Computational design of enzymes for biotechnological applications

Computational design of α-amylase from Bacillus licheniformis to increase its activity and stability at high temperatures

The thermostable α-amylase derived from Bacillus licheniformis (BLA) has multiple advantages, including enhancing the mass transfer rate and by reducing microbial contamination in starch hydrolysis. Nonetheless, the application of BLA is constrained by the accessibility and stability of enzymes capable of achieving high conversion rates at elevated temperatures. Moreover, the thermotolerance of BLA requires further enhancement. Here, we developed a computational strategy for constructing small and smart mutant libraries to identify variants with enhanced thermostability. Initially, molecular dynamics (MD) simulations were employed to identify the regions with high flexibility. Subsequently, FoldX, a computational design predictor, was used to design mutants by rigidifying highly flexible residues, whereas the simultaneous decrease in folding free energy assisted in improving thermostability. Through the utilization of MD and FoldX, residues K251, T277, N278, K319, and E336, situated at a distance of 5 Å from the catalytic triad, were chosen for mutation. Seventeen mutants were identified and characterized by evaluating enzymatic characteristics and kinetic parameters. The catalytic efficiency of the E271L/N278K mutant reached 184.1 g L-1 s-1, which is 1.88-fold larger than the corresponding value determined for the WT. Furthermore, the most thermostable mutant, E336S, exhibited a 1.43-fold improvement in half-life at 95 ℃, compared with that of the WT. This study, by combining computational simulation with experimental verification, establishes that potential sites can be computationally predicted to increase the activity and stability of BLA and thus provide a possible strategy by which to guide protein design.

Efficient and easible biocatalysts: Strategies for enzyme improvement. A review

Owing to the environmental friendliness and vast advantages that enzymes offer in the biotechnology and industry fields, biocatalysts are a prolific investigation field. However, the low catalytic activity, stability, and specific selectivity of the enzyme limit the range of the reaction enzymes involved in. A comprehensive understanding of the protein structure and dynamics in terms of molecular details enables us to tackle these limitations effectively and enhance the catalytic activity by enzyme engineering or modifying the supports and solvents. Along with different strategies including computational, enzyme engineering based on DNA recombination, enzyme immobilization, additives, chemical modification, and physicochemical modification approaches can be promising for the wide spread of industrial enzyme usage. This is attributed to the successful application of biocatalysts in industrial and synthetic processes requires a system that exhibits stability, activity, and reusability in a continuous flow process, thereby reducing the production cost. The main goal of this review is to display relevant approaches for improving enzyme characteristics to overcome their industrial application.

Advancing sustainable biotechnology through protein engineering

The push for industrial sustainability benefits from the use of enzymes as a replacement for traditional chemistry. Biological catalysts, especially those that have been engineered for increased activity, stability, or novel function, and are often greener than alternative chemical approaches. This Review highlights the role of engineered enzymes (and identifies directions for further engineering efforts) in the application areas of greenhouse gas sequestration, fuel production, bioremediation, and degradation of plastic wastes.

Noncanonical Amino Acids in Biocatalysis

In recent years, powerful genetic code reprogramming methods have emerged that allow new functional components to be embedded into proteins as noncanonical amino acid (ncAA) side chains. In this review, we will illustrate how the availability of an expanded set of amino acid building blocks has opened a wealth of new opportunities in enzymology and biocatalysis research. Genetic code reprogramming has provided new insights into enzyme mechanisms by allowing introduction of new spectroscopic probes and the targeted replacement of individual atoms or functional groups. NcAAs have also been used to develop engineered biocatalysts with improved activity, selectivity, and stability, as well as enzymes with artificial regulatory elements that are responsive to external stimuli. Perhaps most ambitiously, the combination of genetic code reprogramming and laboratory evolution has given rise to new classes of enzymes that use ncAAs as key catalytic elements. With the framework for developing ncAA-containing biocatalysts now firmly established, we are optimistic that genetic code reprogramming will become a progressively more powerful tool in the armory of enzyme designers and engineers in the coming years.

Mechanism-Guided Computational Design Drives meso-Diaminopimelate Dehydrogenase to Efficient Synthesis of Aromatic d-amino Acids

Aromatic d-amino acids (d-AAs) play a pivotal role as important chiral building blocks and key intermediates in fine chemical and drug synthesis. Meso-diaminopimelate dehydrogenase (DAPDH) serves as an excellent biocatalyst in the synthesis of d-AAs and their derivatives. However, its strict substrate specificity and the lack of efficient engineering methods have hindered its widespread application. Therefore, this study aims to elucidate the catalytic mechanism underlying DAPDH from Proteus vulgaris (PvDAPDH) through the examination of its crystallographic structure, computational simulations of potential energies and molecular dynamics simulations, and site-directed mutagenesis. Mechanism-guided computational design showed that the optimal mutant PvDAPDH-M3 increased specific activity and catalytic efficiency (kcat/Km) for aromatic keto acids up to 124-fold and 92.4-fold, respectively, compared to that of the wild type. Additionally, it expanded the substrate scope to 10 aromatic keto acid substrates. Finally, six high-value-added aromatic d-AAs and their derivatives were synthesized using a one-pot three-enzyme cascade reaction, exhibiting a good conversion rate ranging from 32 to 84% and excellent stereoselectivity (enantiomeric excess >99%). These findings provide a potential synthetic pathway for the green industrial production of aromatic d-AAs.

Advances in surface design and biomedical applications of magnetic nanoparticles

Despite significant efforts by scientists in the development of advanced nanotechnology materials for smart diagnosis devices and drug delivery systems, the success of clinical trials remains largely elusive. In order to address this biomedical challenge, magnetic nanoparticles (MNPs) have gained attention as a promising candidate due to their theranostic properties, which allow the simultaneous treatment and diagnosis of a disease. Moreover, MNPs have advantageous characteristics such as a larger surface area, high surface-to-volume ratio, enhanced mobility, mass transference and, more notably, easy manipulation under external magnetic fields. Besides, certain magnetic particle types based on the magnetite (Fe3O4) phase have already been FDA-approved, demonstrating biocompatible and low toxicity. Typically, surface modification and/or functional group conjugation are required to prevent oxidation and particle aggregation. A wide range of inorganic and organic molecules have been utilized to coat the surface of MNPs, including surfactants, antibodies, synthetic and natural polymers, silica, metals, and various other substances. Furthermore, various strategies have been developed for the synthesis and surface functionalization of MNPs to enhance their colloidal stability, biocompatibility, good response to an external magnetic field, etc. Both uncoated MNPs and those coated with inorganic and organic compounds exhibit versatility, making them suitable for a range of applications such as drug delivery systems (DDS), magnetic hyperthermia, fluorescent biological labels, biodetection and magnetic resonance imaging (MRI). Thus, this review provides an update of recently published MNPs works, providing a current discussion regarding their strategies of synthesis and surface modifications, biomedical applications, and perspectives.

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.

Automated in vivo enzyme engineering accelerates biocatalyst optimization

Achieving cost-competitive bio-based processes requires development of stable and selective biocatalysts. Their realization through in vitro enzyme characterization and engineering is mostly low throughput and labor-intensive. Therefore, strategies for increasing throughput while diminishing manual labor are gaining momentum, such as in vivo screening and evolution campaigns. Computational tools like machine learning further support enzyme engineering efforts by widening the explorable design space. Here, we propose an integrated solution to enzyme engineering challenges whereby ML-guided, automated workflows (including library generation, implementation of hypermutation systems, adapted laboratory evolution, and in vivo growth-coupled selection) could be realized to accelerate pipelines towards superior biocatalysts.

Hydrophobicity-Based Force Field In Enzymes

The biocatalysis process takes place with the participation of enzymes, which, depending on the reaction carried out, require, apart from the appropriate arrangement of catalytic residues, an appropriate external force field. It is generated by the protein body. The relatively small size of the part directly involved in the process itself is supported by the presence of an often complex structure of the protein body, the purpose of which is to provide an appropriate local force field, eliminating the influence of water. Very often, the large size of the enzyme is an expression of the complex form of this field. In this paper, a comparative analysis of arbitrarily selected enzymes, representatives of different enzyme classes, was carried out, focusing on the measurement of the diversity of the force field provided by a given protein. This analysis was based on the fuzzy oil drop model (FOD) and its modified version (FOD-M), which takes into account the participation of nonaqueous external factors in shaping the structure and thus the force field within the protein. The degree and type of ordering of the hydrophobicity distribution in the protein molecule is the result of the influence of the environment but also the supplier of the local environment for a given process, including the catalysis process in particular. Determining the share of a nonaqueous environment is important due to the ubiquity of polar water, whose participation in processes with high specificity requires control. It can be assumed that some enzymes in their composition have a permanently built-in part, the role of which is reduced to that of a permanent chaperone. It provides a specific external force field needed for the process. The proposed model, generalized to other types of proteins, may also provide a form of recording the environment model for the simulation of the in silico protein folding process, taking into account the impact of its differentiation.

Characterization of Three Types of Elongases from Different Fungi and Site-Directed Mutagenesis

Fatty acid elongases play crucial roles in synthesizing long-chain polyunsaturated fatty acids. Identifying more efficient elongases is essential for enhancing oleaginous microorganisms to produce high yields of target products. We characterized three elongases that were identified with distinct specificities: McELO from Mucor circinelloides, PrELO from Phytophthora ramorum, and PsELO from Phytophthora sojae. Heterologous expression in Saccharomyces cerevisiae showed that McELO preferentially elongates C16 to C18 fatty acids, PrELO targets Δ6 polyunsaturated fatty acids, and PsELO uses long chain saturated fatty acids as substrates. McELO and PrELO exhibited more homology, potentially enabling fatty acid composition remodeling and enhanced LC-PUFAs production in oleaginous microorganisms. Site-directed mutagenesis of conserved amino acids across elongase types identified residues essential for activity, supported by molecular docking. Alanine substitution of conserved polar residues led to enzyme inactivation, underscoring their importance in the condensation reaction. Our findings offer promising elongase candidates for polyunsaturated fatty acid production, contributing to the bioindustry's sustainable development.

Exploring the role of microbial proteins in controlling environmental pollutants based on molecular simulation

Molecular simulation has been widely used to study microbial proteins' structural composition and dynamic properties, such as volatility, flexibility, and stability at the microscopic scale. Herein, this review describes the key elements of molecular docking and molecular dynamics (MD) simulations in molecular simulation; reviews the techniques combined with molecular simulation, such as crystallography, spectroscopy, molecular biology, and machine learning, to validate simulation results and bridge information gaps in the structure, microenvironmental changes, expression mechanisms, and intensity quantification; illustrates the application of molecular simulation, in characterizing the molecular mechanisms of interaction of microbial proteins with four different types of contaminants, namely heavy metals (HMs), pesticides, dyes and emerging contaminants (ECs). Finally, the review outlines the important role of molecular simulations in the study of microbial proteins for controlling environmental contamination and provides ideas for the application of molecular simulation in screening microbial proteins and incorporating targeted mutagenesis to obtain more effective contaminant control proteins.

From De Novo Design to Redesign: Harnessing Computational Protein Design for Understanding SARS-CoV-2 Molecular Mechanisms and Developing Therapeutics

The continuous emergence of novel SARS-CoV-2 variants and subvariants serves as compelling evidence that COVID-19 is an ongoing concern. The swift, well-coordinated response to the pandemic highlights how technological advancements can accelerate the detection, monitoring, and treatment of the disease. Robust surveillance systems have been established to understand the clinical characteristics of new variants, although the unpredictable nature of these variants presents significant challenges. Some variants have shown resistance to current treatments, but innovative technologies like computational protein design (CPD) offer promising solutions and versatile therapeutics against SARS-CoV-2. Advances in computing power, coupled with open-source platforms like AlphaFold and RFdiffusion (employing deep neural network and diffusion generative models), among many others, have accelerated the design of protein therapeutics with precise structures and intended functions. CPD has played a pivotal role in developing peptide inhibitors, mini proteins, protein mimics, decoy receptors, nanobodies, monoclonal antibodies, identifying drug-resistance mutations, and even redesigning native SARS-CoV-2 proteins. Pending regulatory approval, these designed therapies hold the potential for a lasting impact on human health and sustainability. As SARS-CoV-2 continues to evolve, use of such technologies enables the ongoing development of alternative strategies, thus equipping us for the "New Normal".

Engineering and Expression Strategies for Optimization of L-Asparaginase Development and Production

Genetic engineering for heterologous expression has advanced in recent years. Model systems such as Escherichia coli, Bacillus subtilis and Pichia pastoris are often used as host microorganisms for the enzymatic production of L-asparaginase, an enzyme widely used in the clinic for the treatment of leukemia and in bakeries for the reduction of acrylamide. Newly developed recombinant L-asparaginase (L-ASNase) may have a low affinity for asparagine, reduced catalytic activity, low stability, and increased glutaminase activity or immunogenicity. Some successful commercial preparations of L-ASNase are now available. Therefore, obtaining novel L-ASNases with improved properties suitable for food or clinical applications remains a challenge. The combination of rational design and/or directed evolution and heterologous expression has been used to create enzymes with desired characteristics. Computer design, combined with other methods, could make it possible to generate mutant libraries of novel L-ASNases without costly and time-consuming efforts. In this review, we summarize the strategies and approaches for obtaining and developing L-ASNase with improved properties.

A high-performance electrochemical biosensor using an engineered urate oxidase

We constructed a high-performance biosensor for detecting uric acid by immobilizing an engineered urate oxidase on gold nanoparticles deposited on a carbon-glass electrode. This biosensor showed a low limit-of-detection (9.16 nM), a high sensitivity (14 μA/μM), a wide range of linearity (50 nM-1 mM), and more than 28 days lifetime.

Exploring Oxidoreductases from Extremophiles for Biosynthesis in a Non-Aqueous System

Organic solvent tolerant oxidoreductases are significant for both scientific research and biomanufacturing. However, it is really challenging to obtain oxidoreductases due to the shortages of natural resources and the difficulty to obtained it via protein modification. This review summarizes the recent advances in gene mining and structure-functional study of oxidoreductases from extremophiles for non-aqueous reaction systems. First, new strategies combining genome mining with bioinformatics provide new insights to the discovery and identification of novel extreme oxidoreductases. Second, analysis from the perspectives of amino acid interaction networks explain the organic solvent tolerant mechanism, which regulate the discrete structure-functional properties of extreme oxidoreductases. Third, further study by conservation and co-evolution analysis of extreme oxidoreductases provides new perspectives and strategies for designing robust enzymes for an organic media reaction system. Furthermore, the challenges and opportunities in designing biocatalysis non-aqueous systems are highlighted.

The Quantum Chemical Cluster Approach in Biocatalysis

ConspectusThe quantum chemical cluster approach has been used for modeling enzyme active sites and reaction mechanisms for more than two decades. In this methodology, a relatively small part of the enzyme around the active site is selected as a model, and quantum chemical methods, typically density functional theory, are used to calculate energies and other properties. The surrounding enzyme is modeled using implicit solvation and atom fixing techniques. Over the years, a large number of enzyme mechanisms have been solved using this method. The models have gradually become larger as a result of the faster computers, and new kinds of questions have been addressed. In this Account, we review how the cluster approach can be utilized in the field of biocatalysis. Examples from our recent work are chosen to illustrate various aspects of the methodology. The use of the cluster model to explore substrate binding is discussed first. It is emphasized that a comprehensive search is necessary in order to identify the lowest-energy binding mode(s). It is also argued that the best binding mode might not be the productive one, and the full reactions for a number of enzyme-substrate complexes have therefore to be considered to find the lowest-energy reaction pathway. Next, examples are given of how the cluster approach can help in the elucidation of detailed reaction mechanisms of biocatalytically interesting enzymes, and how this knowledge can be exploited to develop enzymes with new functions or to understand the reasons for lack of activity toward non-natural substrates. The enzymes discussed in this context are phenolic acid decarboxylase and metal-dependent decarboxylases from the amidohydrolase superfamily. Next, the application of the cluster approach in the investigation of enzymatic enantioselectivity is discussed. The reaction of strictosidine synthase is selected as a case study, where the cluster calculations could reproduce and rationalize the selectivities of both the natural and non-natural substrates. Finally, we discuss how the cluster approach can be used to guide the rational design of enzyme variants with improved activity and selectivity. Acyl transferase from Mycobacterium smegmatis serves as an instructive example here, for which the calculations could pinpoint the factors controlling the reaction specificity and enantioselectivity. The cases discussed in this Account highlight thus the value of the cluster approach as a tool in biocatalysis. It complements experiments and other computational techniques in this field and provides insights that can be used to understand existing enzymes and to develop new variants with tailored properties.

Design of Artificial Enzymes: Insights into Protein Scaffolds

The design of artificial enzymes has emerged as a promising tool for the generation of potent biocatalysts able to promote new-to-nature reactions with improved catalytic performances, providing a powerful platform for wide-ranging applications and a better understanding of protein functions and structures. The selection of an appropriate protein scaffold plays a key role in the design process. This review aims to give a general overview of the most common protein scaffolds that can be exploited for the generation of artificial enzymes. Several examples are discussed and categorized according to the strategy used for the design of the artificial biocatalyst, namely the functionalization of natural enzymes, the creation of a new catalytic site in a protein scaffold bearing a wide hydrophobic pocket and de novo protein design. The review is concluded by a comparison of these different methods and by our perspective on the topic.

[Approaches for improving L-asparaginase expression in heterologous systems]

L-asparaginase (EC 3.5.1.1) is one of the most demanded enzymes used in the pharmaceutical industry as a drug and in the food industry to prevent the formation of toxic acrylamide. Researchers aimed to improve specific activity and reduce side effects to create safer and more potent enzyme products. However, protein modifications and heterologous expression remain problematic in the production of asparaginases from different species. Heterologous expression in optimized producer strains is rationally organized; therefore, modified and heterologous protein expression is enhanced, which is the main strategy in the production of asparaginase. This strategy solves several problems: incorrect protein folding, metabolic load on the producer strain and codon misreading, which affects translation and final protein domains, leading to a decrease in catalytic activity. The main approaches developed to improve the heterologous expression of L-asparaginases are considered in this paper.

Engineering cellulases for conversion of lignocellulosic biomass

Lignocellulosic biomass is a renewable source of energy, chemicals and materials. Many applications of this resource require the depolymerization of one or more of its polymeric constituents. Efficient enzymatic depolymerization of cellulose to glucose by cellulases and accessory enzymes such as lytic polysaccharide monooxygenases is a prerequisite for economically viable exploitation of this biomass. Microbes produce a remarkably diverse range of cellulases, which consist of glycoside hydrolase (GH) catalytic domains and, although not in all cases, substrate-binding carbohydrate-binding modules (CBMs). As enzymes are a considerable cost factor, there is great interest in finding or engineering improved and robust cellulases, with higher activity and stability, easy expression, and minimal product inhibition. This review addresses relevant engineering targets for cellulases, discusses a few notable cellulase engineering studies of the past decades and provides an overview of recent work in the field.

Rational Design of a Thermostable 2'-Deoxyribosyltransferase for Nelarabine Production by Prediction of Disulfide Bond Engineering Sites

One of the major drawbacks of the industrial implementation of enzymatic processes is the low operational stability of the enzymes under tough industrial conditions. In this respect, the use of thermostable enzymes in the industry is gaining ground during the last decades. Herein, we report a structure-guided approach for the development of novel and thermostable 2′-deoxyribosyltransferases (NDTs) based on the computational design of disulfide bonds on hot spot positions. To this end, a small library of NDT variants from Lactobacillus delbrueckii (LdNDT) with introduced cysteine pairs was created. Among them, LdNDTS104C (100% retained activity) was chosen as the most thermostable variant, displaying a six- and two-fold enhanced long-term stability when stored at 55 °C (t1/255 °C ≈ 24 h) and 60 °C (t1/260 °C ≈ 4 h), respectively. Moreover, the biochemical characterization revealed that LdNDTS104C showed >60% relative activity across a broad range of temperature (30−90 °C) and pH (5−7). Finally, to study the potential application of LdNDTS104C as an industrial catalyst, the enzymatic synthesis of nelarabine was successfully carried out under different substrate conditions (1:1 and 3:1) at different reaction times. Under these experimental conditions, the production of nelarabine was increased up to 2.8-fold (72% conversion) compared with wild-type LdNDT.

NMR-guided directed evolution

Directed evolution is a powerful tool for improving existing properties and imparting completely new functionalities to proteins1-4. Nonetheless, its potential in even small proteins is inherently limited by the astronomical number of possible amino acid sequences. Sampling the complete sequence space of a 100-residue protein would require testing of 20100 combinations, which is beyond any existing experimental approach. In practice, selective modification of relatively few residues is sufficient for efficient improvement, functional enhancement and repurposing of existing proteins5. Moreover, computational methods have been developed to predict the locations and, in certain cases, identities of potentially productive mutations6-9. Importantly, all current approaches for prediction of hot spots and productive mutations rely heavily on structural information and/or bioinformatics, which is not always available for proteins of interest. Moreover, they offer a limited ability to identify beneficial mutations far from the active site, even though such changes may markedly improve the catalytic properties of an enzyme10. Machine learning methods have recently showed promise in predicting productive mutations11, but they frequently require large, high-quality training datasets, which are difficult to obtain in directed evolution experiments. Here we show that mutagenic hot spots in enzymes can be identified using NMR spectroscopy. In a proof-of-concept study, we converted myoglobin, a non-enzymatic oxygen storage protein, into a highly efficient Kemp eliminase using only three mutations. The observed levels of catalytic efficiency exceed those of proteins designed using current approaches and are similar with those of natural enzymes for the reactions that they are evolved to catalyse. Given the simplicity of this experimental approach, which requires no a priori structural or bioinformatic knowledge, we expect it to be widely applicable and to enable the full potential of directed enzyme evolution.

Advances in lytic polysaccharide monooxygenases with the cellulose-degrading auxiliary activity family 9 to facilitate cellulose degradation for biorefinery

One crucial step in processing the recalcitrant lignocellulosic biomass is the fast hydrolysis of natural cellulose to fermentable sugars that can be subsequently converted to biofuels and bio-based chemicals. Recent studies have shown that lytic polysaccharide monooxygenase (LPMOs) with auxiliary activity family 9 (AA9) are capable of efficiently depolymerizing the crystalline cellulose via regioselective oxidation reaction. Intriguingly, the catalysis by AA9 LPMOs requires reductant to provide electrons, and lignin and its phenolic derivatives can be oxidized, releasing reductant to activate the reaction. The activity of AA9 LPMOs can be enhanced by in-situ generation of H₂O₂ in the presence of O₂. Although scientific understanding of these enzymes remains somewhat unknown or controversial, structure modifications on AA9 LPMOs through protein engineering have emerged in recent years, which are prerequisite for their extensive applications in the development of cellulase-mediated lignocellulosic biorefinery processes. In this review, we critically comment on advances in studies for AA9 LPMOs, i.e., characteristic of AA9 LPMOs catalysis, external electron donors to AA9 LPMOs, especially the role of the oxidization of lignin and its derivatives, and AA9 LPMOs protein engineering as well as their extensive applications in the bioprocessing of lignocellulosic biomass. Perspectives are also highlighted for addressing the challenges.

Structure-based rational design of hydroxysteroid dehydrogenases for improving and diversifying steroid synthesis

A group of steroidogenic enzymes, hydroxysteroid dehydrogenases are involved in steroid metabolism which is very important in the cell: signaling, growth, reproduction, and energy homeostasis. The enzymes show an inherent function in the interconversion of ketosteroids and hydroxysteroids in a position- and stereospecific manner on the steroid nucleus and side-chains. However, the biocatalysis of steroids reaction is a vital and demanding, yet challenging, task to produce the desired enantiopure products with non-natural substrates or non-natural cofactors, and/or in non-physiological conditions. This has driven the use of protein design strategies to improve their inherent biosynthetic efficiency or activate their silent catalytic ability. In this review, the innate features and catalytic characteristics of enzymes based on sequence-structure-function relationships of steroidogenic enzymes are reviewed. Combining structure information and catalytic mechanisms, progress in protein redesign to stimulate potential function, for example, substrate specificity, cofactor dependence, and catalytic stability are discussed.

Mechanism and Application of Surface-Charged Ferrite Nanozyme-Based Biosensor toward Colorimetric Detection of l-Cysteine

Peroxidase-like nanozymes with robust catalytic capacity and detection specificity have been proposed as substitutes to natural peroxidases in biochemical sensing. However, the catalytic activity enhancement, detection mechanism, and application of nanozyme-based biosensors toward l-cysteine (l-Cys) detection still remain significant challenges. In this work, a doped ferrite nanozyme with well-defined structure and surface charges is fabricated by a two-step method of continuous flow coprecipitation and high-temperature annealing. The resulted ferrite nanozyme possesses an average size of 54.5 nm and a zeta-potential of 6.45 mV. A high-performance biosensor is manufactured based on the peroxidase-like catalytic feature of the doped ferrite. The ferrite nanozyme can oxidize the 3,3',5,5'-tetramethylbenzidine (TMB) with the assistance of H₂O₂ because of the instinctive capacity to decompose H₂O₂ into ·OH. The Michaelis-Menten constants (0.0911 mM for TMB, 0.140 mM for H₂O₂) of the ferrite nanozyme are significantly smaller than those of horseradish peroxidase. A reliable colorimetric method is established to selectively analyze l-Cys via a facile mixing-and-detecting methodology. The detection limit and linear range are 0.119 μM and 0.2-20 μM, respectively. Taking the merits of the ferrite nanozyme-based biosensors, the l-Cys level in the human serum can be qualitatively detected. It can be anticipated that the surface-charged ferrite nanozyme shows great application prospects in the fields of bioanalytical chemistry and point-of-care testing.

Engineering and screening of novel β-1,3-xylanases with desired hydrolysate type by optimized ancestor sequence reconstruction and data mining

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A novel integrative strategy for engineering β-1,3-xylanases with desired products.

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AncXyl10 is the first successful example of ASR to shift the hydrolysate types.

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The hydrolysates of AncXyl10 was only β-1,3-xylobiose and β-1,3-xylotriose.

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The underlying mechanism laid a new groundwork towards hydrolase engineering.

Engineering of hydrolases to shift their hydrolysate types has not been attempted so far, though computer-assisted enzyme design has been successful. A novel integrative strategy for engineering and screening the β-1,3-xylanase with desired hydrolysate types was proposed, with the purpose to solve problems that the separation and preparation of β-1,3-xylo-oligosaccharides was in high cost yet in low yield as monosaccharides existed in the hydrolysates. By classifying the hydrolysate types and coding them into numerical values, two robust mathematical models with five selected attributes from molecular docking were established based on LogitBoost and partial least squares regression with overall accuracy of 83.3% and 100%, respectively. Then, they were adopted for efficient screening the potential mutagenesis library of β-1,3-xylanases that only product oligosaccharides. The virtually designed AncXyl10 was selected and experimentally verified to produce only β-1,3-xylobiose (60.38%) and β-1,3-xylotriose (39.62%), which facilitated the preparation of oligosaccharides with high purity. The underlying mechanism of AncXyl10 may associated with the gap processing and ancestral amino acid substitution in the process of ancestral sequence reconstruction. Since many carbohydrate-active enzymes have highly conserved active sites, the strategy and their biomolecular basis will shield a new light for engineering carbohydrates hydrolase to produce specific oligosaccharides.

Computational tools and resources for designing new pathways to small molecules

The metabolic engineering community relies on computational methods for pathway design to produce important small molecules in microbial hosts. Metabolic network databases are continuously curated and updated with known and novel reactions that expand the known biochemistry based on different sets of enzymatic reaction rules. To address the complexity of the metabolic networks, elaborate methods were developed to transform them into computable graphs, navigate them, and construct the best possible pathways. However, the recent experimental research points to the new challenges and opportunities for the computational pathway design. Here, we review the most recent advances, especially in the last two years, in computational discovery of new pathways and their prospects for expanding metabolic capabilities. We draw attention to the potential ways of improvement for pathway design algorithms, including the expansion of Design-Build-Test-Learn cycle to novel compounds and reactions and the standardization for the reaction rules and metabolic reaction databases.

Tools for computational design and high-throughput screening of therapeutic enzymes

Therapeutic enzymes are valuable biopharmaceuticals in various biomedical applications. They have been successfully applied for fibrinolysis, cancer treatment, enzyme replacement therapies, and the treatment of rare diseases. Still, there is a permanent demand to find new or better therapeutic enzymes, which would be sufficiently soluble, stable, and active to meet specific medical needs. Here, we highlight the benefits of coupling computational approaches with high-throughput experimental technologies, which significantly accelerate the identification and engineering of catalytic therapeutic agents. New enzymes can be identified in genomic and metagenomic databases, which grow thanks to next-generation sequencing technologies exponentially. Computational design and machine learning methods are being developed to improve catalytically potent enzymes and predict their properties to guide the selection of target enzymes. High-throughput experimental pipelines, increasingly relying on microfluidics, ensure functional screening and biochemical characterization of target enzymes to reach efficient therapeutic enzymes.

Atomistic Simulations for Reactions and Vibrational Spectroscopy in the Era of Machine Learning─Quo Vadis?

Atomistic simulations using accurate energy functions can provide molecular-level insight into functional motions of molecules in the gas and in the condensed phase. This Perspective delineates the present status of the field from the efforts of others and some of our own work and discusses open questions and future prospects. The combination of physics-based long-range representations using multipolar charge distributions and kernel representations for the bonded interactions is shown to provide realistic models for the exploration of the infrared spectroscopy of molecules in solution. For reactions, empirical models connecting dedicated energy functions for the reactant and product states allow statistically meaningful sampling of conformational space whereas machine-learned energy functions are superior in accuracy. The future combination of physics-based models with machine-learning techniques and integration into all-purpose molecular simulation software provides a unique opportunity to bring such dynamics simulations closer to reality.

De novo biosynthesis of diverse plant-derived styrylpyrones in Saccharomyces cerevisiae

Plant styrylpyrones exerting well-established neuroprotective properties have attracted increasing attention in recent years. The ability to synthesize each individual styrylpyrone in engineered microorganisms is important to understanding the biological activity of medicinal plants and the complex mixtures they produce. Microbial biomanufacturing of diverse plant-derived styrylpyrones also provides a sustainable and efficient approach for the production of valuable plant styrylpyrones as daily supplements or potential drugs complementary to the prevalent agriculture-based approach. In this study, we firstly demonstrated the heterogenous biosynthesis of two 7,8-saturated styrylpyrones (7,8-dihydro-5,6-dehydrokavain (DDK) and 7,8-dihydroyangonin (DHY)) and two 7,8-unsaturated styrylpyrones (desmethoxyyangonin (DMY) and yangonin (Y)), in Saccharomyces cerevisiae. Although plant styrylpyrone biosynthetic pathways have not been fully elucidated, we functionally reconstructed the recently discovered kava styrylpyrone biosynthetic pathway that has high substrate promiscuity in yeast, and combined it with upstream hydroxycinnamic acid biosynthetic pathways to produce diverse plant-derived styrylpyrones without the native plant enzymes. We optimized the de novo pathways by engineering yeast endogenous aromatic amino acid metabolism and endogenous double bond reductases and by CRISPR-mediated δ-integration to overexpress the rate-limiting pathway genes. These combinatorial engineering efforts led to the first three yeast strains that can produce diverse plant-derived styrylpyrones de novo, with the titers of DDK, DMY and Y at 4.40 μM, 1.28 μM and 0.10 μM, respectively. This work has laid the foundation for larger-scale styrylpyrone biomanufacturing and the complete biosynthesis of more complicated plant styrylpyrones.

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Complete biosynthesis of plant styrylpyrones was firstly achieved in yeast.

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Yeast enzyme replaces unknown plant enzymes to produce 7,8-saturated styrylpyrones.

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CRISPR-based δ-integration led to stable styrylpyrone overproduction in rich medium.

Gaussian network model revisited: Effects of mutation and ligand binding on protein behavior

The coarse-grained Gaussian Network model, GNM, considers only the alpha carbons of the folded protein. Therefore it is not directly applicable to the study of mutation or ligand binding problems where atomic detail is required. This shortcoming is improved by including all atom pairs within the coordination shell of each other into the Kirchoff Adjacency Matrix. Counting all contacts rather than only alpha carbon contacts diminishes the magnitude of fluctuations in the system. But more importantly, it changes the graph-like connectivity structure, i.e., the Kirchoff Adjacency Matrix of the protein. This change depends on amino acid type which introduces amino acid specific and position specific information into the classical coarse-grained GNM which was originally modelled in analogy with the phantom network model of rubber elasticity. With this modification, it is now possible to explain the consequences of mutation and ligand binding on residue fluctuations, their pair-correlations and mutual information (MI) shared by each pair. We refer to the new model as 'all-atom GNM'. Using examples from published data we show that the all-atom GNM gives B-factors that are in better agreement with experiment, can explain effects of mutation on long range communication in PDZ domains and can predict effects of GDP and GTP binding on the dimerization of KRAS.

Study of self-interaction-errors in barrier heights using locally scaled and Perdew-Zunger self-interaction methods

We study the effect of self-interaction errors on the barrier heights of chemical reactions. For this purpose, we use the well-known Perdew-Zunger self-interaction-correction (PZSIC) [J. P. Perdew and A. Zunger, Phys. Rev. B 23, 5048 (1981)] as well as two variations of the recently developed, locally scaled self-interaction correction (LSIC) [Zope et al., J. Chem. Phys. 151, 214108 (2019)] to study the barrier heights of the BH76 benchmark dataset. Our results show that both PZSIC and especially the LSIC methods improve the barrier heights relative to the local density approximation (LDA). The version of LSIC that uses the iso-orbital indicator z as a scaling factor gives a more consistent improvement than an alternative version that uses an orbital-dependent factor w based on the ratio of orbital densities to the total electron density. We show that LDA energies evaluated using the self-consistent and self-interaction-free PZSIC densities can be used to assess density-driven errors. The LDA reaction barrier errors for the BH76 set are found to contain significant density-driven errors for all types of reactions contained in the set, but the corrections due to adding SIC to the functional are much larger than those stemming from the density for the hydrogen transfer reactions and of roughly equal size for the non-hydrogen transfer reactions.

Learning Strategies in Protein Directed Evolution

Synthetic biology is a fast-evolving research field that combines biology and engineering principles to develop new biological systems for medical, pharmacological, and industrial applications. Synthetic biologists use iterative "design, build, test, and learn" cycles to efficiently engineer genetic systems that are reliable, reproducible, and predictable. Protein engineering by directed evolution can benefit from such a systematic engineering approach for various reasons. Learning can be carried out before starting, throughout or after finalizing a directed evolution project. Computational tools, bioinformatics, and scanning mutagenesis methods can be excellent starting points, while molecular dynamics simulations and other strategies can guide engineering efforts. Similarly, studying protein intermediates along evolutionary pathways offers fascinating insights into the molecular mechanisms shaped by evolution. The learning step of the cycle is not only crucial for proteins or enzymes that are not suitable for high-throughput screening or selection systems, but it is also valuable for any platform that can generate a large amount of data that can be aided by machine learning algorithms. The main challenge in protein engineering is to predict the effect of a single mutation on one functional parameter-to say nothing of several mutations on multiple parameters. This is largely due to nonadditive mutational interactions, known as epistatic effects-beneficial mutations present in a genetic background may not be beneficial in another genetic background. In this work, we provide an overview of experimental and computational strategies that can guide the user to learn protein function at different stages in a directed evolution project. We also discuss how epistatic effects can influence the success of directed evolution projects. Since machine learning is gaining momentum in protein engineering and the field is becoming more interdisciplinary thanks to collaboration between mathematicians, computational scientists, engineers, molecular biologists, and chemists, we provide a general workflow that familiarizes nonexperts with the basic concepts, dataset requirements, learning approaches, model capabilities and performance metrics of this intriguing area. Finally, we also provide some practical recommendations on how machine learning can harness epistatic effects for engineering proteins in an "outside-the-box" way.

Perspectives on the Role of Enzymatic Biocatalysis for the Degradation of Plastic PET

Plastics are highly durable and widely used materials. Current methodologies of plastic degradation, elimination, and recycling are flawed. In recent years, biodegradation (the usage of microorganisms for material recycling) has grown as a valid alternative to previously used methods. The evolution of bioengineering techniques and the discovery of novel microorganisms and enzymes with degradation ability have been key. One of the most produced plastics is PET, a long chain polymer of terephthalic acid (TPA) and ethylene glycol (EG) repeating monomers. Many enzymes with PET degradation activity have been discovered, characterized, and engineered in the last few years. However, classification and integrated knowledge of these enzymes are not trivial. Therefore, in this work we present a summary of currently known PET degrading enzymes, focusing on their structural and activity characteristics, and summarizing engineering efforts to improve activity. Although several high potential enzymes have been discovered, further efforts to improve activity and thermal stability are necessary.

Pollutant Degrading Enzyme: Catalytic Mechanisms and Their Expanded Applications

The treatment of environmental pollution by microorganisms and their enzymes is an innovative and socially acceptable alternative to traditional remediation approaches. Microbial biodegradation is often characterized with high efficiency as this process is catalyzed via degrading enzymes. Various naturally isolated microorganisms were demonstrated to have considerable ability to mitigate many environmental pollutants without external intervention. However, only a small fraction of these strains are studied in detail to reveal the mechanisms at the enzyme level, which strictly limited the enhancement of the degradation efficiency. Accordingly, this review will comprehensively summarize the function of various degrading enzymes with an emphasis on catalytic mechanisms. We also inspect the expanded applications of these pollutant-degrading enzymes in industrial processes. An in-depth understanding of the catalytic mechanism of enzymes will be beneficial for exploring and exploiting more degrading enzyme resources and thus ameliorate concerns associated with the ineffective biodegradation of recalcitrant and xenobiotic contaminants with the help of gene-editing technology and synthetic biology.

Bacterial α-Glucan and Branching Sucrases from GH70 Family: Discovery, Structure-Function Relationship Studies and Engineering

Glucansucrases and branching sucrases are classified in the family 70 of glycoside hydrolases. They are produced by lactic acid bacteria occupying very diverse ecological niches (soil, buccal cavity, sourdough, intestine, dairy products, etc.). Usually secreted by their producer organisms, they are involved in the synthesis of α-glucans from sucrose substrate. They contribute to cell protection while promoting adhesion and colonization of different biotopes. Dextran, an α-1,6 linked linear α-glucan, was the first microbial polysaccharide commercialized for medical applications. Advances in the discovery and characterization of these enzymes have remarkably enriched the available diversity with new catalysts. Research into their molecular mechanisms has highlighted important features governing their peculiarities thus opening up many opportunities for engineering these catalysts to provide new routes for the transformation of sucrose into value-added molecules. This article reviews these different aspects with the ambition to show how they constitute the basis for promising future developments.

Prospects of Using Biocatalysis for the Synthesis and Modification of Polymers

Trends in the dynamically developing application of biocatalysis for the synthesis and modification of polymers over the past 5 years are considered, with an emphasis on the production of biodegradable, biocompatible and functional polymeric materials oriented to medical applications. The possibilities of using enzymes not only as catalysts for polymerization but also for the preparation of monomers for polymerization or oligomers for block copolymerization are considered. Special attention is paid to the prospects and existing limitations of biocatalytic production of new synthetic biopolymers based on natural compounds and monomers from biomass, which can lead to a huge variety of functional biomaterials. The existing experience and perspectives for the integration of bio- and chemocatalysis in this area are discussed.

Design and optimizing a new CDP-choline in vitro multienzyme producing process starts from d-ribose

This work designs an in vitro multienzyme system to produce CDP-choline from d-ribose and develop an optimization procedure for one-pot multienzyme catalytic system. The entire process integrated 10 enzymes, and an efficient acetate kinase/acetyl phosphate-based ATP regeneration module was applied. Then, some optimizations to this system were made including selecting optimum enzyme building blocks and improving expression parameters. The process improved the final yield of CDP-choline from 0.2 to 6 g/L (CDP-choline titer 12.2 mM). This new one-pot CDP-choline producing system has a potential for industrial use, and the optimization procedure shed light on improving other one-pot multienzyme system for industrial production of energy rich compounds.

Web-based tools for computational enzyme design

Enzymes are in high demand for very diverse biotechnological applications. However, natural biocatalysts often need to be engineered for fine-tuning their properties towards the end applications, such as the activity, selectivity, stability to temperature or co-solvents, and solubility. Computational methods are increasingly used in this task, providing predictions that narrow down the space of possible mutations significantly and can enormously reduce the experimental burden. Many computational tools are available as web-based platforms, making them accessible to non-expert users. These platforms are typically user-friendly, contain walk-throughs, and do not require deep expertise and installations. Here we describe some of the most recent outstanding web-tools for enzyme engineering and formulate future perspectives in this field.