Engineering the third wave of biocatalysis

Metabolic engineering of Saccharomyces cerevisiae for high-level production of (+)-ambrein from glucose

(+)-Ambrein is the primary component of ambergris, a rare product found in sperm whales (Physeter microcephalus). Microbial production using sustainable resources is a promising way to replace animal extraction and chemical synthesis. We constructed an engineered yeast strain to produce (+)-ambrein de novo. Squalene is a substrate for the biosynthesis of (+)-ambrein. Firstly, strain LQ2, with a squalene yield of 384.4 mg/L was obtained by optimizing the mevalonate pathway. Then we engineered a method for the de novo production of (+)-ambrein using glucose as a carbon source by overexpressing codon-optimized tetraprenyl-β-curcumene cyclase (BmeTC) and its double mutant enzyme (BmeTCY167A/D373C), evaluating different promoters, knocking out GAL80, and fusing the protein with BmeTC and squalene synthase (AtSQS2). Nevertheless, the synthesis of (+)-ambrein is still limited, causing low catalytic activity in BmeTC. We carried out a protein surface amino acid modification of BmeTC. The dominant mutant BmeTCK6A/Q9E/N454A for the first step was obtained to improve its catalytic activity. The yield of (+)-ambrein increased from 35.2 to 59.0 mg/L in the shake flask and finally reached 457.4 mg/L in the 2 L fermenter, the highest titer currently available for yeast. Efficiently engineered strains and inexpensive fermentation conditions for the industrial production of (+)-ambrein. The metabolic engineering tools provide directions for optimizing the biosynthesis of other high-value triterpenes.

Chemoenzymatic Synthesis of 2-Aryl Thiazolines from 4-Hydroxybenzaldehydes Using Vanillyl Alcohol Oxidases

Nitrogen heterocycles are commonly found in bioactive natural products and drugs. However, the biocatalytic tools for nitrogen heterocycle synthesis are limited. Herein, we report the discovery of vanillyl alcohol oxidases (VAOs) as efficient biocatalysts for the one-pot synthesis of 2-aryl thiazolines from various 4-hydroxybenzaldehydes and aminothiols. The wild-type biocatalyst features a broad scope of 4-hydroxybenzaldehydes. Though the scope of aminothiols is limited, it could be improved via semi-rational protein engineering, generating a variant to produce previously inaccessible cysteine-derived bioactive 2-aryl thiazolines using the wild-type VAO. Benefiting from the derivatizable functional groups in the enzymatic products, we further chemically modified these products to expand the chemical space, offering a new chemoenzymatic strategy for the green and efficient synthesis of structurally diverse 2-aryl-thiazoline derivatives to prompt their use in drug discovery and catalysis.

Biocatalytic approach for the synthesis of chiral alcohols for the development of pharmaceutical intermediates and other industrial applications: A review

Biocatalysis has emerged as a strong tool for the synthesis of active pharmaceutical ingredients (APIs). In the early twentieth century, whole cell biocatalysis was used to develop the first industrial biocatalytic processes, and the precise work of enzymes was unknown. Biocatalysis has evolved over the years into an essential tool for modern, cost-effective, and sustainable pharmaceutical manufacturing. Meanwhile, advances in directed evolution enable the rapid production of process-stable enzymes with broad substrate scope and high selectivity. Large-scale synthetic pathways incorporating biocatalytic critical steps towards >130 APIs of authorized pharmaceuticals and drug prospects are compared in terms of steps, reaction conditions, and scale with the corresponding chemical procedures. This review is designed on the functional group developed during the reaction forming alcohol functional groups. Some important biocatalyst sources, techniques, and challenges are described. A few APIs and their utilization in pharmaceutical drugs are explained here in this review. Biocatalysis has provided shorter, more efficient, and more sustainable alternative pathways toward existing small molecule APIs. Furthermore, non-pharmaceutical applications of biocatalysts are also mentioned and discussed. Finally, this review includes the future outlook and challenges of biocatalysis. In conclusion, Further research and development of promising enzymes are required before they can be used in industry.

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.

Enhancing efficiency of protein language models with minimal wet-lab data through few-shot learning

Accurately modeling the protein fitness landscapes holds great importance for protein engineering. Pre-trained protein language models have achieved state-of-the-art performance in predicting protein fitness without wet-lab experimental data, but their accuracy and interpretability remain limited. On the other hand, traditional supervised deep learning models require abundant labeled training examples for performance improvements, posing a practical barrier. In this work, we introduce FSFP, a training strategy that can effectively optimize protein language models under extreme data scarcity for fitness prediction. By combining meta-transfer learning, learning to rank, and parameter-efficient fine-tuning, FSFP can significantly boost the performance of various protein language models using merely tens of labeled single-site mutants from the target protein. In silico benchmarks across 87 deep mutational scanning datasets demonstrate FSFP's superiority over both unsupervised and supervised baselines. Furthermore, we successfully apply FSFP to engineer the Phi29 DNA polymerase through wet-lab experiments, achieving a 25% increase in the positive rate. These results underscore the potential of our approach in aiding AI-guided protein engineering.

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.

A New Age of Biocatalysis Enabled by Generic Activation Modes

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

Understanding the Enzyme (S)-Norcoclaurine Synthase Promiscuity to Aldehydes and Ketones

The (S)-norcoclaurine synthase from Thalictrum flavum (TfNCS) stereoselectively catalyzes the Pictet-Spengler reaction between dopamine and 4-hydroxyphenylacetaldehyde to give (S)-norcoclaurine. TfNCS can catalyze the Pictet-Spengler reaction with various aldehydes and ketones, leading to diverse tetrahydroisoquinolines. This substrate promiscuity positions TfNCS as a highly promising enzyme for synthesizing fine chemicals. Understanding carbonyl-containing substrates' structural and electronic signatures that influence TfNCS activity can help expand its applications in the synthesis of different compounds and aid in protein optimization strategies. In this study, we investigated the influence of the molecular properties of aldehydes and ketones on their reactivity in the TfNCS-catalyzed Pictet-Spengler reaction. Initially, we compiled a library of reactive and unreactive compounds from previous publications. We also performed enzymatic assays using nuclear magnetic resonance to identify some reactive and unreactive carbonyl compounds, which were then included in the library. Subsequently, we employed QSAR and DFT calculations to establish correlations between substrate-candidate structures and reactivity. Our findings highlight correlations of structural and stereoelectronic features, including the electrophilicity of the carbonyl group, to the reactivity of aldehydes and ketones toward the TfNCS-catalyzed Pictet-Spengler reaction. Interestingly, experimental data of seven compounds out of fifty-three did not correlate with the electrophilicity of the carbonyl group. For these seven compounds, we identified unfavorable interactions between them and the TfNCS. Our results demonstrate the applications of in silico techniques in understanding enzyme promiscuity and specificity, with a particular emphasis on machine learning methodologies, DFT electronic structure calculations, and molecular dynamic (MD) simulations.

Enhancing Machine-Learning Prediction of Enzyme Catalytic Temperature Optima through Amino Acid Conservation Analysis

Enzymes play a crucial role in various industrial production and pharmaceutical developments, serving as catalysts for numerous biochemical reactions. Determining the optimal catalytic temperature (Topt) of enzymes is crucial for optimizing reaction conditions, enhancing catalytic efficiency, and accelerating the industrial processes. However, due to the limited availability of experimentally determined Topt data and the insufficient accuracy of existing computational methods in predicting Topt, there is an urgent need for a computational approach to predict the Topt values of enzymes accurately. In this study, using phosphatase (EC 3.1.3.X) as an example, we constructed a machine learning model utilizing amino acid frequency and protein molecular weight information as features and employing the K-nearest neighbors regression algorithm to predict the Topt of enzymes. Usually, when conducting engineering for enzyme thermostability, researchers tend not to modify conserved amino acids. Therefore, we utilized this machine learning model to predict the Topt of phosphatase sequences after removing conserved amino acids. We found that the predictive model's mean coefficient of determination (R2) value increased from 0.599 to 0.755 compared to the model based on the complete sequences. Subsequently, experimental validation on 10 phosphatase enzymes with undetermined optimal catalytic temperatures shows that the predicted values of most phosphatase enzymes based on the sequence without conservative amino acids are closer to the experimental optimal catalytic temperature values. This study lays the foundation for the rapid selection of enzymes suitable for industrial conditions.

Classification and functional origins of stereocomplementary alcohol dehydrogenases for asymmetric synthesis of chiral secondary alcohols: A review

Alcohol dehydrogenases (ADHs) mediated biocatalytic asymmetric reduction of ketones have been widely applied in the synthesis of optically active secondary alcohols with highly reactive hydroxyl groups ligated to the stereogenic carbon and divided into (R)- and (S)-configurations. Stereocomplementary ADHs could be applied in the synthesis of both enantiomers and are increasingly accepted as the "first of choice" in green chemistry due to the high atomic economy, low environmental factor, 100 % theoretical yield, and high environmentally friendliness. Due to the equal importance of complementary alcohols, development of stereocomplementary ADHs draws increasing attention. This review is committed to summarize recent advance in discovery of naturally evolved and tailor-made stereocomplementary ADHs, unveil the molecular mechanism of stereoselective catalysis in views of classification and functional basis, and provide guidance for further engineering the stereoselectivity of ADHs for the industrial biosynthesis of chiral secondary alcohol of industrial relevance.

Identification of 3-ketocapnine reductase activity within the human microbiota

The microbial synthesis of sulfonolipids within the human body is likely involved in maintaining human health or causing diseases. However, the enzymes responsible for their biosynthesis remain largely unknown. In this study, we identified and verified the role of 3-ketocapnine reductase, the third-step enzyme, in the four-step conversion of l-phosphoserine into sulfobacin B both in vivo and in vitro. This finding builds upon our previous research into sulfonolipid biosynthesis, which focused on the vaginal bacterium Chryseobacterium gleum DSM 16776 and the gut bacterium Alistipes finegoldii DSM 17242. Through comprehensive gene mapping, we demonstrate the widespread presence of potential sulfonolipid biosynthetic genes across diverse bacterial species inhabiting various regions of the human body. These findings shed light on the prevalence of sulfonolipid-like metabolites within the human microbiota, suggesting a potential role for these lipid molecules in influencing the intricate biointeractions within the complex microbial ecosystem of the human body.

Eliminating Imaginary Vibrational Frequencies in Quantum-Chemical Cluster Models of Enzymatic Active Sites

In constructing finite models of enzyme active sites for quantum-chemical calculations, atoms at the periphery of the model must be constrained to prevent unphysical rearrangements during geometry relaxation. A simple fixed-atom or "coordinate-lock" approach is commonly employed but leads to undesirable artifacts in the form of small imaginary frequencies. These preclude evaluation of finite-temperature free-energy corrections, limiting thermochemical calculations to enthalpies only. Full-dimensional vibrational frequency calculations are possible by replacing the fixed-atom constraints with harmonic confining potentials. Here, we compare that approach to an alternative strategy in which fixed-atom contributions to the Hessian are simply omitted. While the latter strategy does eliminate imaginary frequencies, it tends to underestimate both the zero-point energy and the vibrational entropy while introducing artificial rigidity. Harmonic confining potentials eliminate imaginary frequencies and provide a flexible means to construct active-site models that can be used in unconstrained geometry relaxations, affording better convergence of reaction energies and barrier heights with respect to the model size, as compared to models with fixed-atom constraints.

Recent advances in catalytic asymmetric synthesis

Asymmetric catalysis stands at the forefront of modern chemistry, serving as a cornerstone for the efficient creation of enantiopure chiral molecules characterized by their high selectivity. In this review, we delve into the realm of asymmetric catalytic reactions, which spans various methodologies, each contributing to the broader landscape of the enantioselective synthesis of chiral molecules. Transition metals play a central role as catalysts for a wide range of transformations with chiral ligands such as phosphines, N-heterocyclic carbenes (NHCs), etc., facilitating the formation of chiral C-C and C-X bonds, enabling precise control over stereochemistry. Enantioselective photocatalytic reactions leverage the power of light as a driving force for the synthesis of chiral molecules. Asymmetric electrocatalysis has emerged as a sustainable approach, being both atom-efficient and environmentally friendly, while offering a versatile toolkit for enantioselective reductions and oxidations. Biocatalysis relies on nature's most efficient catalysts, i.e., enzymes, to provide exquisite selectivity, as well as a high tolerance for diverse functional groups under mild conditions. Thus, enzymatic optical resolution, kinetic resolution and dynamic kinetic resolution have revolutionized the production of enantiopure compounds. Enantioselective organocatalysis uses metal-free organocatalysts, consisting of modular chiral phosphorus, sulfur and nitrogen components, facilitating remarkably efficient and diverse enantioselective transformations. Additionally, unlocking traditionally unreactive C-H bonds through selective functionalization has expanded the arsenal of catalytic asymmetric synthesis, enabling the efficient and atom-economical construction of enantiopure chiral molecules. Incorporating flow chemistry into asymmetric catalysis has been transformative, as continuous flow systems provide precise control over reaction conditions, enhancing the efficiency and facilitating optimization. Researchers are increasingly adopting hybrid approaches that combine multiple strategies synergistically to tackle complex synthetic challenges. This convergence holds great promise, propelling the field of asymmetric catalysis forward and facilitating the efficient construction of complex molecules in enantiopure form. As these methodologies evolve and complement one another, they push the boundaries of what can be accomplished in catalytic asymmetric synthesis, leading to the discovery of novel, highly selective transformations which may lead to groundbreaking applications across various industries.

Polymer-enhanced peroxidase activity of ceria nanozyme for highly sensitive detection of alkaline phosphatase

Nanoceria have demonstrated a wide array of catalytic activity similar to natural enzymes, holding considerable significance in the colorimetric detection of alkaline phosphatase (ALP), which is a biomarker of various biological disorders. However, the issues of physiological stability and formation of protein corona, which are strongly related to their surface chemistry, limit their practical application. In this work, CeO2 nanoparticles characterized by enhanced dimensional uniformity and specific surface area were synthesized, followed by encapsulation with various polymers to further increase catalytic activity and physiological stability. Notably, the CeO2 nanoparticles encapsulated within each polymer exhibited improved catalytic characteristics, with PAA-capped CeO2 exhibiting the highest performance. We further demonstrated that the PAA-CeO2 obtained with enhanced catalytic activity was attributed to an increase in surface negative charge. PAA-CeO2 enabled the quantitative assessment of AA activity within a wide concentration range of 10 to 60 μM, with a detection limit of 0.111 μM. Similarly, it allowed for the evaluation of alkaline phosphatase activity throughout a broad range of 10 to 80 U/L, with a detection limit of 0.12 U/L. These detection limits provided adequate sensitivity for the practical detection of ALP in human serum.

Bio-oxidation of progesterone by Penicillium oxalicum CBMAI 1185 and evaluation of the cytotoxic activity

We report the biotransformation of progesterone 1 by whole cells of Brazilian marine-derived fungi. A preliminary screening with 12 fungi revealed that the strains Penicillium oxalicum CBMAI 1996, Mucor racemous CBMAI 847, Cladosporium sp. CBMAI 1237, Penicillium oxalicum CBMAI 1185 and Aspergillus sydowii CBMAI 935 were efficient in the biotransformation of progesterone 1 in the first days of the reaction, with conversion values ranging from 75 % to 99 %. The fungus P. oxalicum CBMAI 1185 was employed in the reactions in quintuplicate to purify and characterize the main biotransformation products of progesterone 1. The compounds testololactone 1a, 12β-hydroxyandrostenedione 1b and 1β-hydroxyandrostenedione 1c were isolated and characterized by NMR, MS, [α]D and MP. In addition, the chromatographic yield of compound 1a was determined by HPLC-PDA in the screening experiments. In this study, we show a biotransformation pathway of progesterone 1, suggesting the presence of several enzymes such as Baeyer-Villiger monooxygenases, dehydrogenases and cytochrome P450 monooxygenases in the fungus P. oxalicum CBMAI 1185. In summary, the results obtained in this study contribute to the synthetic area and have environmental importance, since the marine-derived fungi can be employed in the biodegradation of steroids present in wastewater and the environment. The cytotoxic results demonstrate that the biodegradation products were inactive against the cell lines, in contrast to progesterone.

Biocatalytic strategy for the construction of sp3-rich polycyclic compounds from directed evolution and computational modelling

Catalysis with engineered enzymes has provided more efficient routes for the production of active pharmaceutical agents. However, the potential of biocatalysis to assist in early-stage drug discovery campaigns remains largely untapped. In this study, we have developed a biocatalytic strategy for the construction of sp3-rich polycyclic compounds via the intramolecular cyclopropanation of benzothiophenes and related heterocycles. Two carbene transferases with complementary regioisomer selectivity were evolved to catalyse the stereoselective cyclization of benzothiophene substrates bearing diazo ester groups at the C2 or C3 position of the heterocycle. The detailed mechanisms of these reactions were elucidated by a combination of crystallographic and computational analyses. Leveraging these insights, the substrate scope of one of the biocatalysts could be expanded to include previously unreactive substrates, highlighting the value of integrating evolutionary and rational strategies to develop enzymes for new-to-nature transformations. The molecular scaffolds accessed here feature a combination of three-dimensional and stereochemical complexity with 'rule-of-three' properties, which should make them highly valuable for fragment-based drug discovery campaigns.

The potential use of nanozymes as an antibacterial agents in oral infection, periodontitis, and peri-implantitis

Several studies suggest that oral pathogenic biofilms cause persistent oral infections. Among these is periodontitis, a prevalent condition brought on by plaque biofilm. It can even result in tooth loss. Furthermore, the accumulation of germs around a dental implant may lead to peri-implantitis, which damages the surrounding bone and gum tissue. Furthermore, bacterial biofilm contamination on the implant causes soft tissue irritation and adjacent bone resorption, severely compromising dental health. On decontaminated implant surfaces, however, re-osseointegration cannot be induced by standard biofilm removal techniques such as mechanical cleaning and antiseptic treatment. A family of nanoparticles known as nanozymes (NZs) comprise highly catalytically active multivalent metal components. The most often employed NZs with antibacterial activity are those that have peroxidase (POD) activity, among other types of NZs. Since NZs are less expensive, more easily produced, and more stable than natural enzymes, they hold great promise for use in various applications, including treating microbial infections. NZs have significantly contributed to studying implant success rates and periodontal health maintenance in periodontics and implantology. An extensive analysis of the research on various NZs and their applications in managing oral health conditions, including dental caries, dental pulp disorders, oral ulcers, peri-implantitis, and bacterial infections of the mouth. To combat bacteria, this review concentrates on NZs that imitate the activity of enzymes in implantology and periodontology. With a view to the future, there are several ways that NZs might be used to treat dental disorders antibacterially.

Computational scoring and experimental evaluation of enzymes generated by neural networks

In recent years, generative protein sequence models have been developed to sample novel sequences. However, predicting whether generated proteins will fold and function remains challenging. We evaluate a set of 20 diverse computational metrics to assess the quality of enzyme sequences produced by three contrasting generative models: ancestral sequence reconstruction, a generative adversarial network and a protein language model. Focusing on two enzyme families, we expressed and purified over 500 natural and generated sequences with 70-90% identity to the most similar natural sequences to benchmark computational metrics for predicting in vitro enzyme activity. Over three rounds of experiments, we developed a computational filter that improved the rate of experimental success by 50-150%. The proposed metrics and models will drive protein engineering research by serving as a benchmark for generative protein sequence models and helping to select active variants for experimental testing.

A Robust Growth-Based Selection Platform to Evolve an Enzyme via Dependency on Noncanonical Tyrosine Analogues

Growth-based selections evaluate the fitness of individual organisms at a population level. In enzyme engineering, such growth selections allow for the rapid and straightforward identification of highly efficient biocatalysts from extensive libraries. However, selection-based improvement of (synthetically useful) biocatalysts is challenging, as they require highly dependable strategies that artificially link their activities to host survival. Here, we showcase a robust and scalable growth-based selection platform centered around the complementation of noncanonical amino acid-dependent bacteria. Specifically, we demonstrate how serial passaging of populations featuring millions of carbamoylase variants autonomously selects biocatalysts with up to 90,000-fold higher initial rates. Notably, selection of replicate populations enriched diverse biocatalysts, which feature distinct amino acid motifs that drastically boost carbamoylase activity. As beneficial substitutions also originated from unintended copying errors during library preparation or cell division, we anticipate that our growth-based selection platform will be applicable to the continuous, autonomous evolution of diverse biocatalysts in the future.

Integrating Enzymes with Supramolecular Polymers for Recyclable Photobiocatalytic Catalysis

Chemical modifications of enzymes excel in the realm of enzyme engineering due to its directness, robustness, and efficiency; however, challenges persist in devising versatile and effective strategies. In this study, we introduce a supramolecular modification methodology that amalgamates a supramolecular polymer with Candida antarctica lipase B (CalB) to create supramolecular enzymes (SupEnzyme). This approach features the straightforward preparation of a supramolecular amphiphilic polymer (β-CD@SMA), which was subsequently conjugated to the enzyme, resulting in a SupEnzyme capable of self-assembly into supramolecular nanoparticles. The resulting SupEnzyme nanoparticles can form micron-scale supramolecular aggregates through supramolecular and electrostatic interactions with guest entities, thus enhancing catalyst recycling. Remarkably, these aggregates maintain 80 % activity after seven cycles, outperforming Novozym 435. Additionally, they can effectively initiate photobiocatalytic cascade reactions using guest photocatalysts. As a consequence, our SupEnzyme methodology exhibits noteworthy adaptability in enzyme modification, presenting a versatile platform for various polymer, enzyme, and biocompatible catalyst pairings, with potential applications in the fields of chemistry and biology.

Covalent Organic Frameworks Based Photoenzymatic Nano-reactor for Asymmetric Dynamic Kinetic Resolution of Secondary Amines

Bio-catalysis represents a highly efficient and stereoselective method for the synthesis of valuable chiral compounds, however, the poor stability and limited reaction types of free enzymes restrict their wide application in industrial production. In this work, to overcome these problems, a multifunctional photoenzymatic nanoreactor CALB@COF-Ir was developed through the encapsulation of Candida antarctica lipase B (CALB) in a photosensitive covalent organic framework COF-Ir. This bio-nanocluster serves as efficient catalysts in asymmetric dynamic kinetic resolution (DKR) of secondary amines to give a series of chiral amines in high yields (up to 99 %) and enantioselectivities (up to 99 % ee). The well-designed COF-Ir not only acts as safety cover to prevent CALB from deactivation but promotes racemization of secondary amines via photo-induced hydrogen atom transfer (HAT) process. Photoelectric characterization and TDDFT calculation revealed that (ppy)2Ir units in COF-Ir play crucial role in this photocatalytic system which enhance its photo-redox properties through facilitating the separation between photoelectrons (e-) and holes (h+). Furthermore, the heterogeneous photoenzymatic nanoreactor could be recycled for five rounds with slight decline of catalytic reactivity.

Characterization of a novel 3-quinuclidinone reductase possessing remarkable thermostability

The 3-quinuclidinone reductase plays an irreplaceable role in the biopreparation of (R)-3-quinuclidinol, an intermediate vital for synthesis of various pharmaceuticals. Thermal robustness is a critical factor for enzymatic synthesis in industrial applications. This study characterized a new 3-quinuclidinone reductase, named SaQR, with significant thermal stability. The SaQR was overexpressed in a GST-fused state, and substrate and cofactor screening were conducted. Additionally, three-dimensional structure prediction using AlphaFold and analysis were performed, along with relevant thermostability tests, and the evaluation of factors influencing enzyme activity. The findings highlight the remarkable thermostability of SaQR, retaining over 90% of its activity after 72 h at 50°C, with an optimal operational temperature of 85°C. SaQR showed typical structural traits of the SDR superfamily, with its cofactor-determining residue being aspartic acid, conferring nicotinamide adenine dinucleotide (NAD(H)) preference. Moreover, K+ and Na+, at a concentration of 400 mM, could significantly enhance the activity, while Mg2+ and Mn2+ only display inhibitory effects within the tested concentration range. The findings of molecular dynamics simulations suggest that high temperatures may disrupt the binding of enzyme to substrate by increasing the flexibility of residues 205-215. In conclusion, this study reports a novel 3-quinuclidinone reductase with remarkable thermostability.

Multi-strategy orthogonal enhancement and analysis of aldo-keto reductase thermal stability

Given their outstanding efficiency and selectivity, enzymes are integral in various domains such as drug synthesis, the food industry, and environmental management. However, the inherent instability of natural enzymes limits their widespread industrial application. In this study, we underscore the efficacy of enhancing protein thermal stability through comprehensive protein design strategies, encompassing elements such as the free energy of protein folding, internal forces within proteins, and the overall structural design. We also demonstrate the efficiency and precision of combinatorial screening in the thermal stability design of aldo-keto reductase (AKR7-2-1). In our research, three single-point mutations and five combinatorial mutations were strategically introduced into AKR7-2-1, using multiple computational techniques. Notably, the E12I/S235I mutant showed a significant increase of 25.4 °C in its melting temperature (Tm). Furthermore, the optimal mutant, E12V/S235I, maintained 80 % of its activity while realizing a 16.8 °C elevation in Tm. Remarkably, its half-life at 50 °C was increased to twenty times that of the wild type. Structural analysis indicates that this enhanced thermal stability primarily arises from reduced oscillation in the loop region and increased internal hydrogen bonding. The promising results achieved with AKR7-2-1 demonstrate that our strategy could serve as a valuable reference for enhancing the thermal stability of other industrial enzymes.

Utilizing microbial metabolite in catalytic cascade synthesis of conjugated oligomers for In-Situ regulation of biological activity

Despite the advances of multistep enzymatic cascade reactions, their incorporation with abiotic reactions in living organisms remains challenging in synthetic biology. Herein, we combined microbial metabolic pathways and Pd-catalyzed processes for in-situ generation of bioactive conjugated oligomers. Our biocompatible one-pot coupling reaction utilized the fermentation process of engineered E. coli that converted glucose to styrene, which participated in the Pd-catalyzed Heck reaction for in-situ synthesis of conjugated oligomers. This process serves a great interest in understanding resistance evolution by utilizing the inhibitory activity of the synthesized conjugated oligomers. The approach allows for the in-situ combination of biological metabolism and CC coupling reactions, opening up new possibilities for the biosynthesis of unnatural molecules and enabling the in-situ regulation of the bioactivity of the obtained products.

Nature-inspired polymer photocatalysts for green NADH regeneration and nitroarene transformation

Photocatalysis has emerged as a promising approach for generating solar chemical and organic transformations under the solar light spectrum, employing polymer photocatalysts. In this study, our aim is to achieve the regeneration of NADH and fixation of nitroarene compounds, which hold significant importance in various fields such as pharmaceuticals, biology, and chemistry. The development of an in-situ nature-inspired artificial photosynthetic pathway represents a challenging task, as it involves harnessing solar energy for efficient solar chemical production and organic transformation. In this work, we have successfully synthesized a novel artificial photosynthetic polymer, named TFc photocatalyst, through the Friedel-Crafts alkylation reaction between triptycene (T) and a ferrocene motif (Fc). The TFC photocatalyst is a promising material with excellent optical properties, an appropriate band gap, and the ability to facilitate the regeneration of NADH and the fixation of nitroarene compounds through photocatalysis. These characteristics are necessary for several applications, including organic synthesis and environmental remediation. Our research provides a significant step forward in establishing a reliable pathway for the regeneration and fixation of solar chemicals and organic compounds under the solar light spectrum.

Latest Trends in Lipase-Catalyzed Synthesis of Ester Carbohydrate Surfactants: From Key Parameters to Opportunities and Future Development

Carbohydrate-based surfactants are amphiphilic compounds containing hydrophilic moieties linked to hydrophobic aglycones. More specifically, carbohydrate esters are biosourced and biocompatible surfactants derived from inexpensive renewable raw materials (sugars and fatty acids). Their unique properties allow them to be used in various areas, such as the cosmetic, food, and medicine industries. These multi-applications have created a worldwide market for biobased surfactants and consequently expectations for their production. Biobased surfactants can be obtained from various processes, such as chemical synthesis or microorganism culture and surfactant purification. In accordance with the need for more sustainable and greener processes, the synthesis of these molecules by enzymatic pathways is an opportunity. This work presents a state-of-the-art lipase action mode, with a focus on the active sites of these proteins, and then on four essential parameters for optimizing the reaction: type of lipase, reaction medium, temperature, and ratio of substrates. Finally, this review discusses the latest trends and recent developments, showing the unlimited potential for optimization of such enzymatic syntheses.

Asymmetric Synthesis of α-Chloroamides via Photoenzymatic Hydroalkylation of Olefins

Photoenzymatic intermolecular hydroalkylations of olefins are highly enantioselective for chiral centers formed during radical termination but poorly selective for centers set in the C-C bond-forming event. Here, we report the evolution of a flavin-dependent "ene"-reductase to catalyze the coupling of α,α-dichloroamides with alkenes to afford α-chloroamides in good yield with excellent chemo- and stereoselectivity. These products can serve as linchpins in the synthesis of pharmaceutically valuable motifs. Mechanistic studies indicate that radical formation occurs by exciting a charge-transfer complex templated by the protein. Precise control over the orientation of molecules within the charge-transfer complex potentially accounts for the observed stereoselectivity. The work expands the types of motifs that can be prepared using photoenzymatic catalysis.

Biocatalysis: landmark discoveries and applications in chemical synthesis

Biocatalysis has become an important tool in chemical synthesis, allowing access to complex molecules with high levels of activity and selectivity and with low environmental impact. Key discoveries in protein engineering, bioinformatics, recombinant technology and DNA sequencing have contributed towards the rapid acceleration of the field. This tutorial review explores enzyme engineering strategies and high-throughput screening approaches that have been applied for the discovery and development of enzymes for synthetic application. Landmark developments in the field are discussed and have been carefully selected to highlight the diverse synthetic applications of enzymes within the pharmaceutical, agricultural, food and chemical industries. The design and development of artificial biocatalytic cascades is also examined. This tutorial review will give readers an insight into the landmark discoveries and milestones that have helped shape and grow this branch of catalysis since the discovery of the first enzyme.

Strategies for designing biocatalysts with new functions

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

Empowering Site-Specific Bioconjugations In Vitro and In Vivo: Advances in Sortase Engineering and Sortase-Mediated Ligation

Sortase-mediated ligation (SML) has emerged as a powerful and versatile methodology for site-specific protein conjugation, functionalization/labeling, immobilization, and design of biohybrid molecules and systems. However, the broader application of SML faces several challenges, such as limited activity and stability, dependence on calcium ions, and reversible reactions caused by nucleophilic side-products. Over the past decade, protein engineering campaigns and particularly directed evolution, have been extensively employed to overcome sortase limitations, thereby expanding the potential application of SML in multiple directions, including therapeutics, biorthogonal chemistry, biomaterials, and biosensors. This review provides an overview of achieved advancements in sortase engineering and highlights recent progress in utilizing SML in combination with other state-of-the-art chemical and biological methodologies. The aim is to encourage scientists to employ sortases in their conjugation experiments.

Biophysics-based protein language models for protein engineering

Protein language models trained on evolutionary data have emerged as powerful tools for predictive problems involving protein sequence, structure, and function. However, these models overlook decades of research into biophysical factors governing protein function. We propose Mutational Effect Transfer Learning (METL), a protein language model framework that unites advanced machine learning and biophysical modeling. Using the METL framework, we pretrain transformer-based neural networks on biophysical simulation data to capture fundamental relationships between protein sequence, structure, and energetics. We finetune METL on experimental sequence-function data to harness these biophysical signals and apply them when predicting protein properties like thermostability, catalytic activity, and fluorescence. METL excels in challenging protein engineering tasks like generalizing from small training sets and position extrapolation, although existing methods that train on evolutionary signals remain powerful for many types of experimental assays. We demonstrate METL's ability to design functional green fluorescent protein variants when trained on only 64 examples, showcasing the potential of biophysics-based protein language models for protein engineering.

Biocatalytic Regioselective O-acylation of Sesquiterpene Lactones from Chicory: A Pathway to Novel Ester Derivatives

We report the first biocatalytic modification of sesquiterpene lactones (STLs) found in the chicory plants, specifically lactucin (Lc), 11β,13-dihydrolactucin (DHLc), lactucopicrin (Lp), and 11β,13-dihydrolactucopicrin (DHLp). The selective O-acylation of their primary alcohol group was carried out by the lipase B from Candida antarctica (CAL-B) using various aliphatic vinyl esters as acyl donors. Perillyl alcohol, a simpler monoterpenoid, served as a model to set up the desired O-acetylation reaction by comparing the use of acetic acid and vinyl acetate as acyl donors. Similar conditions were then applied to DHLc, where five novel ester chains were selectively introduced onto the primary alcohol group, with conversions going from >99 % (acetate and propionate) to 69 % (octanoate). The synthesis of the corresponding O-acetyl esters of Lc, Lp, and DHLp was also successfully achieved with near-quantitative conversion. Molecular docking simulations were then performed to elucidate the preferred enzyme-substrate binding modes in the acylation reactions with STLs, as well as to understand their interactions with crucial amino acid residues at the active site. Our methodology enables the selective O-acylation of the primary alcohol group in four different STLs, offering possibilities for synthesizing novel derivatives with significant potential applications in pharmaceuticals or as biocontrol agents.

Enzymatic Synthesis of Indole-Based Imidazopyridine using α-Amylase

The imidazo[1,2-a]pyridine scaffold has gained significant attention due to its presence as a lead structure in several commercially available pharmaceuticals like zolimidine, zolpidem, olprinone, soraprazan, etc. Further, indole-based imidazo[1,2-a]pyridine derivatives have been found interesting due to their anticancer and antibacterial activities. However, limited methods have been reported for the synthesis of indole-based imidazo[1,2-a]pyridines. In this study, we have successfully developed a biocatalytic process for synthesizing indole-based imidazo[1,2-a]pyridine derivatives using the α-amylase enzyme catalyzed Groebke-Blackburn-Bienayme (GBB) multicomponent reaction of 2-aminopyridine, indole-3-carboxaldehyde, and isocyanide. The generality and robustness of this protocol were shown by synthesizing differently substituted indole-based imidazo[1,2-a]pyridines in good isolated yields. Furthermore, to make α-amylase a reusable catalyst for GBB multicomponent reaction, it was immobilized onto magnetic metal-organic framework (MOF) materials [Fe3 O4 @MIL-100(Fe)] and found reusable up to four consecutive catalytic cycles without the significant loss in catalytic activity.

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.

Modifying the Oxidative Potentials of Imines in a Dye Loaded Capsule for Photocatalytic Cyclization with Hydrogen Evolution

Modifying redox potential of substrates and intermediates to balance pairs of redox steps are important stages for multistep photosynthesis but faced marked challenges. Through co-clathration of iridium photosensitizer and imine substrate within one packet of a metal-organic capsule to shift the redox potentials of substrate, herein, we reported a multiphoton enzymatic strategy for the generation of heterocycles by intramolecular C-X hydrogen evolution cross-couplings. The cage facilitated a pre-equilibrium substrate-involving clathrate that cathodic shifts the oxidation potential of the substrate-dye-host ternary complex and configuration inversion of substrate via spatial constraints in the confined space. The new two photon excitation strategy enabled the precise control of the multistep electron transfer between each pair (photosensitizer, substrate and the capsule), endowing the catalytic system proceeding smoothly with an enzymatic fashion. Three kinds of 2-subsituted (-OH, -NH2 , and -SH) imines and N-aryl enamines all give the corresponding cyclization products efficiently under visible light irradiation, demonstrating the promising of the microenvironment driven thermodynamic activation in the host-dye-substrate ternary for synergistic combination of multistep photocatalytic transformations.

Towards a hybrid model-driven platform based on flux balance analysis and a machine learning pipeline for biosystem design

Metabolic modeling and machine learning (ML) are crucial components of the evolving next-generation tools in systems and synthetic biology, aiming to unravel the intricate relationship between genotype, phenotype, and the environment. Nonetheless, the comprehensive exploration of integrating these two frameworks, and fully harnessing the potential of fluxomic data, remains an unexplored territory. In this study, we present, rigorously evaluate, and compare ML-based techniques for data integration. The hybrid model revealed that the overexpression of six target genes and the knockout of seven target genes contribute to enhanced ethanol production. Specifically, we investigated the influence of succinate dehydrogenase (SDH) on ethanol biosynthesis in Saccharomyces cerevisiae through shake flask experiments. The findings indicate a noticeable increase in ethanol yield, ranging from 6 % to 10 %, in SDH subunit gene knockout strains compared to the wild-type strain. Moreover, in pursuit of a high-yielding strain for ethanol production, dual-gene deletion experiments were conducted targeting glycerol-3-phosphate dehydrogenase (GPD) and SDH. The results unequivocally demonstrate significant enhancements in ethanol production for the engineered strains Δsdh4Δgpd1, Δsdh5Δgpd1, Δsdh6Δgpd1, Δsdh4Δgpd2, Δsdh5Δgpd2, and Δsdh6Δgpd2, with improvements of 21.6 %, 27.9 %, and 22.7 %, respectively. Overall, the results highlighted that integrating mechanistic flux features substantially improves the prediction of gene knockout strains not accounted for in metabolic reconstructions. In addition, the finding in this study delivers valuable tools for comprehending and manipulating intricate phenotypes, thereby enhancing prediction accuracy and facilitating deeper insights into mechanistic aspects within the field of synthetic biology.

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.

Biomimetic Mineralization of Large Enzymes Utilizing a Stable Zirconium-Based Metal-Organic Frameworks

Enzymes are natural catalysts for a wide range of metabolic chemical transformations, including selective hydrolysis, oxidation, and phosphorylation. Herein, we demonstrate a strategy for the encapsulation of enzymes within a highly stable zirconium-based metal-organic framework. UiO-66-F4 was synthesized under mild conditions using an enzyme-compatible amino acid modulator, serine, at a modest temperature in an aqueous solution. Enzyme@UiO-66-F4 biocomposites were then formed by an in situ encapsulation route in which UiO-66-F4 grows around the enzymes and, consequently, provides protection for the enzymes. A range of enzymes, namely, lysozyme, horseradish peroxidase, and amano lipase, were successfully encapsulated within UiO-66-F4. We further demonstrate that the resulting biocomposites are stable under conditions that could denature many enzymes. Horseradish peroxidase encapsulated within UiO-66-F4 maintained its biological activity even after being treated with the proteolytic enzyme pepsin and heated at 60 °C. This strategy expands the toolbox of potential metal-organic frameworks with different topologies or functionalities that can be used as enzyme encapsulation hosts. We also demonstrate that this versatile process of in situ encapsulation of enzymes under mild conditions (i.e., submerged in water and at a modest temperature) can be generalized to encapsulate enzymes of various sizes within UiO-66-F4 while protecting them from harsh conditions (i.e., high temperatures, contact with denaturants or organic solvents).

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.

In situ electrochemical regeneration of active 1,4-NADH for enzymatic lactic acid formation via concerted functions on Pt-modified TiO2/Ti

Nicotinamide adenine dinucleotide (NAD+) and its reduced form (NADH) are key cofactors serving as essential hydrogen acceptors and donors to facilitate energy and material conversions under mild conditions. We demonstrate direct electrochemical conversion to achieve highly efficient regeneration of enzymatically active 1,4-NADH using a Pt-modified TiO2 catalyst grown directly on a Ti mesh electrode (Pt-TOT). Spectral analyses revealed that defects formed by the inclusion of Pt species in the lattice of TiO2 play a critical role in the regeneration process. In particular, Pt-TOT containing approximately 3 atom% of Pt exhibited unprecedented efficiency in the electrochemical reduction of NAD+ at the lowest overpotential to date. This exceptional performance led to the production of active 1,4-NADH with a significantly high yield of 86 ± 3% at -0.6 V vs. Ag/AgCl (-0.06 V vs. RHE) and an even higher yield of 99.5 ± 0.4% at a slightly elevated negative potential of -0.8 V vs. Ag/AgCl (-0.2 V vs. RHE). Furthermore, the electrochemically generated NADH was directly applied in the enzymatic conversion of pyruvic acid to lactic acid using lactate dehydrogenase.

High-Throughput Screening Techniques for the Selection of Thermostable Enzymes

The acquisition of a thermostable enzyme is an indispensable prerequisite for its successful implementation in industrial applications and the development of novel functionalities. Various protein engineering approaches, including rational design, semirational design, and directed evolution, have been employed to enhance thermostability. However, all of these approaches require sensitive and reliable high-throughput screening (HTS) technologies to efficiently and rapidly identify variants with improved properties. While numerous reviews focus on modification strategies for enhancing enzyme thermostability, there is a dearth of literature reviewing HTS methods specifically aimed at this objective. Herein, we present a comprehensive overview of various HTS methods utilized for modifying enzyme thermostability across different screening platforms. Additionally, we highlight significant recent examples that demonstrate the successful application of these methods. Furthermore, we address the technical challenges associated with HTS technologies used for screening thermostable enzyme variants and discuss valuable perspectives to promote further advancements in this field. This review serves as an authoritative reference source offering theoretical support for selecting appropriate screening strategies tailored to specific enzymes with the aim of improving their thermostability.

Metal-pyrimidine nanocubes immobilized enzymes with pH-switchable multienzyme-like activity for broad-pH-responsive sensing assay for organophosphorus pesticides

Peroxidase (POD)-like can only function in acidic environments and the pH mismatch restricts the application of enzyme-nanozyme cascade catalytic sensing platforms in the broad-pH-responsive assay for organophosphorus pesticides (OPs). Herein, the metal-pyrimidine nanocubes (MPNCs) with intrinsic pH-switchable POD-like and catalase (CAT)-like properties were synthesized via the coordination of pyrimidin-2-ol with Cu2+. Meanwhile, acetylcholinesterase (AChE) and choline oxidase (CHO) were simultaneously encapsulated in MPNCs to construct an enzyme-nanozyme cascade catalytic platform (AChE/CHO@MPNCs). AChE/CHO@MPNCs could catalyze the hydrolysis of acetylcholine to choline, which was subsequently converted to H2O2. The POD-like activity of MPNCs was dominant under acidic conditions, while the CAT-like activity prevailed under neutral and alkaline conditions, which could catalyze H2O2 to •OH and O2, respectively, then oxidizing dopamine (DA) to polydopamine quantum dots (PDA QDs) with different fluorescence characteristics. Consequently, OPs could be detected in a linear range from 0.05 to 1000 nM with a LOD of 0.015 nM in acidic environments and a linear range from 0.05 to 500 nM with a LOD of 0.023 nM in alkaline environments. Overall, our work expands the horizon of constructing enzyme@MOFs composites with high catalytic activity. Meanwhile, the intrinsic pH-switchable multienzyme-like property opens avenues to construct sensing platforms with broad-pH-responsive for OPs and other analytes detection.

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.

Remote stereocontrol with azaarenes via enzymatic hydrogen atom transfer

Strategies for achieving asymmetric catalysis with azaarenes have traditionally fallen short of accomplishing remote stereocontrol, which would greatly enhance accessibility to distinct azaarenes with remote chiral centres. The primary obstacle to achieving superior enantioselectivity for remote stereocontrol has been the inherent rigidity of the azaarene ring structure. Here we introduce an ene-reductase system capable of modulating the enantioselectivity of remote carbon-centred radicals on azaarenes through a mechanism of chiral hydrogen atom transfer. This photoenzymatic process effectively directs prochiral radical centres located more than six chemical bonds, or over 6 Å, from the nitrogen atom in azaarenes, thereby enabling the production of a broad array of azaarenes possessing a remote γ-stereocentre. Results from our integrated computational and experimental investigations underscore that the hydrogen bonding and steric effects of key amino acid residues are important for achieving such high stereoselectivities.

Biocatalytic Stereoselective Oxidation of 2-Arylindoles

3-Hydroxyindolenines can be used to access several structural motifs that are featured in natural products and pharmaceutical compounds, yet the chemical synthesis of 3-hydroxyindolenines is complicated by overoxidation, rearrangements, and complex product mixtures. The selectivity possible in enzymatic reactions can overcome these challenges and deliver enantioenriched products. Herein, we present the development of an asymmetric biocatalytic oxidation of 2-arylindole substrates aided by a curated library of flavin-dependent monooxygenases (FDMOs) sampled from an ancestral sequence space, a sequence similarity network, and a deep-learning-based latent space model. From this library of FDMOs, a previously uncharacterized enzyme, Champase, from the Valley fever fungus, Coccidioides immitis strain RS, was found to stereoselectively catalyze the oxidation of a variety of substituted indole substrates. The promiscuity of this enzyme is showcased by the oxidation of a wide variety of substituted 2-arylindoles to afford the respective 3-hydroxyindolenine products in moderate to excellent yields and up to 95:5 er.

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/.

Novel Biocatalysts from Specialized Metabolism

Enzymes are increasingly recognized as valuable (bio)catalysts that complement existing synthetic methods. However, the range of biotransformations used in the laboratory is limited. Here we give an overview on the biosynthesis-inspired discovery of novel biocatalysts that address various synthetic challenges. Prominent examples from this dynamic field highlight remarkable enzymes for protecting-group-free amide formation and modification, control of pericyclic reactions, stereoselective hetero- and polycyclizations, atroposelective aryl couplings, site-selective C-H activations, introduction of ring strain, and N-N bond formation. We also explore unusual functions of cytochrome P450 monooxygenases, radical SAM-dependent enzymes, flavoproteins, and enzymes recruited from primary metabolism, which offer opportunities for synthetic biology, enzyme engineering, directed evolution, and catalyst design.

Selection pressures on evolution of ribonuclease H explored with rigorous free-energy-based design

Understanding natural protein evolution and designing novel proteins are motivating interest in development of high-throughput methods to explore large sequence spaces. In this work, we demonstrate the application of multisite λ dynamics (MSλD), a rigorous free energy simulation method, and chemical denaturation experiments to quantify evolutionary selection pressure from sequence-stability relationships and to address questions of design. This study examines a mesophilic phylogenetic clade of ribonuclease H (RNase H), furthering its extensive characterization in earlier studies, focusing on E. coli RNase H (ecRNH) and a more stable consensus sequence (AncCcons) differing at 15 positions. The stabilities of 32,768 chimeras between these two sequences were computed using the MSλD framework. The most stable and least stable chimeras were predicted and tested along with several other sequences, revealing a designed chimera with approximately the same stability increase as AncCcons, but requiring only half the mutations. Comparing the computed stabilities with experiment for 12 sequences reveals a Pearson correlation of 0.86 and root mean squared error of 1.18 kcal/mol, an unprecedented level of accuracy well beyond less rigorous computational design methods. We then quantified selection pressure using a simple evolutionary model in which sequences are selected according to the Boltzmann factor of their stability. Selection temperatures from 110 to 168 K are estimated in three ways by comparing experimental and computational results to evolutionary models. These estimates indicate selection pressure is high, which has implications for evolutionary dynamics and for the accuracy required for design, and suggests accurate high-throughput computational methods like MSλD may enable more effective protein design.

Progress in research on the biosynthesis of 1,2,4-butanetriol by engineered microbes

1,2,4-butanetriol (BT) is a polyol with unique chemical properties, which has a stereocenter and can be divided into D-BT (the S-enantiomer) and L-BT (the R-enantiomer). BT can be used for the synthesis of 1,2,4-butanetriol trinitrate, 3-hydroxytetrahydrofuran, polyurethane, and other chemicals. It is widely used in the military industry, medicine, tobacco, polymer. At present, the BT is mainly synthesized by chemical methods, which are accompanied by harsh reaction conditions, poor selectivity, many by-products, and environmental pollution. Therefore, BT biosynthesis methods with the advantages of mild reaction conditions and green sustainability have become a current research hotspot. In this paper, the research status of microbial synthesis of BT was summarized from the following three aspects: (1) the biosynthetic pathway establishment for BT from xylose; (2) metabolic engineering strategies employed for improving BT production from xylose; (3) other substrates for BT production. Finally, the challenges and prospects of biosynthetic BT were discussed for future methods to improve competitiveness for industrial production.

Rational Design of l-Threonine Transaldolase-Mediated System for Enhanced Florfenicol Intermediate Production

l-threo-p-methylsulfonylphenylserine (compound 1b) is the main intermediate of florfenicol, and its efficient synthesis has been the subject of current research. Herein, Burkholderia diffusa l-threonine transaldolase (BuLTTA) was rationally designed based on the sequence-structure-function relationship. A mutant M4 (Asn35Ser/Thr352Asn) could produce 35.5 mM 1b with 88.8% conversion and 93.8% diastereoselectivity, 314 and 129% of the values observed for wild-type BuLTTA. Molecular dynamics simulations indicated that the shortened distance between key active site residues and the transition state (PLP-1b) and the improved hydrogen bond force enhanced the catalytic performance of the M4 variant. Then, the mutant M4 was combined with K. kurtzmanii alcohol dehydrogenase (KkADH) to eliminate the BuLTTA-inhibiting byproduct acetaldehyde, and a cosubstrate was added to regenerate the ADH cofactor NADH. Under optimized conditions, the yield of 1b reached 115.2 mM with a conversion of 96% and a diastereoselectivity of 95.5%. This work provides a new strategy for the efficient and sustainable production of 1b.