Automated Design of Efficient and Functionally Diverse Enzyme Repertoires

Enzyme Enhancement Through Computational Stability Design Targeting NMR-Determined Catalytic Hotspots

Enzymes are the quintessential green catalysts, but realizing their full potential for biotechnology typically requires improvement of their biomolecular properties. Catalysis enhancement, however, is often accompanied by impaired stability. Here, we show how the interplay between activity and stability in enzyme optimization can be efficiently addressed by coupling two recently proposed methodologies for guiding directed evolution. We first identify catalytic hotspots from chemical shift perturbations induced by transition-state-analogue binding and then use computational/phylogenetic design (FuncLib) to predict stabilizing combinations of mutations at sets of such hotspots. We test this approach on a previously designed de novo Kemp eliminase, which is already highly optimized in terms of both activity and stability. Most tested variants displayed substantially increased denaturation temperatures and purification yields. Notably, our most efficient engineered variant shows a ∼3-fold enhancement in activity (kcat ∼ 1700 s-1, kcat/KM ∼ 4.3 × 105 M-1 s-1) from an already heavily optimized starting variant, resulting in the most proficient proton-abstraction Kemp eliminase designed to date, with a catalytic efficiency on a par with naturally occurring enzymes. Molecular simulations pinpoint the origin of this catalytic enhancement as being due to the progressive elimination of a catalytically inefficient substrate conformation that is present in the original design. Remarkably, interaction network analysis identifies a significant fraction of catalytic hotspots, thus providing a computational tool which we show to be useful even for natural-enzyme engineering. Overall, our work showcases the power of dynamically guided enzyme engineering as a design principle for obtaining novel biocatalysts with tailored physicochemical properties, toward even anthropogenic reactions.

Rational Design and Engineering of 3-O-Sulfotransferase 1 Based on Enzyme Affinity for Improved Enzymatic Heparin Preparation

Heparin, a naturally occurring glycosaminoglycan, is renowned for its potent anticoagulant properties, which are critical for various medical applications. A significant determinant of its anticoagulant activity is the degree of 3-O-sulfation. Gaining insight into the substrate binding characteristics of 3-O-sulfotransferase-1 (3-OST-1) could enhance our understanding of the sulfotransferase family and facilitate the enzymatic preparation of heparin. This study aimed to identify mutants of 3-OST-1 with improved catalytic activities through a rational design. The enzyme activities of the mutants W72R and H144R were recorded at 26.40 and 17.21 U/L, respectively, representing increases of 1.7 and 1.1 times compared to the wild-type (WT) 3-OST-1. Notably, the enzyme activity of the double mutant W72R/H144R reached 34.41 U/L, which is 2.2 times greater than that of the WT. The heparin modified by the 3-OST-1 mutants exhibited superior anticoagulant properties compared with those modified by the WT, with W72R/H144R demonstrating the highest anticoagulant potency. Furthermore, enzyme kinetic assays and molecular dynamics simulations illustrated that the enhanced catalytic activity of the mutant enzyme resulted from an increased affinity for the substrate.

Semantical and geometrical protein encoding toward enhanced bioactivity and thermostability

Protein engineering is a pivotal aspect of synthetic biology, involving the modification of amino acids within existing protein sequences to achieve novel or enhanced functionalities and physical properties. Accurate prediction of protein variant effects requires a thorough understanding of protein sequence, structure, and function. Deep learning methods have demonstrated remarkable performance in guiding protein modification for improved functionality. However, existing approaches predominantly rely on protein sequences, which face challenges in efficiently encoding the geometric aspects of amino acids' local environment and often fall short in capturing crucial details related to protein folding stability, internal molecular interactions, and bio-functions. Furthermore, there lacks a fundamental evaluation for developed methods in predicting protein thermostability, although it is a key physical property that is frequently investigated in practice. To address these challenges, this article introduces a novel pre-training framework that integrates sequential and geometric encoders for protein primary and tertiary structures. This framework guides mutation directions toward desired traits by simulating natural selection on wild-type proteins and evaluates variant effects based on their fitness to perform specific functions. We assess the proposed approach using three benchmarks comprising over 300 deep mutational scanning assays. The prediction results showcase exceptional performance across extensive experiments compared to other zero-shot learning methods, all while maintaining a minimal cost in terms of trainable parameters. This study not only proposes an effective framework for more accurate and comprehensive predictions to facilitate efficient protein engineering, but also enhances the in silico assessment system for future deep learning models to better align with empirical requirements. The PyTorch implementation is available at https://github.com/ai4protein/ProtSSN.

Unraveling the complexity of organophosphorus pesticides: Ecological risks, biochemical pathways and the promise of machine learning

Organophosphorus pesticides (OPPs) are widely used in agriculture but pose significant ecological and human health risks due to their persistence and toxicity in the environment. While microbial degradation offers a promising solution, gaps remain in understanding the enzymatic mechanisms, degradation pathways, and ecological impacts of OPP transformation products. This review aims to bridge these gaps by integrating traditional microbial degradation research with emerging machine learning (ML) technologies. We hypothesize that ML can enhance OPP degradation studies by improving the efficiency of enzyme discovery, pathway prediction, and ecological risk assessment. Through a comprehensive analysis of microbial degradation mechanisms, environmental factors, and ML applications, we propose a novel framework that combines biochemical insights with data-driven approaches. Our review highlights the potential of ML to optimize microbial strain screening, predict degradation pathways, and identify key active sites, offering innovative strategies for sustainable pesticide management. By integrating traditional research with cutting-edge ML technologies, this work contributes to the journal's scope by promoting eco-friendly solutions for environmental protection and pesticide pollution control.

Semirational Design and Immobilization Synergistically Enhance Barnase Activity and Stability

Barnase, derived from Bacillus amyloliquefaciens, is a key enzyme in biocatalysis with widespread applications in pharmaceutical synthesis. However, its stability under extreme conditions, such as high temperatures and extreme pH, limits its industrial applications. Therefore, enhancing both its catalytic efficiency and stability through genetic engineering has become a critical focus of research. In this study, AlphaFold was employed to predict the structure of Barnase, followed by molecular docking and molecular dynamics simulations using GROMACS to design and construct 24 mutants. The results demonstrated that the enzymatic activity of the S28H and D101 K mutants increased by 75.28% and 71.86%, respectively, while the stability of D101 K declined under high temperatures. To address this, D101 K was immobilized onto a ZIF-8 carrier. Under optimized immobilization conditions (1.5 M 2-methylimidazole, 1.5 mL enzyme solution, 20 °C), ZIF-8@D101 K exhibited significantly enhanced thermal stability and pH adaptability. Recycling experiments showed that 96.21% of its activity was retained after three cycles, and 72.47% after eight cycles, demonstrating superior reusability and stability, making it more suitable for industrial applications.

Evolutionary paths that link orthogonal pairs of binding proteins

Some protein-binding pairs exhibit extreme specificities that functionally insulate them from homologs. Such pairs evolve mostly by accumulating single-point mutations, and mutants are selected if they exhibit sufficient affinity. Until now, finding a fully functional single-mutation path connecting orthogonal pairs could only be achieved by full enumeration of intermediates and was restricted to pairs that were mutationally close. We present a computational framework for discovering single-mutation paths with low molecular strain and apply it to two orthogonal bacterial endonuclease-immunity pairs separated by 17 interfacial mutations. By including mutations that bridge identities that could not be exchanged by single-nucleotide mutations, we discovered a strain-free 19-mutation path that was fully functional in vivo. The change in binding preference occurred remarkably abruptly, resulting from only one radical mutation in each partner. Furthermore, each of the specificity-switch mutations increased fitness, demonstrating that functional divergence could be driven by positive Darwinian selection.

Asymmetric Synthesis of CF3-Substituted β-Hydroxyketones and 1,3-Diols by Engineered Ketoreductases

Chiral CF3-substituted β-hydroxy ketones and 1,3-diols were prepared via the ketoreductase-catalyzed asymmetric reduction of the corresponding benzoyl trifluoroacetones. The variants of two ketoreductases, LfSDR1 and CgKR1, were screened or engineered for stereocomplementary synthesis of CF3-substituted β-hydroxy ketones with up to >99% conversions and up to >99% enantiomeric excess (ee) values. In addition, the cascade reduction or one pot reduction of diketones could afford the CF3-substituted 1,3-diols with up to >99% ratios and >99% ee values.

Robust enzyme discovery and engineering with deep learning using CataPro

Accurate prediction of enzyme kinetic parameters is crucial for enzyme exploration and modification. Existing models face the problem of either low accuracy or poor generalization ability due to overfitting. In this work, we first developed unbiased datasets to evaluate the actual performance of these methods and proposed a deep learning model, CataPro, based on pre-trained models and molecular fingerprints to predict turnover number (kcat), Michaelis constant (Km), and catalytic efficiency (kcat/Km). Compared with previous baseline models, CataPro demonstrates clearly enhanced accuracy and generalization ability on the unbiased datasets. In a representational enzyme mining project, by combining CataPro with traditional methods, we identified an enzyme (SsCSO) with 19.53 times increased activity compared to the initial enzyme (CSO2) and then successfully engineered it to improve its activity by 3.34 times. This reveals the high potential of CataPro as an effective tool for future enzyme discovery and modification.

Laccase based per- and polyfluoroalkyl substances degradation: Status and future perspectives

Per- and polyfluoroalkyl substances (PFAS) with stable carbon-fluorine bonds are used in a wide range of industrial and commercial applications. Due to their extreme environmental persistence, PFAS have the potential to bioaccumulate, cause adverse effects, and present challenges regarding remediation. Recently, microbial and enzymatic reactions for sustainable degradation of PFAS have gained attention from researchers, although biological decomposition of PFAS remains challenging. Surprisingly, laccases, the multi-copper oxidases produced by various organisms, showed potential for PFAS degradation. Mediators play key roles in initiating laccase induced PFAS degradation and defluorination reactions. The laccase-catalyzed PFAS degradation reactions are relatively slower than normal biocatalytic reactions and the low activity of native laccases constrains their capacity to complete defluorination. With their low redox potential and narrow substrate scope, an innovative remediation strategy must be taken to accelerate this reaction. In this review we have summarized the status, challenges, and future perspectives of enzymatic PFAS degradation. The knowledge of laccase-based defluorination and the molecular basis of the reaction mechanisms overviewed in this study could inform future applications of laccases for sustainable PFAS remediation.

Navigating the challenges of engineering composite specialized metabolite pathways in plants

Plants are a valuable source of diverse specialized metabolites with numerous applications. However, these compounds are often produced in limited quantities, particularly under unfavorable ecological conditions. To achieve sufficient levels of target metabolites, alternative strategies such as pathway engineering in heterologous systems like microbes (e.g., bacteria and fungi) or cell-free systems can be employed. Another approach is plant engineering, which aims to either enhance the native production in the original plant or reconstruct the target pathway in a model plant system. Although increasing metabolite production in the native plant is a promising strategy, these source plants are often exotic and pose significant challenges for genetic manipulation. Effective pathway engineering requires comprehensive prior knowledge of the genes and enzymes involved, as well as the precursor, intermediate, branching, and final metabolites. Thus, a thorough elucidation of the biosynthetic pathway is closely linked to successful metabolic engineering in host or model systems. In this review, we highlight recent advances in strategies for biosynthetic pathway elucidation and metabolic engineering. We focus on efforts to engineer complex, multi-step pathways that require the expression of at least eight genes for transient and three genes for stable transformation. Reports on the engineering of complex pathways in stably transformed plants remain relatively scarce. We discuss the major hurdles in pathway elucidation and strategies for overcoming them, followed by an overview of achievements, challenges, and solutions in pathway reconstitution through metabolic engineering. Recent advances including computer-based predictions offer valuable platforms for the sustainable production of specialized metabolites in plants.

AI-enabled alkaline-resistant evolution of protein to apply in mass production

Artificial intelligence (AI) models have been used to study the compositional regularities of proteins in nature, enabling it to assist in protein design to improve the efficiency of protein engineering and reduce manufacturing cost. However, in industrial settings, proteins are often required to work in extreme environments where they are relatively scarce or even non-existent in nature. Since such proteins are almost absent in the training datasets, it is uncertain whether AI model possesses the capability of evolving the protein to adapt extreme conditions. Antibodies are crucial components of affinity chromatography, and they are hoped to remain active at the extreme environments where most proteins cannot tolerate. In this study, we applied an advanced large language model (LLM), the Pro-PRIME model, to improve the alkali resistance of a representative antibody, a VHH antibody capable of binding to growth hormone. Through two rounds of design, we ensured that the selected mutant has enhanced functionality, including higher thermal stability, extreme pH resistance, and stronger affinity, thereby validating the generalized capability of the LLM in meeting specific demands. To the best of our knowledge, this is the first LLM-designed protein product, which is successfully applied in mass production.

htFuncLib: Designing Libraries of Active-site Multipoint Mutants for Protein Optimization

Protein function relies on accurate and densely packed constellations of amino acids within the active site. The high density in the active site optimizes activity but reduces tolerance to mutations, thereby frustrating efforts to engineer or design new or dramatically improved activity. Introducing new activities may therefore require simultaneous multipoint mutations. Still, in a phenomenon known as epistasis, the outcome of combinations of mutations can differ significantly-and even reverse-the impact of the individual mutations, limiting predictability. To address these challenges we previously developed FuncLib, a method for the computational design of multipoint mutants in active sites. We recently extended FuncLib to enable the design of large combinatorial mutation libraries for high-throughput screening in a method called htFuncLib that generates compatible sets of mutations likely to yield functional multipoint mutants. htFuncLib enables scalable library design and experimental screening of hundreds and up to millions of active-site variants. This approach has generated thousands of active enzymes and fluorescent proteins with diverse functional properties. We have updated the FuncLib web server (https://FuncLib.weizmann.ac.il/) to support htFuncLib and introduced an electronic notebook (https://github.com/Fleishman-Lab/htFuncLib-web-server) for customizable library design, making those tools easily accessible for protein engineering and design. The new FuncLib web server enables reliable and scalable design of function for low-, medium- and high-throughput experiments through a single computational platform. We envision that this server will accelerate the optimization and discovery of function in enzymes, antibodies, and other proteins.

Improved Enzymatic Properties of Chitosanase CsnMY002 from Bacillus subtilis via Computational Design

Chitooligosaccharides (COSs) are a class of functional carbohydrates with significant application prospects in food and medicine. Chitosanase CsnMY002 from the GH46 family has been used to prepare COS with controlled degrees of polymerization. To enhance the industrial applicability of CsnMY002, molecular dynamics (MD) simulations were applied to investigate the structure-property relationship. Guided by the simulation results, the beneficial mutants were screened through a synergistic strategy using a residue-folding free energy calculation and consensus sequence analysis. Iterative combinations constructed the mutant Mut6 (A49G/K70A/S84A/N89G/D199R/N221G) with significantly improved thermal stability, which had a half-life (t1/2 value) at 55 °C and 75 °C that was 1.80 and 1.62 times higher than that of the wild type, respectively. A highly active mutant, Mut2, was created, exhibiting a 1.52 times catalytic efficiency of the wild type. An MD simulation analysis of the mutants suggested that the improved enzymatic properties were highly correlated with changes in the dynamic behaviours of the enzyme structure. This study generated more suitable CsnMY002 variants for COS production and provided a comprehensive strategy for the optimization of other industrial enzymes with application potential.

Bioinformatics-assisted mining and design of novel pullulanase suitable for starch cold hydrolysis

Cold-active pullulanases with good catalytic performance possess promising applications in cold hydrolysis of starch. Adopting bioinformatics-assisted mining strategies, 7 candidate cold-active pullulanases were initially screened out from IMG/MER database. Among the candidates, PulBs exhibited good thermostability and the highest specific activity of 147.4 U/mg. The half-life of PulBs was about 200 h at 35 °C. Employing PulBs as the initial enzyme, the active-site design of FuncLib was implemented to enhance the activity. The design PulBs-20 exhibited an enhanced specific activity of 209.9 U/mg, which was 1.4 times that of PulBs. Furthermore, the thermostability of PulBs-20 was augmented, with a half-life of 250 h at 35 °C. When applied in the cold hydrolysis of starch, PulBs-20 can effectively enhance the hydrolysis effect of raw starch. Supplemented with the raw starch-hydrolyzing α-amylase AmyZ1 and PulBs-20, the hydrolysis rate of raw corn starch increased to 53.5 %, which was 1.3 times that of using AmyZ1 alone. Due to its high hydrolysis activity and good thermostability, PulBs-20 can serve as an efficient accessory enzyme in starch cold hydrolysis.

A Practical Guide to Computational Tools for Engineering Biocatalytic Properties

The growing demand for efficient, selective, and stable enzymes has fueled advancements in computational enzyme engineering, a field that complements experimental methods to accelerate enzyme discovery. With a plethora of software and tools available, researchers from different disciplines often face challenges in selecting the most suitable method that meets their requirements and available starting data. This review categorizes the computational tools available for enzyme engineering based on their capacity to enhance the following specific biocatalytic properties of biotechnological interest: (i) protein-ligand affinity/selectivity, (ii) catalytic efficiency, (iii) thermostability, and (iv) solubility for recombinant enzyme production. By aligning tools with their respective scoring functions, we aim to guide researchers, particularly those new to computational methods, in selecting the appropriate software for the design of protein engineering campaigns. De novo enzyme design, involving the creation of novel proteins, is beyond this review's scope. Instead, we focus on practical strategies for fine-tuning enzymatic performance within an established reference framework of natural proteins.

ProBID-Net: a deep learning model for protein-protein binding interface design

Protein-protein interactions are pivotal in numerous biological processes. The computational design of these interactions facilitates the creation of novel binding proteins, crucial for advancing biopharmaceutical products. With the evolution of artificial intelligence (AI), protein design tools have swiftly transitioned from scoring-function-based to AI-based models. However, many AI models for protein design are constrained by assuming complete unfamiliarity with the amino acid sequence of the input protein, a feature most suited for de novo design but posing challenges in designing protein-protein interactions when the receptor sequence is known. To bridge this gap in computational protein design, we introduce ProBID-Net. Trained using natural protein-protein complex structures and protein domain-domain interface structures, ProBID-Net can discern features from known target protein structures to design specific binding proteins based on their binding sites. In independent tests, ProBID-Net achieved interface sequence recovery rates of 52.7%, 43.9%, and 37.6%, surpassing or being on par with ProteinMPNN in binding protein design. Validated using AlphaFold-Multimer, the sequences designed by ProBID-Net demonstrated a close correspondence between the design target and the predicted structure. Moreover, the model's output can predict changes in binding affinity upon mutations in protein complexes, even in scenarios where no data on such mutations were provided during training (zero-shot prediction). In summary, the ProBID-Net model is poised to significantly advance the design of protein-protein interactions.

Site-specific incorporation of 19F-nulcei at protein C-terminus to probe allosteric conformational transitions of metalloproteins

Allosteric conformational change is an important paradigm in the regulation of protein function, which is typically triggered by the binding of small cofactors, metal ions or protein partners. Here, we found those conformational transitions can be effectively monitored by 19F NMR, facilitated by a site-specific 19F incorporation strategy at the protein C-terminus using asparaginyl endopeptidase (AEP). Three case studies show that C-terminal 19F-nuclei can reveal protein dynamics not only adjacent but also distal to C-terminus, including those occurring in a hemoprotein neuroglobin (Ngb), calmodulin (CaM), and a cobalt metalloregulator (CoaR) responding to both cobalt and tetrapyrrole. In Ngb, the heme orientation disorder is affected by missense mutations that perturb backbone rigidity or surface charges close to the heme axial ligands. In CaM, the C-terminal 19F-nuclei is an ideal probe for detecting the binding states of Ca2+, peptides and inhibitors. Furthermore, multiple 19F-moieties were incorporated into the two domains of CoaR, revealing the intrinsically disordered C-terminal metal binding tail might be an allosteric conformational switch to maintain cobalt homeostasis and balance corrinoid biosynthesis. This study demonstrates that the AEP-based 19F-modification strategy can be applied to various targets to study allosteric regulation, especially for those biological processes modulated by the protein C-terminus.

Highly multiplexed design of an allosteric transcription factor to sense new ligands

Allosteric transcription factors (aTF) regulate gene expression through conformational changes induced by small molecule binding. Although widely used as biosensors, aTFs have proven challenging to design for detecting new molecules because mutation of ligand-binding residues often disrupts allostery. Here, we develop Sensor-seq, a high-throughput platform to design and identify aTF biosensors that bind to non-native ligands. We screen a library of 17,737 variants of the aTF TtgR, a regulator of a multidrug exporter, against six non-native ligands of diverse chemical structures - four derivatives of the cancer therapeutic tamoxifen, the antimalarial drug quinine, and the opiate analog naltrexone - as well as two native flavonoid ligands, naringenin and phloretin. Sensor-seq identifies biosensors for each of these ligands with high dynamic range and diverse specificity profiles. The structure of a naltrexone-bound design shows shape-complementary methionine-aromatic interactions driving ligand specificity. To demonstrate practical utility, we develop cell-free detection systems for naltrexone and quinine. Sensor-seq enables rapid and scalable design of new biosensors, overcoming constraints of natural biosensors.

Microbial Cell Factories in the Bioeconomy Era: From Discovery to Creation

Microbial cell factories (MCFs) are extensively used to produce a wide array of bioproducts, such as bioenergy, biochemical, food, nutrients, and pharmaceuticals, and have been regarded as the "chips" of biomanufacturing that will fuel the emerging bioeconomy era. Biotechnology advances have led to the screening, investigation, and engineering of an increasing number of microorganisms as diverse MCFs, which are the workhorses of biomanufacturing and help develop the bioeconomy. This review briefly summarizes the progress and strategies in the development of robust and efficient MCFs for sustainable and economic biomanufacturing. First, a comprehensive understanding of microbial chassis cells, including accurate genome sequences and corresponding annotations; metabolic and regulatory networks governing substances, energy, physiology, and information; and their similarity and uniqueness compared with those of other microorganisms, is needed. Moreover, the development and application of effective and efficient tools is crucial for engineering both model and nonmodel microbial chassis cells into efficient MCFs, including the identification and characterization of biological parts, as well as the design, synthesis, assembly, editing, and regulation of genes, circuits, and pathways. This review also highlights the necessity of integrating automation and artificial intelligence (AI) with biotechnology to facilitate the development of future customized artificial synthetic MCFs to expedite the industrialization process of biomanufacturing and the bioeconomy.

Click, Compute, Create: A Review of Web-based Tools for Enzyme Engineering

Enzyme engineering, though pivotal across various biotechnological domains, is often plagued by its time-consuming and labor-intensive nature. This review aims to offer an overview of supportive in silico methodologies for this demanding endeavor. Starting from methods to predict protein structures, to classification of their activity and even the discovery of new enzymes we continue with describing tools used to increase thermostability and production yields of selected targets. Subsequently, we discuss computational methods to modulate both, the activity as well as selectivity of enzymes. Last, we present recent approaches based on cutting-edge machine learning methods to redesign enzymes. With exception of the last chapter, there is a strong focus on methods easily accessible via web-interfaces or simple Python-scripts, therefore readily useable for a diverse and broad community.

GGAssembler: Precise and economical design and synthesis of combinatorial mutation libraries

Golden Gate assembly (GGA) can seamlessly generate full-length genes from DNA fragments. In principle, GGA could be used to design combinatorial mutation libraries for protein engineering, but creating accurate, complex, and cost-effective libraries has been challenging. We present GGAssembler, a graph-theoretical method for economical design of DNA fragments that assemble a combinatorial library that encodes any desired diversity. We used GGAssembler for one-pot in vitro assembly of camelid antibody libraries comprising >105 variants with DNA costs <0.007$ per variant and dropping significantly with increased library complexity. >93% of the desired variants were present in the assembly product and >99% were represented within the expected order of magnitude as verified by deep sequencing. The GGAssembler workflow is, therefore, an accurate approach for generating complex variant libraries that may drastically reduce costs and accelerate discovery and optimization of antibodies, enzymes and other proteins. The workflow is accessible through a Google Colab notebook at https://github.com/Fleishman-Lab/GGAssembler.

On synergy between ultrahigh throughput screening and machine learning in biocatalyst engineering

Protein design and directed evolution have separately contributed enormously to protein engineering. Without being mutually exclusive, the former relies on computation from first principles, while the latter is a combinatorial approach based on chance. Advances in ultrahigh throughput (uHT) screening, next generation sequencing and machine learning may create alternative routes to engineered proteins, where functional information linked to specific sequences is interpreted and extrapolated in silico. In particular, the miniaturisation of functional tests in water-in-oil emulsion droplets with picoliter volumes and their rapid generation and analysis (>1 kHz) allows screening of >107-membered libraries in a day. Subsequently, decoding the selected clones by short or long-read sequencing methods leads to large sequence-function datasets that may allow extrapolation from experimental directed evolution to further improved mutants beyond the observed hits. In this work, we explore experimental strategies for how to draw up 'fitness landscapes' in sequence space with uHT droplet microfluidics, review the current state of AI/ML in enzyme engineering and discuss how uHT datasets may be combined with AI/ML to make meaningful predictions and accelerate biocatalyst engineering.

Enhancing catalytic efficiency of Bacillus subtilis laccase BsCotA through active site pocket design

BsCotA laccase is a promising candidate for industrial application due to its excellent thermal stability. In this research, our objective was to enhance the catalytic efficiency of BsCotA by modifying the active site pocket. We utilized a strategy combining the diversity design of the active site pocket with molecular docking screening, which resulted in selecting five variants for characterization. All five variants proved functional, with four demonstrating improved turnover rates. The most effective variants exhibited a remarkable 7.7-fold increase in catalytic efficiency, evolved from 1.54 × 105 M-1 s-1 to 1.18 × 106 M-1 s-1, without any stability loss. To investigate the underlying molecular mechanisms, we conducted a comprehensive structural analysis of our variants. The analysis suggested that substituting Leu386 with aromatic residues could enhance BsCotA's ability to accommodate the 2,2'-azino-di-(3-ethylbenzothiazoline)-6-sulfonate (ABTS) substrate. However, the inclusion of charged residues, G323D and G417H, into the active site pocket reduced kcat. Ultimately, our research contributes to a deeper understanding of the role played by residues in the laccases' active site pocket, while successfully demonstrating a method to lift the catalytic efficiency of BsCotA. KEY POINTS: • Active site pocket design that enhanced BsCotA laccase efficiency • 7.7-fold improved in catalytic rate • All tested variants retain thermal stability.

Genome Mining and Biological Engineering of Type III Borosins from Bacteria

Borosins are a class of ribosomally synthesized and post-translationally modified peptides (RiPPs) with α-N-methylated backbones. Although the first mature compound of borosin was reported in 1997, the biosynthetic pathway was elucidated 20 years later. Until this work, borosins have been able to be categorized into 11 types based on the features of their protein structure and core peptides. Type III borosins were reported only in fungi initially. In order to explore the sources and potential of type III borosins, a precise genome mining work of type III borosins was conducted in bacteria and KchMA's self-methylation activity was validated by biochemical experiment. Furthermore, a commercial protease and AI-assisted rational design was employed to engineer KchMA for the capacity to produce various N-methylated peptides. Our work demonstrates that type III borosins are abundant not only in eukaryotes but also in bacteria and have immense potential as a tool for synthetic biology.

Addressing epistasis in the design of protein function

Mutations in protein active sites can dramatically improve function. The active site, however, is densely packed and extremely sensitive to mutations. Therefore, some mutations may only be tolerated in combination with others in a phenomenon known as epistasis. Epistasis reduces the likelihood of obtaining improved functional variants and dramatically slows natural and lab evolutionary processes. Research has shed light on the molecular origins of epistasis and its role in shaping evolutionary trajectories and outcomes. In addition, sequence- and AI-based strategies that infer epistatic relationships from mutational patterns in natural or experimental evolution data have been used to design functional protein variants. In recent years, combinations of such approaches and atomistic design calculations have successfully predicted highly functional combinatorial mutations in active sites. These were used to design thousands of functional active-site variants, demonstrating that, while our understanding of epistasis remains incomplete, some of the determinants that are critical for accurate design are now sufficiently understood. We conclude that the space of active-site variants that has been explored by evolution may be expanded dramatically to enhance natural activities or discover new ones. Furthermore, design opens the way to systematically exploring sequence and structure space and mutational impacts on function, deepening our understanding and control over protein activity.

Biocatalytic Synthesis of Ruxolitinib Intermediate via Engineered Imine Reductase

An enzyme catalyzed strategy for the synthesis of a chiral hydrazine from 3-cyclopentyl-3-oxopropanenitrile 5 and hydrazine hydrate 2 is presented. An imine reductase (IRED) from Streptosporangium roseum was identified to catalyze the reaction between 3-cyclopentyl-3-oxopropanenitrile 5 and hydrazine hydrate 2 to produce trace amounts of (R)-3-cyclopentyl-3-hydrazineylpropanenitrile 4. We employed a 2-fold approach to optimize the catalytic performance of this enzyme. First, a transition state analogue (TSA) model was constructed to illuminate the enzyme-substrate interactions. Subsequently, the Enzyme_design and Funclib methods were utilized to predict mutants for experimental evaluation. Through three rounds of site-directed mutagenesis, site saturation mutagenesis, and combinatorial mutagenesis, we obtained mutant M6 with a yield of 98% and an enantiomeric excess (ee) of 99%. This study presents an effective method for constructing a hydrazine derivative via IRED-catalyzed reductive amination of ketone and hydrazine. Furthermore, it provides a general approach for constructing suitable enzymes, starting from nonreactive enzymes and gradually enhancing their catalytic activity through active site modifications.

Unleashing the Power of Evolution in Xylanase Engineering: Investigating the Role of Distal Mutation Regulation

The drive to enhance enzyme performance in industrial applications frequently clashes with the practical limitations of exhaustive experimental screening, underscoring the urgency for more refined and strategic methodologies in enzyme engineering. In this study, xylanase Xyl-1 was used as the model, coupling evolutionary insights with energy functions to obtain theoretical potential mutants, which were subsequently validated experimentally. We observed that mutations in the nonloop region primarily aimed at enhancing stability and also encountered selective pressure for activity. Notably, mutations in this region simultaneously boosted the Xyl-1 stability and activity, achieving a 65% success rate. Using a greedy strategy, mutant M4 was developed, achieving a 12 °C higher melting temperature and doubled activity. By integration of spectroscopy, crystallography, and quantum mechanics/molecular mechanics molecular dynamics, the mechanism behind the enhanced thermal stability of M4 was elucidated. It was determined that the activity differences between M4 and the wild type were primarily driven by dynamic factors influenced by distal mutations. In conclusion, the study emphasizes the pivotal role of evolution-based approaches in augmenting the stability and activity of the enzymes. It sheds light on the unique adaptive mechanisms employed by various structural regions of proteins and expands our understanding of the intricate relationship between distant mutations and enzyme dynamics.

Opportunities and challenges in design and optimization of protein function

The field of protein design has made remarkable progress over the past decade. Historically, the low reliability of purely structure-based design methods limited their application, but recent strategies that combine structure-based and sequence-based calculations, as well as machine learning tools, have dramatically improved protein engineering and design. In this Review, we discuss how these methods have enabled the design of increasingly complex structures and therapeutically relevant activities. Additionally, protein optimization methods have improved the stability and activity of complex eukaryotic proteins. Thanks to their increased reliability, computational design methods have been applied to improve therapeutics and enzymes for green chemistry and have generated vaccine antigens, antivirals and drug-delivery nano-vehicles. Moreover, the high success of design methods reflects an increased understanding of basic rules that govern the relationships among protein sequence, structure and function. However, de novo design is still limited mostly to α-helix bundles, restricting its potential to generate sophisticated enzymes and diverse protein and small-molecule binders. Designing complex protein structures is a challenging but necessary next step if we are to realize our objective of generating new-to-nature activities.

Machine learning-guided co-optimization of fitness and diversity facilitates combinatorial library design in enzyme engineering

The effective design of combinatorial libraries to balance fitness and diversity facilitates the engineering of useful enzyme functions, particularly those that are poorly characterized or unknown in biology. We introduce MODIFY, a machine learning (ML) algorithm that learns from natural protein sequences to infer evolutionarily plausible mutations and predict enzyme fitness. MODIFY co-optimizes predicted fitness and sequence diversity of starting libraries, prioritizing high-fitness variants while ensuring broad sequence coverage. In silico evaluation shows that MODIFY outperforms state-of-the-art unsupervised methods in zero-shot fitness prediction and enables ML-guided directed evolution with enhanced efficiency. Using MODIFY, we engineer generalist biocatalysts derived from a thermostable cytochrome c to achieve enantioselective C-B and C-Si bond formation via a new-to-nature carbene transfer mechanism, leading to biocatalysts six mutations away from previously developed enzymes while exhibiting superior or comparable activities. These results demonstrate MODIFY's potential in solving challenging enzyme engineering problems beyond the reach of classic directed evolution.

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

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

Unlocking the potential of enzyme engineering via rational computational design strategies

Enzymes play a pivotal role in various industries by enabling efficient, eco-friendly, and sustainable chemical processes. However, the low turnover rates and poor substrate selectivity of enzymes limit their large-scale applications. Rational computational enzyme design, facilitated by computational algorithms, offers a more targeted and less labor-intensive approach. There has been notable advancement in employing rational computational protein engineering strategies to overcome these issues, it has not been comprehensively reviewed so far. This article reviews recent developments in rational computational enzyme design, categorizing them into three types: structure-based, sequence-based, and data-driven machine learning computational design. Case studies are presented to demonstrate successful enhancements in catalytic activity, stability, and substrate selectivity. Lastly, the article provides a thorough analysis of these approaches, highlights existing challenges and potential solutions, and offers insights into future development directions.

Automated optimization of the solubility of a hyper-stable α-amylase

Most successes in computational protein engineering to date have focused on enhancing one biophysical trait, while multi-trait optimization remains a challenge. Different biophysical properties are often conflicting, as mutations that improve one tend to worsen the others. In this study, we explored the potential of an automated computational design strategy, called CamSol Combination, to optimize solubility and stability of enzymes without affecting their activity. Specifically, we focus on Bacillus licheniformis α-amylase (BLA), a hyper-stable enzyme that finds diverse application in industry and biotechnology. We validate the computational predictions by producing 10 BLA variants, including the wild-type (WT) and three designed models harbouring between 6 and 8 mutations each. Our results show that all three models have substantially improved relative solubility over the WT, unaffected catalytic rate and retained hyper-stability, supporting the algorithm's capacity to optimize enzymes. High stability and solubility embody enzymes with superior resilience to chemical and physical stresses, enhance manufacturability and allow for high-concentration formulations characterized by extended shelf lives. This ability to readily optimize solubility and stability of enzymes will enable the rapid and reliable generation of highly robust and versatile reagents, poised to contribute to advancements in diverse scientific and industrial domains.

Highly multiplexed design of an allosteric transcription factor to sense novel ligands

Allosteric transcription factors (aTF), widely used as biosensors, have proven challenging to design for detecting novel molecules because mutation of ligand-binding residues often disrupts allostery. We developed Sensor-seq, a high-throughput platform to design and identify aTF biosensors that bind to non-native ligands. We screened a library of 17,737 variants of the aTF TtgR, a regulator of a multidrug exporter, against six non-native ligands of diverse chemical structures - four derivatives of the cancer therapeutic tamoxifen, the antimalarial drug quinine, and the opiate analog naltrexone - as well as two native flavonoid ligands, naringenin and phloretin. Sensor-seq identified novel biosensors for each of these ligands with high dynamic range and diverse specificity profiles. The structure of a naltrexone-bound design showed shape-complementary methionine-aromatic interactions driving ligand specificity. To demonstrate practical utility, we developed cell-free detection systems for naltrexone and quinine. Sensor-seq enables rapid, scalable design of new biosensors, overcoming constraints of natural biosensors.

Engineering carboxylic acid reductases and unspecific peroxygenases for flavor and fragrance biosynthesis

Emerging consumer demand for safer, more sustainable flavors and fragrances has created new challenges for the industry. Enzymatic syntheses represent a promising green production route, but the broad application requires engineering advancements for expanded diversity, improved selectivity, and enhanced stability to be cost-competitive with current methods. This review discusses recent advances and future outlooks for enzyme engineering in this field. We focus on carboxylic acid reductases (CARs) and unspecific peroxygenases (UPOs) that enable selective productions of complex flavor and fragrance molecules. Both enzyme types consist of natural variants with attractive characteristics for biocatalytic applications. Applying protein engineering methods, including rational design and directed evolution in concert with computational modeling, present excellent examples for property improvements to unleash the full potential of enzymes in the biosynthesis of value-added chemicals.

Enhancing the Catalytic Efficiency of D-lactonohydrolase through the Synergy of Tunnel Engineering, Evolutionary Analysis, and Force-Field Calculations

Computational design advances enzyme evolution and their use in biocatalysis in a faster and more efficient manner. In this study, a synergistic approach integrating tunnel engineering, evolutionary analysis, and force-field calculations has been employed to enhance the catalytic activity of D-lactonohydrolase (D-Lac), which is a pivotal enzyme involved in the resolution of racemic pantolactone during the production of vitamin B5. The best mutant, N96S/A271E/F274Y/F308G (M3), was obtained and its catalytic efficiency (kcat/KM) was nearly 23-fold higher than that of the wild-type. The M3 whole-cell converted 20 % of DL-pantolactone into D-pantoic acid (D-PA, >99 % e.e.) with a conversion rate of 47 % and space-time yield of 107.1 g L-1 h-1, demonstrating its great potential for industrial-scale D-pantothenic acid production. Molecular dynamics (MD) simulations revealed that the reduction in the steric hindrance within the substrate tunnel and conformational reconstruction of the distal loop resulted in a more favourable"catalytic" conformation, making it easier for the substrate and enzyme to enter their pre-reaction state. This study illustrates the potential of the distal residue on the pivotal loop at the entrance of the D-Lac substrate tunnel as a novel modification hotspot capable of reshaping energy patterns and consequently influencing the enzymatic activity.

Empowering Protein Engineering through Recombination of Beneficial Substitutions

Directed evolution stands as a seminal technology for generating novel protein functionalities, a cornerstone in biocatalysis, metabolic engineering, and synthetic biology. Today, with the development of various mutagenesis methods and advanced analytical machines, the challenge of diversity generation and high-throughput screening platforms is largely solved, and one of the remaining challenges is: how to empower the potential of single beneficial substitutions with recombination to achieve the epistatic effect. This review overviews experimental and computer-assisted recombination methods in protein engineering campaigns. In addition, integrated and machine learning-guided strategies were highlighted to discuss how these recombination approaches contribute to generating the screening library with better diversity, coverage, and size. A decision tree was finally summarized to guide the further selection of proper recombination strategies in practice, which was beneficial for accelerating protein engineering.

Rheostats, toggles, and neutrals, Oh my! A new framework for understanding how amino acid changes modulate protein function

Advances in personalized medicine and protein engineering require accurately predicting outcomes of amino acid substitutions. Many algorithms correctly predict that evolutionarily-conserved positions show "toggle" substitution phenotypes, which is defined when a few substitutions at that position retain function. In contrast, predictions often fail for substitutions at the less-studied "rheostat" positions, which are defined when different amino acid substitutions at a position sample at least half of the possible functional range. This review describes efforts to understand the impact and significance of rheostat positions: (1) They have been observed in globular soluble, integral membrane, and intrinsically disordered proteins; within single proteins, their prevalence can be up to 40%. (2) Substitutions at rheostat positions can have biological consequences and ∼10% of substitutions gain function. (3) Although both rheostat and "neutral" (defined when all substitutions exhibit wild-type function) positions are nonconserved, the two classes have different evolutionary signatures. (4) Some rheostat positions have pleiotropic effects on function, simultaneously modulating multiple parameters (e.g., altering both affinity and allosteric coupling). (5) In structural studies, substitutions at rheostat positions appear to cause only local perturbations; the overall conformations appear unchanged. (6) Measured functional changes show promising correlations with predicted changes in protein dynamics; the emergent properties of predicted, dynamically coupled amino acid networks might explain some of the complex functional outcomes observed when substituting rheostat positions. Overall, rheostat positions provide unique opportunities for using single substitutions to tune protein function. Future studies of these positions will yield important insights into the protein sequence/function relationship.

Key interaction networks: Identifying evolutionarily conserved non-covalent interaction networks across protein families

Protein structure (and thus function) is dictated by non-covalent interaction networks. These can be highly evolutionarily conserved across protein families, the members of which can diverge in sequence and evolutionary history. Here we present KIN, a tool to identify and analyze conserved non-covalent interaction networks across evolutionarily related groups of proteins. KIN is available for download under a GNU General Public License, version 2, from https://www.github.com/kamerlinlab/KIN. KIN can operate on experimentally determined structures, predicted structures, or molecular dynamics trajectories, providing insight into both conserved and missing interactions across evolutionarily related proteins. This provides useful insight both into protein evolution, as well as a tool that can be exploited for protein engineering efforts. As a showcase system, we demonstrate applications of this tool to understanding the evolutionary-relevant conserved interaction networks across the class A β-lactamases.

Rational design of a highly active N-glycosyltransferase mutant using fragment replacement approach

The modularity of carbohydrate-active enzymes facilitates that enzymes with different functions have similar fragments. However, because of the complex structure of the enzyme active sites and the epistatic effects of various mutations on enzyme activity, it is difficult to design enzymes with multiple mutation sites using conventional methods. In this study, we designed multi-point mutants by fragment replacement in the donor-acceptor binding pocket of Actinobacillus pleuropneumoniae N-glycosyltransferase (ApNGT) to obtain novel properties. Candidate fragments were selected from a customized glycosyltransferase database. The stability and substrate-binding energy of the three fragment replacement mutants were calculated in comparison with wild-type ApNGT, and mutants with top-ranking stability and middle-ranking substrate-binding energy were chosen for priority experimental verification. We found that a mutant called F13, which increased the glycosylation efficiency of the natural substrate by 1.44 times, the relative conversion of UDP-galactose by 14.2 times, and the relative conversion of UDP-xylose from almost 0 to 78.6%. Most importantly, F13 mutant acquired an entirely new property, the ability to utilize UDP-glucuronic acid. On one hand, this work shows that replacing similar fragments in the donor-acceptor binding pocket of the enzyme might provide new ideas for designing mutants with new properties; on the other hand, F13 mutant is expected to play an important role in targeted drug delivery.

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 silico design of multipoint mutants for enhanced performance of Thermomyces lanuginosus lipase for efficient biodiesel production

BACKGROUND

Biodiesel, an emerging sustainable and renewable clean energy, has garnered considerable attention as an alternative to fossil fuels. Although lipases are promising catalysts for biodiesel production, their efficiency in industrial-scale application still requires improvement.

RESULTS

In this study, a novel strategy for multi-site mutagenesis in the binding pocket was developed via FuncLib (for mutant enzyme design) and Rosetta Cartesian_ddg (for free energy calculation) to improve the reaction rate and yield of lipase-catalyzed biodiesel production. Thermomyces lanuginosus lipase (TLL) with high activity and thermostability was obtained using the Pichia pastoris expression system. The specific activities of the mutants M11 and M21 (each with 5 and 4 mutations) were 1.50- and 3.10-fold higher, respectively, than those of the wild-type (wt-TLL). Their corresponding melting temperature profiles increased by 10.53 and 6.01 °C, [Formula: see text] (the temperature at which the activity is reduced to 50% after 15 min incubation) increased from 60.88 to 68.46 °C and 66.30 °C, and the optimum temperatures shifted from 45 to 50 °C. After incubation in 60% methanol for 1 h, the mutants M11 and M21 retained more than 60% activity, and 45% higher activity than that of wt-TLL. Molecular dynamics simulations indicated that the increase in thermostability could be explained by reduced atomic fluctuation, and the improved catalytic properties were attributed to a reduced binding free energy and newly formed hydrophobic interaction. Yields of biodiesel production catalyzed by mutants M11 and M21 for 48 h at an elevated temperature (50 °C) were 94.03% and 98.56%, respectively, markedly higher than that of the wt-TLL (88.56%) at its optimal temperature (45 °C) by transesterification of soybean oil.

CONCLUSIONS

An integrating strategy was first adopted to realize the co-evolution of catalytic efficiency and thermostability of lipase. Two promising mutants M11 and M21 with excellent properties exhibited great potential for practical applications for in biodiesel production.

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

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

Design of stable circular permutants of the GroEL chaperone apical domain

Enhancing protein stability holds paramount significance in biotechnology, therapeutics, and the food industry. Circular permutations offer a distinctive avenue for manipulating protein stability while keeping intra-protein interactions intact. Amidst the creation of circular permutants, determining the optimal placement of the new N- and C-termini stands as a pivotal, albeit largely unexplored, endeavor. In this study, we employed PONDR-FIT's predictions of disorder propensity to guide the design of circular permutants for the GroEL apical domain (residues 191-345). Our underlying hypothesis posited that a higher predicted disorder value would correspond to reduced stability in the circular permutants, owing to the increased likelihood of fluctuations in the novel N- and C-termini. To substantiate this hypothesis, we engineered six circular permutants, positioning glycines within the loops as locations for the new N- and C-termini. We demonstrated the validity of our hypothesis along the set of the designed circular permutants, as supported by measurements of melting temperatures by circular dichroism and differential scanning microcalorimetry. Consequently, we propose a novel computational methodology that rationalizes the design of circular permutants with projected stability. Video Abstract.

Comparative modeling and enzymatic affinity of novel haloacid dehalogenase from Bacillus megaterium strain BHS1 isolated from alkaline Blue Lake in Turkey

This study presents the initial structural model of L-haloacid dehalogenase (DehLBHS1) from Bacillus megaterium BHS1, an alkalotolerant bacterium known for its ability to degrade halogenated environmental pollutants. The model provides insights into the structural features of DehLBHS1 and expands our understanding of the enzymatic mechanisms involved in the degradation of these hazardous pollutants. Key amino acid residues (Arg40, Phe59, Asn118, Asn176, and Trp178) in DehLBHS1 were identified to play critical roles in catalysis and molecular recognition of haloalkanoic acid, essential for efficient binding and transformation of haloalkanoic acid molecules. DehLBHS1 was modeled using I-TASSER, yielding a best TM-score of 0.986 and an RMSD of 0.53 Å. Validation of the model using PROCHECK revealed that 89.2% of the residues were located in the most favored region, providing confidence in its structural accuracy. Molecular docking simulations showed that the non-simulated DehLBHS1 preferred 2,2DCP over other substrates, forming one hydrogen bond with Arg40 and exhibiting a minimum energy of -2.5 kJ/mol. The simulated DehLBHS1 exhibited a minimum energy of -4.3 kJ/mol and formed four hydrogen bonds with Arg40, Asn176, Asp9, and Tyr11, further confirming the preference for 2,2DCP. Molecular dynamics simulations supported this preference, based on various metrics, including RMSD, RMSF, gyration, hydrogen bonding, and molecular distance. MM-PBSA calculations showed that the DehLBHS1-2,2-DCP complex had a markedly lower binding energy (-21.363 ± 1.26 kcal/mol) than the DehLBHS1-3CP complex (-14.327 ± 1.738 kcal/mol). This finding has important implications for the substrate specificity and catalytic function of DehLBHS1, particularly in the bioremediation of 2,2-DCP in contaminated alkaline environments. These results provide a detailed view of the molecular interactions between the enzyme and its substrate and may aid in the development of more efficient biocatalytic strategies for the degradation of halogenated compounds.Communicated by Ramaswamy H. Sarma.

Engineering Enzymes for Environmental Sustainability

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

Engineering Enzymes for Environmental Sustainability

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

Advances in ligand-specific biosensing for structurally similar molecules

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

Evolutionary paths that link orthogonal pairs of binding proteins

Some protein binding pairs exhibit extreme specificities that functionally insulate them from homologs. Such pairs evolve mostly by accumulating single-point mutations, and mutants are selected if their affinity exceeds the threshold required for function1-4. Thus, homologous and high-specificity binding pairs bring to light an evolutionary conundrum: how does a new specificity evolve while maintaining the required affinity in each intermediate5,6? Until now, a fully functional single-mutation path that connects two orthogonal pairs has only been described where the pairs were mutationally close thus enabling experimental enumeration of all intermediates2. We present an atomistic and graph-theoretical framework for discovering low molecular strain single-mutation paths that connect two extant pairs, enabling enumeration beyond experimental capability. We apply it to two orthogonal bacterial colicin endonuclease-immunity pairs separated by 17 interface mutations7. We were not able to find a strain-free and functional path in the sequence space defined by the two extant pairs. But including mutations that bridge amino acids that cannot be exchanged through single-nucleotide mutations led us to a strain-free 19-mutation trajectory that is completely viable in vivo. Our experiments show that the specificity switch is remarkably abrupt, resulting from only one radical mutation on each partner. Furthermore, each of the critical specificity-switch mutations increases fitness, demonstrating that functional divergence could be driven by positive Darwinian selection. These results reveal how even radical functional changes in an epistatic fitness landscape may evolve.

Protein engineering in the computational age: An open source framework for exploring mutational landscapes in silico

The field of protein engineering has seen tremendous expansion in the last decade, with researchers developing novel proteins with specialised functionalities for a range of uses, from drug discovery to industrial biotechnology. The emergence of computational tools and high-throughput screening technology has substantially sped up the process of protein engineering. However, much of the expertise required to engage in such projects is still concentrated in the hands of a few specialised individuals, including computational biologists and structural biochemists. The international Genetically Engineered Machine (iGEM) competition represents a platform for undergraduate students to innovate in synthetic biology. Yet, due to their complexity, arduous protein engineering projects are hindered by the resources available and strict timelines of the competition. The authors highlight how the 2022 iGEM Team, 'Sporadicate', set out to develop InFinity 1.0, a computational framework for increased accessibility to effective protein engineering, hoping to increase awareness and accessibility to novel in silico tools.

Mutexa: A Computational Ecosystem for Intelligent Protein Engineering

Protein engineering holds immense promise in shaping the future of biomedicine and biotechnology. This Review focuses on our ongoing development of Mutexa, a computational ecosystem designed to enable "intelligent protein engineering". In this vision, researchers will seamlessly acquire sequences of protein variants with desired functions as biocatalysts, therapeutic peptides, and diagnostic proteins through a finely-tuned computational machine, akin to Amazon Alexa's role as a versatile virtual assistant. The technical foundation of Mutexa has been established through the development of a database that combines and relates enzyme structures and their respective functions (e.g., IntEnzyDB), workflow software packages that enable high-throughput protein modeling (e.g., EnzyHTP and LassoHTP), and scoring functions that map the sequence-structure-function relationship of proteins (e.g., EnzyKR and DeepLasso). We will showcase the applications of these tools in benchmarking the convergence conditions of enzyme functional descriptors across mutants, investigating protein electrostatics and cavity distributions in SAM-dependent methyltransferases, and understanding the role of nonelectrostatic dynamic effects in enzyme catalysis. Finally, we will conclude by addressing the future steps and fundamental challenges in our endeavor to develop new Mutexa applications that assist the identification of beneficial mutants in protein engineering.