Design of Efficient Artificial Enzymes Using Crystallographically Enhanced Conformational Sampling.

The ability to create efficient artificial enzymes for any chemical reaction is of great interest. Here, we describe a computational design method for increasing the catalytic efficiency of de novo enzymes by several orders of magnitude without relying on directed evolution and high-throughput screening. Using structural ensembles generated from dynamics-based refinement against X-ray diffraction data collected from crystals of Kemp eliminases HG3 (kcat/KM 125 M-1 s-1) and KE70 (kcat/KM 57 M-1 s-1), we design from each enzyme ≤10 sequences predicted to catalyze this reaction more efficiently. The most active designs display kcat/KM values improved by 100-250-fold, comparable to mutants obtained after screening thousands of variants in multiple rounds of directed evolution. Crystal structures show excellent agreement with computational models, with catalytic contacts present as designed and transition-state root-mean-square deviations of ≤0.65 Å. Our work shows how ensemble-based design can generate efficient artificial enzymes by exploiting the true conformational ensemble to design improved active sites.

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.

The unreasonable effectiveness of the total quasi-steady state approximation, and its limitations.

In this note, we discuss the range of parameters for which the total quasi-steady-state approximation of the Michaelis-Menten reaction mechanism holds validity. We challenge the prevalent notion that total quasi-steady-state approximation is "roughly valid" across all parameters, showing that its validity cannot be assumed, even roughly, across the entire parameter space - when the initial substrate concentration is high. On the positive side, we show that the linearized one-dimensional equation for total substrate is a valid approximation when the initial reduced substrate concentration s0/KM is small. Moreover, we obtain a precise picture of the long-term time course of both substrate and complex.

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.

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.

Ultrahigh-Throughput Directed Evolution of Polymer-Degrading Enzymes Using Yeast Display.

Enzymes that degrade synthetic polymers have attracted intense interest for eco-friendly plastic recycling. However, because enzymes did not evolve for the cleavage of abiotic polymers, directed evolution strategies are needed to enhance activity for plastic degradation. Previous directed evolution efforts relied on polymer degradation assays that were limited to screening ∼104 mutants. Here, we report a high-throughput yeast surface display platform to rapidly evaluate >107 enzyme mutants for increased activity in cleaving synthetic polymers. In this platform, individual yeast cells display distinct mutants, and enzyme activity is detected by a change in fluorescence upon the cleavage of a synthetic probe resembling a polymer of interest. Highly active mutants are isolated by fluorescence activated cell sorting and identified through DNA sequencing. To demonstrate this platform, we performed directed evolution of a polyethylene terephthalate (PET)-depolymerizing enzyme, leaf and branch compost cutinase (LCC). We identified activity-boosting mutations that substantially increased the kinetics of degradation of solid PET films. Biochemical assays and molecular dynamics (MD) simulations of the most active variants suggest that the H218Y mutation improves the binding of the enzyme to PET. Overall, this evolution platform increases the screening throughput of polymer-degrading enzymes by 3 orders of magnitude and identifies mutations that enhance kinetics for depolymerizing solid substrates.

Generalized Michaelis-Menten rate law with time-varying molecular concentrations.

The Michaelis-Menten (MM) rate law has been the dominant paradigm of modeling biochemical rate processes for over a century with applications in biochemistry, biophysics, cell biology, systems biology, and chemical engineering. The MM rate law and its remedied form stand on the assumption that the concentration of the complex of interacting molecules, at each moment, approaches an equilibrium (quasi-steady state) much faster than the molecular concentrations change. Yet, this assumption is not always justified. Here, we relax this quasi-steady state requirement and propose the generalized MM rate law for the interactions of molecules with active concentration changes over time. Our approach for time-varying molecular concentrations, termed the effective time-delay scheme (ETS), is based on rigorously estimated time-delay effects in molecular complex formation. With particularly marked improvements in protein-protein and protein-DNA interaction modeling, the ETS provides an analytical framework to interpret and predict rich transient or rhythmic dynamics (such as autogenously-regulated cellular adaptation and circadian protein turnover), which goes beyond the quasi-steady state assumption.

B12-dependent radical SAM enzymes: Ever expanding structural and mechanistic diversity.

In the last decade, B12-dependent radical SAM enzymes have emerged as central biocatalysts in the biosynthesis of a myriad of natural products. Notably, these enzymes have been shown to catalyze carbon-carbon bond formation on unactivated carbon atoms leading to unusual methylations. Recently, structural studies have revealed unprecedented insights into the complex chemistry catalyzed by these enzymes. In this review, we cover recent advances in our understanding of B12-dependent radical SAM enzymes from a mechanistic and structural perspective. We discuss the unanticipated diversity of these enzymes which suggests evolutionary links between various biosynthetic and metabolic pathways from antibiotic to RiPP and methane biosynthesis.

Regulatory mechanisms of one-carbon metabolism enzymes.

One-carbon metabolism is a central metabolic pathway critical for the biosynthesis of several amino acids, methyl group donors, and nucleotides. The pathway mostly relies on the transfer of a carbon unit from the amino acid serine, through the cofactor folate (in its several forms), and to the ultimate carbon acceptors that include nucleotides and methyl groups used for methylation of proteins, RNA, and DNA. Nucleotides are required for DNA replication, DNA repair, gene expression, and protein translation, through ribosomal RNA. Therefore, the one-carbon metabolism pathway is essential for cell growth and function in all cells, but is specifically important for rapidly proliferating cells. The regulation of one-carbon metabolism is a critical aspect of the normal and pathological function of the pathway, such as in cancer, where hijacking these regulatory mechanisms feeds an increased need for nucleotides. One-carbon metabolism is regulated at several levels: via gene expression, posttranslational modification, subcellular compartmentalization, allosteric inhibition, and feedback regulation. In this review, we aim to inform the readers of relevant one-carbon metabolism regulation mechanisms and to bring forward the need to further study this aspect of one-carbon metabolism. The review aims to integrate two major aspects of cancer metabolism-signaling downstream of nutrient sensing and one-carbon metabolism, because while each of these is critical for the proliferation of cancerous cells, their integration is critical for comprehensive understating of cellular metabolism in transformed cells and can lead to clinically relevant insights.

Enzyme function and evolution through the lens of bioinformatics.

Enzymes have been shaped by evolution over billions of years to catalyse the chemical reactions that support life on earth. Dispersed in the literature, or organised in online databases, knowledge about enzymes can be structured in distinct dimensions, either related to their quality as biological macromolecules, such as their sequence and structure, or related to their chemical functions, such as the catalytic site, kinetics, mechanism, and overall reaction. The evolution of enzymes can only be understood when each of these dimensions is considered. In addition, many of the properties of enzymes only make sense in the light of evolution. We start this review by outlining the main paradigms of enzyme evolution, including gene duplication and divergence, convergent evolution, and evolution by recombination of domains. In the second part, we overview the current collective knowledge about enzymes, as organised by different types of data and collected in several databases. We also highlight some increasingly powerful computational tools that can be used to close gaps in understanding, in particular for types of data that require laborious experimental protocols. We believe that recent advances in protein structure prediction will be a powerful catalyst for the prediction of binding, mechanism, and ultimately, chemical reactions. A comprehensive mapping of enzyme function and evolution may be attainable in the near future.

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.

Endophytic bacteria isolated from Urtica dioica L.- preliminary screening for enzyme and polyphenols production.

Endophytes, especially those isolated from herbal plants, may act as a reservoir of a variety of secondary metabolites exhibiting biological activity. Some endophytes express the ability to produce the same bioactive compounds as their plant hosts, making them a more sustainable industrial supply of these substances. Urtica dioica L. (common stinging nettle) is a synanthropic plant that is widely used in herbal medicine due to the diversity of bioactive chemicals it contains, e.g., polyphenols, which demonstrate anti-inflammatory, antioxidant, and anti-cancerous capabilities. This study aimed at isolating endophytic bacteria from stinging nettles for their bioactive compounds. The endophytic isolates were identified by both biochemical and molecular methods (16S rRNA) and investigated for enzymes, biosurfactants, and polyphenols production. Each of the isolated bacterial strains was capable of producing biosurfactants and polyphenols. However, three of the isolated endophytes, identified as two strains of Bacillus cereus and one strain of Bacillus mycoides, possessed the greatest capacity to produce biosurfactants and polyphenols. The derivatized extracts from culture liquid showed the 1.633 mol l-1 (9.691 mg l-1) concentration of polyphenol compounds. Therefore, the present study signifies that endophytic B. cereus and B. mycoides isolated from Urtica dioica L. could be a potential source of biosurfactants and polyphenols. However, further study is required to understand the mechanism of the process and achieve efficient polyphenol production by endophytic bacteria.

Acid-resistant enzymes: the acquisition strategies and applications.

Enzymes have promising applications in chemicals, food, pharmaceuticals, and other variety products because of their high efficiency, specificity, and environmentally friendly properties. However, due to the complexity of raw materials, pH, temperature, solvents, etc., the application range of enzymes is greatly limited in the industry. Protein engineering and enzyme immobilization are classical strategies to overcome the limitations of industrial applications. Although the pH tendency of enzymes has been extensively researched, the mechanism underlying enzyme acid resistance is unclear, and a less practical strategy for altering the pH propensity of enzymes has been suggested. This review proposes that the optimum pH of enzyme is determined by the pKa values of active center ionizable amino acid residues. Three levels of acquiring acid-resistant enzymes are summarized: mining from extreme environments and enzyme databases, modification with protein engineering and enzyme microenvironment engineering, and de novo synthesis. The industrial applications of acid-resistant enzymes in chemicals, food, and pharmaceuticals are also summarized. KEY POINTS: • The mechanism of enzyme acid resistance is fundamentally determined. • The three aspects of the method for acquiring acid-resistant enzymes are summarized. • Computer-aided strategies and artificial intelligence are used to obtain acid-resistant enzymes.

Engineering of Therapeutic and Detoxifying Enzymes.

Therapeutic enzymes present excellent opportunities for the treatment of human disease, modulation of metabolic pathways and system detoxification. However, current use of enzyme therapy in the clinic is limited as naturally occurring enzymes are seldom optimal for such applications and require substantial improvement by protein engineering. Engineering strategies such as design and directed evolution that have been successfully implemented for industrial biocatalysis can significantly advance the field of therapeutic enzymes, leading to biocatalysts with new-to-nature therapeutic activities, high selectivity, and suitability for medical applications. This minireview highlights case studies of how state-of-the-art and emerging methods in protein engineering are explored for the generation of therapeutic enzymes and discusses gaps and future opportunities in the field of enzyme therapy.

Control analysis in the identification of key enzymes driving metabolic adaptations: Towards drug target discovery.

Metabolic Control Analysis (MCA) marked a turning point in understanding the design principles of metabolic network control by establishing control coefficients as a means to quantify the degree of control that an enzyme exerts on flux or metabolite concentrations. MCA has demonstrated that control of metabolic pathways is distributed among many enzymes rather than depending on a single rate-limiting step. MCA also proved that this distribution depends not only on the stoichiometric structure of the network but also on other kinetic determinants, such as the degree of saturation of the enzyme active site, the distance to thermodynamic equilibrium, and metabolite feedback regulatory loops. Consequently, predicting the alterations that occur during metabolic adaptation in response to strong changes involving a redistribution in such control distribution can be challenging. Here, using the framework provided by MCA, we illustrate how control distribution in a metabolic pathway/network depends on enzyme kinetic determinants and to what extent the redistribution of control affects our predictions on candidate enzymes suitable as targets for small molecule inhibition in the drug discovery process. Our results uncover that kinetic determinants can lead to unexpected control distribution and outcomes that cannot be predicted solely from stoichiometric determinants. We also unveil that the inference of key enzyme-drivers of an observed metabolic adaptation can be dramatically improved using mean control coefficients and ruling out those enzyme activities that are associated with low control coefficients. As the use of constraint-based stoichiometric genome-scale metabolic models (GSMMs) becomes increasingly prevalent for identifying genes/enzymes that could be potential drug targets, we anticipate that incorporating kinetic determinants and ruling out enzymes with low control coefficients into GSMM workflows will facilitate more accurate predictions and reveal novel therapeutic targets.

Bioengineered Enzymes and Precision Fermentation in the Food Industry.

Enzymes have been used in the food processing industry for many years. However, the use of native enzymes is not conducive to high activity, efficiency, range of substrates, and adaptability to harsh food processing conditions. The advent of enzyme engineering approaches such as rational design, directed evolution, and semi-rational design provided much-needed impetus for tailor-made enzymes with improved or novel catalytic properties. Production of designer enzymes became further refined with the emergence of synthetic biology and gene editing techniques and a plethora of other tools such as artificial intelligence, and computational and bioinformatics analyses which have paved the way for what is referred to as precision fermentation for the production of these designer enzymes more efficiently. With all the technologies available, the bottleneck is now in the scale-up production of these enzymes. There is generally a lack of accessibility thereof of large-scale capabilities and know-how. This review is aimed at highlighting these various enzyme-engineering strategies and the associated scale-up challenges, including safety concerns surrounding genetically modified microorganisms and the use of cell-free systems to circumvent this issue. The use of solid-state fermentation (SSF) is also addressed as a potentially low-cost production system, amenable to customization and employing inexpensive feedstocks as substrate.

Rank-ordering of known enzymes as starting points for re-engineering novel substrate activity using a convolutional neural network.

Retro-biosynthetic approaches have made significant advances in predicting synthesis routes of target biofuel, bio-renewable or bio-active molecules. The use of only cataloged enzymatic activities limits the discovery of new production routes. Recent retro-biosynthetic algorithms increasingly use novel conversions that require altering the substrate or cofactor specificities of existing enzymes while connecting pathways leading to a target metabolite. However, identifying and re-engineering enzymes for desired novel conversions are currently the bottlenecks in implementing such designed pathways. Herein, we present EnzRank, a convolutional neural network (CNN) based approach, to rank-order existing enzymes in terms of their suitability to undergo successful protein engineering through directed evolution or de novo design towards a desired specific substrate activity. We train the CNN model on 11,800 known active enzyme-substrate pairs from the BRENDA database as positive samples and data generated by scrambling these pairs as negative samples using substrate dissimilarity between an enzyme's native substrate and all other molecules present in the dataset using Tanimoto similarity score. EnzRank achieves an average recovery rate of 80.72% and 73.08% for positive and negative pairs on test data after using a 10-fold holdout method for training and cross-validation. We further developed a web-based user interface (available at https://huggingface.co/spaces/vuu10/EnzRank) to predict enzyme-substrate activity using SMILES strings of substrates and enzyme sequence as input to allow convenient and easy-to-use access to EnzRank. In summary, this effort can aid de novo pathway design tools to prioritize starting enzyme re-engineering candidates for novel reactions as well as in predicting the potential secondary activity of enzymes in cell metabolism.

Curse and Blessing of Non-Proteinogenic Parts in Computational Enzyme Engineering.

Enzyme engineering aims to improve or install a new function in biocatalysts for applications ranging from chemical synthesis to biomedicine. For decades, computational techniques have been developed to predict the effect of protein changes and design new enzymes. However, these techniques may have been optimized to deal with proteins composed of the standard amino acid alphabet, while the function of many enzymes relies on non-proteogenic parts like cofactors, nucleic acids, and post-translational modifications. Enzyme systems containing such molecules might be handled or modeled improperly by computational tools, and thus be unsuitable, or require additional tweaking, parameterization, or preparation. In this review, we give an overview of common and recent tools and workflows available to computational enzyme engineers. We highlight the various pitfalls that come with including non-proteogenic compounds in computations and outline potential ways to address common issues. Finally, we showcase successful examples from the literature that computationally engineered such enzymes.

Optimal enzyme utilization suggests that concentrations and thermodynamics determine binding mechanisms and enzyme saturations.

Deciphering the metabolic functions of organisms requires understanding the dynamic responses of living cells upon genetic and environmental perturbations, which in turn can be inferred from enzymatic activity. In this work, we investigate the optimal modes of operation for enzymes in terms of the evolutionary pressure driving them toward increased catalytic efficiency. We develop a framework using a mixed-integer formulation to assess the distribution of thermodynamic forces and enzyme states, providing detailed insights into the enzymatic mode of operation. We use this framework to explore Michaelis-Menten and random-ordered multi-substrate mechanisms. We show that optimal enzyme utilization is achieved by unique or alternative operating modes dependent on reactant concentrations. We find that in a bimolecular enzyme reaction, the random mechanism is optimal over any other ordered mechanism under physiological conditions. Our framework can investigate the optimal catalytic properties of complex enzyme mechanisms. It can further guide the directed evolution of enzymes and fill in the knowledge gaps in enzyme kinetics.