosetta and structural alignment to design mutations that enable CbFDH to use NMN+ for the oxidation of formate. Although the suggested mutations did not result in enhanced activity with NMN+, we found that PyRosetta was able to easily design single mutations that disrupted all enzymatic activity.

The rapid rise of antibiotic-resistant bacteria necessitates the search for alternative, unconventional solutions, such as targeting bacterial communication. Signal disruption can be achieved by enzymatic degradation of signaling compounds, reducing the expression of genes responsible for virulence, biofilm formation, and drug resistance while evading common resistance mechanisms. Therefore, enzymes with such activity have considerable potential as antimicrobial agents for medicine, industry, and other areas of life. Here, we designed molecular gates that control the binding site of penicillin G acylase to shift its preference from native substrate to signaling molecules. Using an ensemble-based design, three variants carrying triple-point mutations were proposed and experimentally characterized. Integrated inference from biochemical and computational analyses demonstrated that these three variants had markedly reduced activity towards penicillin and each preferred specific signal molecules of different pathogenic bacteria, exhibiting up to three orders of magnitude shifts in substrate specificity. Curiously, while we could consistently expand the pockets in these mutants, the reactive binding of larger substrates was limited, either by overpromoting or overstabilizing the pocket dynamics. Overall, we demonstrated the designability of this acylase for signal disruption and provided insights into the role of appropriately modulated pocket dynamics for such a function. The improved mutants, the knowledge gained, and the computational workflow developed to prioritize large datasets of promising variants may provide a suitable toolbox for future exploration and design of enzymes tailored to disrupt specific signaling pathways as viable antimicrobial agents.

Efficient regeneration of cofactors is vital for the establishment of continuous biocatalytic processes. Formate is an ideal electron donor for cofactor regeneration due to its general availability, low reduction potential, and benign byproduct (CO2). However, formate dehydrogenases (FDHs) are usual specific to NAD+, such that NADPH regeneration with formate is challenging. Previous studies reported naturally occurring FDHs or engineered FDHs that accept NADP+, but these enzymes show low kinetic efficiencies and specificities. Here, we harness the power of natural selection to engineer FDH variants to simultaneously optimize three properties: kinetic efficiency with NADP+, specificity towards NADP+, and affinity towards formate. By simultaneously mutating multiple residues of FDH from Pseudomonas sp. 101, which exhibits no initial activity towards NADP+, we generate a library of >106 variants. We introduce this library into an E. coli strain that cannot produce NADPH. By selecting for growth with formate as sole NADPH source, we isolate several enzyme variants that support efficient NADPH regeneration. We find that the kinetically superior enzyme variant, harboring five mutations, has 5-fold higher efficiency and 13-fold higher specificity than the best enzyme previously engineered, while retaining high affinity towards formate. By using molecular dynamics simulations, we reveal the contribution of each mutation to the superior kinetics of this variant. We further determine how non-additive epistatic effects improve multiple parameters simultaneously. Our work demonstrates the capacity of in vivo selection to identify superior enzyme variants carrying multiple mutations which would be almost impossible to find using conventional screening methods.

Bacterial aryl malonate decarboxylase is a cofactor-free enzyme that generates a wide spectrum of -chiral carboxylic acids in outstanding optical purity, including several non-steroidal anti-inflammatory drugs and chiral building blocks. The well-characterized AMDase from Bordetella bronchiseptica (BbAMDase) and related enzymes of the same family have three main limitations: (i) low stability, both operational and thermal, and (ii) limited substrate spectrum regarding the size of the smaller substituent on the -C-atom and (iii) low stereoselectivity towards -alkenyl--alkyl malonic acids. To address these limitations, we expanded the structural diversity of the AMDase family by ancestral sequence reconstruction (ASR). The phylogenetic analysis of the decarboxylase revealed conserved structural motifs and key amino acids in the hydrophobic active-site cavity, a catalytic motif crucial for activity and selectivity of the enzyme. The analysis highlighted the natural distribution of amino acid exchanges that had been previously identified in enzyme engineering campaigns. AMDase ancestors showed higher stability, activity, and, in one case, also stereoselectivity than BbAMDase. While the up to 10 {degrees}C higher unfolding temperature of AMDase ancestors is a frequent result in ASR, the improvement of the half-life time of 294-fold of ancestor N131 was surprising. Ancestor N31 formed 2-methyl-but-3-enoic acid from its corresponding malonic acid in an optical purity of 99.7% eeR. The extant BbAMDase produces this compound in much lower optical purity (96.8% eeR), which corresponds to a 1.4 kcal{middle dot}mol-1 difference of the transition state free energy of the two reaction paths leading to the different enantiomers. Furthermore, the stereoselectivity of the ancestors was completely inverted by switch of the catalytic cysteine residues G74C/C188G.

Loop engineering of enzymes remains challenging due to high flexibility and conformational complexity, posing a bottleneck for deep-learning-based design. Here, we constructed mutant libraries for three loops of TEV protease to assess combining directed evolution with deep learning. Using an M13 phagemid-based selection system, the three libraries were screened, resulting in a Loop 1 variant (HyperTEV60/L1) that significantly enhanced the Michaelis constant (Km) of the HyperTEV60 scaffold, a highly active mutant identified by ProteinMPNN. Structural modeling suggested that a single-residue deletion and substitution in Loop 1 expands the substrate binding pocket, accounting for the improved Km. Although the catalytic efficiency kcat/Km of HyperTEV60/L1 was only marginally higher than HyperTEV60, due to a kcat decrease, our results reveal that the phagemid-based selection system tended to find variants optimizing Km. This study demonstrates that combining deep-learning-based global optimization with localized directed evolution maximizes the probability of discovering distinct, high-performance enzyme variants.

White-rot fungi secrete a repertoire of high-redox potential oxidoreductases to efficiently decompose lignin. Of these enzymes, versatile peroxidases (VPs) are the most promiscuous biocatalysts. VPs are attractive enzymes for research and industrial use, but their recombinant production is extremely challenging. To date, only a single VP has been structurally characterized and optimized for recombinant functional expression, stability and activity. Computational enzyme optimization methods can be applied to many enzymes in parallel, but they require accurate structures. Here, we demonstrate that model structures computed by deep-learning based ab initio structure prediction methods are reliable starting points for one-shot PROSS stability-design calculations. Four designed VPs encoding as many as 43 mutations relative to the wild type enzymes are functionally expressed in yeast whereas their wild type parents are not. Three of these designs exhibit substantial and useful diversity in reactivity profile and tolerance to environmental conditions. The reliability of the new generation of structure predictors and design methods increases the scale and scope of computational enzyme optimization, enabling efficient discovery and exploitation of the functional diversity in natural enzyme families.

Cold active esterases represent an important class of enzymes capable of undertaking useful chemical transformations at low temperatures. EstN7 from Bacillus cohnii represents a true psychrophilic esterase with a temperature optimum below 20{degrees}C. We have recently determined the structure of EstN7 and have used this knowledge to understand substrate specificity and expands its substrate range through protein engineering. Substrate range is determined by a plug at the end of acyl binding pocket that blocks access to a buried water filled cavity, so limiting EstN7 to turnover of C2 and C4 substrates. Data mining revealed a potentially important commercial reaction, conversion of triacetin to only the 1,2-glyceryl diacetate isomer, which the EstN7 could achieve. Residues M187, N211 and W206 were identified as plug residues. M187 was identified as the key plug residue but mutation to alanine destabilised the structure as whole. Another plug mutation, N211A had a stabilising effect on EstN7 and suppressed the destabilising M187A mutation. The M187A-N211A variant had the broadest substrate range, capable of hydrolysing a C8 substrate. Thus, the structure of EstN7 together with focused engineering has provided new insights into the structural stability and substrate specificity that allowed expansion of substrate range.

Enzymes offer unparalleled selectivity and sustainability for chemical synthesis, yet their widespread industrial application is often hindered by the slow and uncertain process of discovering and optimizing suitable biocatalysts. While directed evolution remains the gold standard for enzyme optimization, its success hinges on the availability of a starting enzyme with measurable activity, a persistent bottleneck for many desired functions. Designing libraries likely to contain such functional starting points remains a major challenge. In this work, we use the GenSLM protein language model (PLM) along with a series of filters to generate novel sequences of the {beta}-subunit of tryptophan synthase (TrpB) that express in Escherichia coli, are stable, and are catalytically active in the absence of a TrpA partner. Many generated TrpBs also demonstrated significant substrate promiscuity, accepting non-canonical substrates typically inaccessible to natural TrpBs. Remarkably, several outperformed both natural and laboratory-optimized TrpBs on native and non-canonical substrates. Comparative analysis of the most active and promiscuous generated TrpB and its closest natural homolog confirmed that this enhanced functional versatility does not stem from the natural enzyme, highlighting the creative potential of generative models. Our results demonstrate that the model can generate enzymes which not only preserve natural structure and function but also acquire non-natural properties, establishing PLMs as powerful tools for biocatalyst discovery and engineering, with the potential in some cases to bypass further optimization.

Guanosine 5-monophosphate (GMP) synthetases, enzymes that catalyze the conversion of xanthosine 5-monophosphate (XMP) to GMP are comprised of two different catalytic units, which are either two domains of a polypeptide chain or two subunits that associate to form a complex. The glutamine amidotransferase (GATase) unit hydrolyzes glutamine generating ammonia and the ATP pyrophosphatase (ATPPase) unit catalyzes the formation of AMP-XMP intermediate. The substrate-bound ATPPase allosterically activates GATase and the ammonia thus generated is tunnelled to the ATPPase active site where it reacts with AMP-XMP generating GMP. In ammonia tunnelling enzymes reported thus far, a tight complex of the two subunits is observed, while the interaction of the two subunits of Methanocaldococcus jannaschii GMP synthetase (MjGMPS) is transient with the underlying mechanism of allostery and substrate channelling largely unclear. Here, we present a mechanistic model encompassing the various steps in the catalytic cycle of MjGMPS based on biochemical experiments, crystal structure and cross-linking mass spectrometry guided integrative modelling. pH dependence of enzyme kinetics establish that ammonia is tunnelled across the subunits with the lifetime of the complex being [≤] 0.5 s. The crystal structure of XMP-bound ATPPase subunit reported herein highlights the role of conformationally dynamic loops in enabling catalysis. The structure of MjGMPS derived using restraints obtained from cross-linking mass spectrometry has enabled the visualization of subunit interactions that enable allostery under catalytic conditions. We integrate the results and propose a functional mechanism for MjGMPS detailing the various steps involved in catalysis. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/481963v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@1eb7261org.highwire.dtl.DTLVardef@a25d02org.highwire.dtl.DTLVardef@1885ed7org.highwire.dtl.DTLVardef@ab189_HPS_FORMAT_FIGEXP M_FIG C_FIG

Evolutionary constraints significantly limit the diversity of naturally occurring enzymes, thereby reducing the sequence repertoire available for enzyme discovery and engineering. Recent breakthroughs in protein structure prediction and de novo design, powered by artificial intelligence, now enable us to create enzymes with desired functions without relying on traditional genome mining. Here, we demonstrate a computational strategy for creating new-to-nature PET hydrolases by leveraging the known catalytic mechanisms and implementing multiple deep learning algorithms and molecular computations. This strategy includes the extraction of functional motifs from a template enzyme (here we use leaf-branch compost cutinase, LCC), regeneration of new protein scaffolds, computational screening, experimental validation, and sequence refinement. We successfully replicate PET hydrolytic activity with designer enzymes that are at least 30% shorter in sequence length than LCC. Among them, RsPETase 1 stands out due to its robust expressibility. It exhibits comparable activity to IsPETase and considerable thermostability with a melting temperature of 56 {degrees}C, despite sharing only 34% sequence similarity with LCC. This work suggests that enzyme diversity can be expanded by recapitulating functional motifs with computationally built protein scaffolds, thus generating opportunities to acquire highly active and robust enzymes that do not exist in nature.

Trimethylamine (TMA) is a major contributor to undesirable odours in protein hydrolysates derived from marine by-products, limiting their industrial use. Flavin-containing monooxygenases (FMOs) catalyse the conversion of TMA to the odourless trimethylamine N-oxide (TMAO); however, industrial applications demand enzymes that are both thermally stable and compatible with cost-effective cofactors. A thermostable variant of the Methylophaga aminisulfidivorans FMO (mFMO_20) can function at elevated temperatures but depends exclusively on the expensive and unstable cofactor NADPH. In this study, we investigated whether it is possible to simultaneously enhance thermostability and NADH compatibility using a multi-objective engineering strategy. We first targeted residues in the cofactor binding site of mFMO_20 to restore NADH activity, which had been completely lost despite the wild type enzyme being naturally active with both cofactors. Variants derived from the thermostable scaffold partially recovered NADH activity but showed reduced NADPH activity. Given the wild types inherent NADH compatibility, we next pursued a stability-improvement approach, introducing highly conserved stabilizing mutations. This preserved cofactor competence but produced only modest improvements in thermostability. Finally, by combining physical, evolutionary, and statistical metrics, we obtained variants that retained higher NADPH activity after heat treatment than any previously reported thermostable mutants, while a subset also retained measurable NADH activity before heat treatment. These findings show that combining complementary scoring strategies helps navigate the trade-off between stability and activity; while, robust NADH function under thermal stress remains elusive, with only one variant retaining detectable NADH activity after heat treatment, the results provide valuable insight into the underlying constraints linking stability and cofactor usage and highlights possible directions for engineering FMOs with both enhanced thermostability and cofactor compatibility. Author summaryIn this work, we aimed to improve an enzyme that could be useful in industrial applications but is limited by two common constraints: poor stability at high temperatures and dependence on an expensive cofactor. To make the enzyme more suitable for large-scale applications, we sought to engineer variants that are both more thermostable and compatible with a cheaper cofactor, NADH. For enzyme engineering, we used a strategy that balances several properties rather than prioritizing a single trait. We combined tools that capture evolutionary patterns, protein physics, and AI-based predictions to explore which mutations might provide the right combination of stability and function. Through this approach, we obtained variants with improved heat resistance and higher cofactor activity retention.

Cyclohexanone monooxygenases (CHMO) consume molecular oxygen and NADPH to catalyze the valuable oxidation of cyclic ketones. However, CHMO usage is restricted by poor thermostability and stringent specificity for NADPH. Efforts to engineer CHMO have been limited by the sensitivity of the enzyme to perturbations in conformational dynamics and long-range interactions that cannot be predicted. We demonstrate a pair of aerobic, high-throughput growth selection platforms in Escherichia coli for oxygenase evolution, based on NADPH or NADH redox balance. We utilize the NADPH-dependent selection in the directed evolution of thermostable CHMO and discover the variant CHMO GV (A245G-A288V) with a 2.7-fold improvement in residual activity compared to the wild type after 40 {degrees}C incubation. Addition of a previously reported mutation resulted in A245G-A288V-T415C which has further improved thermostability at 45 {degrees}C. We apply the NADH-dependent selection to alter the cofactor specificity of CHMO to accept NADH, a less expensive cofactor than NADPH. We identified the variant CHMO DTNP (S208D-K326T-K349N-L143P) with a 21-fold cofactor specificity switch from NADPH to NADH compared to the wild type. Molecular modeling indicates that CHMO GV experiences more favorable residue packing and backbone torsions, and CHMO DTNP activity is driven by cooperative fine-tuning of cofactor contacts. Our introduced tools for oxygenase evolution enable the rapid engineering of properties critical to industrial scalability.

Experimental studies support that protein engineering based on ancestral sequence reconstruction often leads to variants with biotechnologically useful biomolecular properties. These may include high stability, enhanced conformational flexibility and a modified catalysis range. Carbohydrate-active enzymes have numerous applications related with the degradation and synthesis of carbohydrates and glycoconjugates. Here, we explore how ancestral reconstruction may impact substrate scope in glycosidases, highly diverse enzymes that catalyze the hydrolysis of glycosidic bonds in all living cells and find applications as catalysts of the reverse reaction. To this end, we screen a library of [~]500 potential glycosidase substrates for degradation by both, a modern family-1 glycosidase from Halothermothrix orenii and a putative ancestral family-1 glycosidase derived from sequence reconstruction at a bacterial-eukaryotic common ancestor. The modern enzyme is the better catalyst for most substrates. But the ancestral glycosidase is more efficient with flavonoid glycosides bearing large aglycon moieties. Analysis of the catalytic parameters for a selected set of substrates, alongside analysis of the library data using a supervised learning algorithm, support the hypothesis that the modern enzyme tends to become less catalytically efficient with increasing substrate size, while this trend is not observed for the ancestral glycosidase. Molecular simulations support that the ancestral catalysis pattern is linked to the existence of a highly flexible region of the ancestral structure and a cavity capable of accommodating large aglycons. Our results provide guidelines for the engineering of enzymes for the synthesis and hydrolysis of large glycoconjugates.

Enzyme engineering has produced numerous methods to optimize enzymes for biotechnological processes; however, less is known about how natural evolution creates new functionalities. We investigate the evolutionary emergence of enantioselectivity in plant borneol dehydrogenases (BDHs), which feature hydrophobic active-sites and are enantioselective towards dibornane-type monoterpenols. Ancestral sequence reconstruction provided a trajectory from the oldest unselective BDH ancestor N30 (E=12) toward the youngest selective ancestor N32, involving 19 mutations: 18 mutations are peripheral, one (I111L) occurs in the active-site. The mutation L111I in the hydrophobic pocket increased the selectivity of N30, while the back-mutation I111L decreased the selectivity of N32. Additional peripheral mutations (V136L/G169A/V183I) were required for high selectivity. Crystal structures suggested that protein dynamics, rather than structural changes shape these catalytic properties. Molecular simulations with funnel-metadynamics revealed a correlation between the active-sites solvent-accessible surface area (SASA) and selectivity. This potential evolutionary pathway shapes enantioselectivity, and guides future enzyme engineering campaigns.

Transketolase (TK) has been previously engineered, using semi-rational directed evolution and substrate walking, to accept increasingly aliphatic, cyclic and then aromatic substrates. This has ultimately led to the poor water solubility of new substrates, as a potential bottleneck to further exploitation of this enzyme in biocatalysis. Here we used a range of biophysical studies to characterise the response of both E. coli apo- and holo-TK activity and structure to a range of commonly used polar organic co-solvents: acetonitrile (MeCN), n- butanol (nBuOH), ethyl acetate (EToAc), isopropanol (iPrOH), and tetrahydrofuran (THF). The mechanism of enzyme deactivation was found to be predominantly via solvent-induced local unfolding. Holo-TK is thermodynamically more stable than apo-TK and yet for four of the five co-solvents it retained less activity than apo-TK after exposure to organic solvents, indicating that solvent tolerance was not correlated to global conformational stability. The co-solvent concentrations required for complete enzyme inactivation was inversely proportional to co-solvent log(P), while the unfolding rate was directly proportional, indicating that the solvents interact with and partially unfold the enzyme through hydrophobic contacts. Aggregation was not found to be the driving mechanism of enzyme inactivation, but was in some cases an additional impact of solvent-induced local or global unfolding. TK was found to be tolerant to 15% (v/v) iPrOH, 10% (v/v) MeCN, or 6% (v/v) nBuOH over 3 hours. This work indicates that future attempts to engineer the enzyme to better tolerate co-solvents should focus on increasing the stability of the protein to local unfolding, particularly in and around the cofactor-binding loops.

Glycoside hydrolases (GHs) play central roles in carbohydrate metabolism and are widely exploited for industrial and biomedical applications. However, they are often not optimal for applications due to their constrained function and strict stereochemical specificity, necessitating the discovery and optimization of distinct enzymes for each glycosidic configuration. Members of glycoside hydrolase family 1 (GH1) are archetypal retaining {beta}-glycosidases, while -specific activity is rare within this family. Here, I demonstrate that a retaining GH1 enzyme can be engineered to hydrolyze both {beta}- and -configured substrates without altering its canonical catalytic residues. Using a well-characterized {beta}-glycosidase and computational protein design strategies targeting second-shell residues surrounding the active site, a bifunctional {beta}-/-glycosidase containing 45 mutations was generated. The engineered variant acquired the ability to hydrolyze the -configured substrate 4-nitrophenyl--D-glucopyranoside while retaining activity toward the originals {beta}-substrates, with reduced catalytic efficiency and thermostability. Structural modeling and docking analyses reveal that the engineered enzyme preserves the original fold and accommodates substrates within the catalytic pocket in a similar manner to the wild type. These findings provide direct evidence that stereochemical constraint in retaining GH is more flexible than previously appreciated and can be modulated through targeted engineering.

The TIM barrel is the most prevalent fold in natural enzymes, supporting efficient catalysis of diverse chemical reactions. While de novo TIM barrels have been successfully designed, their minimalistic architecture lacks structural elements essential for substrate binding and catalysis. Here, we present CANVAS, a computational workflow that introduces a structural lid into a minimal de novo TIM barrel to anchor catalytic residues and form an active-site pocket for enzymatic function. Starting from two de novo TIM barrels, we designed nine variants with distinct lids to form active sites for the Kemp elimination. Four designs showed measurable activity, with the most active reaching a catalytic efficiency of 21,000 M-{superscript 1} s-{superscript 1} at its optimal pH. A co-crystal structure of this variant bound to a transition-state analogue confirmed the accuracy of the designed lid and active site. Using the X-ray structure of a lower-activity variant (19 M-{superscript 1} s-{superscript 1}), we applied ensemble-based design to optimize its active site, increasing catalytic efficiency by >1,600-fold to 32,000 M-{superscript 1} s-{superscript 1}. These results demonstrate that de novo TIM barrels can be endowed with substrate binding pockets supporting efficient catalytic function, establishing a platform for building enzymes on demand from minimal protein scaffolds.

O_SCPLOWLC_SCPLOW-Threonine aldolases (LTAs) are attractive biocatalysts for synthesizing {beta}-hydroxy--amino acids (HAAs) via C-C bond formation in pharmaceuticals, although their industrial applications suffer from low activity and diastereoselectivity. Herein, we describe the discovery of a new LTA from Neptunomonas marine (NmLTA) that displays both ideal enzymatic activity (64.8 U/mg) and diastereoselectivity (89.5% diastereomeric excess; de) for the desired product O_SCPLOWLC_SCPLOW-threo-4-methylsulfonylphenylserine (O_SCPLOWLC_SCPLOW-threo-MPTS). Using X-ray crystallography, site-directed mutagenesis, and computational modeling, we propose a "dual-conformation" mechanism for the diastereoselectivity control of NmLTA, whereby the incoming 4-methylsulfonylbenzaldehyde (4-MTB) could potentially bind at the NmLTA active site in two distinct orientations, potentially forming two diastereoisomers (threo- or erythro-form products). Importantly, two key NmLTA residues H140 and Y319 play critical roles in fine-tuning the binding mode of 4-MTB, supported by our site-mutagenesis assays. Uncovering of the catalytic mechanism in NmLTA guides us to further improve the diastereoselectivity of this enzyme. A triple variant of NmLTA (N18S/Q39R/Y319L; SRL) exhibited both improved diastereoselectivity (de value > 99%) and enzymatic activity (95.7 U/mg) for the synthesis of O_SCPLOWLC_SCPLOW-threo-MPTS compared with that of wild type. The preparative gram-scale synthesis for O_SCPLOWLC_SCPLOW-threo-MPTS with the SRL variant produced a space-time yield of up to 9.0 g L-1h-1, suggesting a potential role as a robust C-C bond synthetic tool for industrial synthesis of HAAs at a preparative scale. Finally, the SRL variant accepted a wider range of aromatic aldehyde derivatives as substrates and exhibited improved diastereoselectivity toward para-site substituents. This work provides deep structural insights into the molecular mechanism underlying the catalysis in NmLTA and pinpoints the key structural motifs responsible for regulating the diastereoselectivity control, thereby guiding future attempts for protein engineering of various LTAs from different sources.