Tuning the activity of an enzyme for unusual environments: sequential random mutagenesis of subtilisin E for catalysis in dimethylformamide

Multi-omics analysis reveals promiscuous O-glycosyltransferases involved in the diversity of flavonoid glycosides in Periploca forrestii (Apocynaceae)

Flavonoid glycosides are widespread in plants, and are of great interest owing to their diverse biological activities and effectiveness in preventing chronic diseases. Periploca forrestii, a renowned medicinal plant of the Apocynaceae family, contains diverse flavonoid glycosides and is clinically used to treat rheumatoid arthritis and traumatic injuries. However, the mechanisms underlying the biosynthesis of these flavonoid glycosides have not yet been elucidated. In this study, we used widely targeted metabolomics and full-length transcriptome sequencing to identify flavonoid diversity and biosynthetic genes in P. forrestii. A total of 120 flavonoid glycosides, including 21 C-, 96 O-, and 3 C/O-glycosides, were identified and annotated. Based on 24,123 full-length coding sequences, 99 uridine diphosphate sugar-utilizing glycosyltransferases (UGTs) were identified and classified into 14 groups. Biochemical assays revealed that four UGTs exhibited O-glycosyltransferase activity toward apigenin and luteolin. Among them, PfUGT74B4 and PfUGT92A8 were highly promiscuous and exhibited multisite O-glycosylation or consecutive glycosylation activities toward various flavonoid aglycones. These four glycosyltransferases may significantly contribute to the diversity of flavonoid glycosides in P. forrestii. Our findings provide a valuable genetic resource for further studies on P. forrestii and insights into the metabolic engineering of bioactive flavonoid glycosides.

Deep learning in template-free de novo biosynthetic pathway design of natural products

Natural products (NPs) are indispensable in drug development, particularly in combating infections, cancer, and neurodegenerative diseases. However, their limited availability poses significant challenges. Template-free de novo biosynthetic pathway design provides a strategic solution for NP production, with deep learning standing out as a powerful tool in this domain. This review delves into state-of-the-art deep learning algorithms in NP biosynthesis pathway design. It provides an in-depth discussion of databases like Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, and UniProt, which are essential for model training, along with chemical databases such as Reaxys, SciFinder, and PubChem for transfer learning to expand models' understanding of the broader chemical space. It evaluates the potential and challenges of sequence-to-sequence and graph-to-graph translation models for accurate single-step prediction. Additionally, it discusses search algorithms for multistep prediction and deep learning algorithms for predicting enzyme function. The review also highlights the pivotal role of deep learning in improving catalytic efficiency through enzyme engineering, which is essential for enhancing NP production. Moreover, it examines the application of large language models in pathway design, enzyme discovery, and enzyme engineering. Finally, it addresses the challenges and prospects associated with template-free approaches, offering insights into potential advancements in NP biosynthesis pathway design.

Rational enzyme design by reducing the number of hotspots and library size

Biocatalysts that are eco-friendly, sustainable, and highly specific have great potential for applications in the production of fine chemicals, food, detergents, biofuels, pharmaceuticals, and more. However, due to factors such as low activity, narrow substrate scope, poor thermostability, or incorrect selectivity, most natural enzymes cannot be directly used for large-scale production of the desired products. To overcome these obstacles, protein engineering methods have been developed over decades and have become powerful and versatile tools for adapting enzymes with improved catalytic properties or new functions. The vastness of the protein sequence space makes screening a bottleneck in obtaining advantageous mutated enzymes in traditional directed evolution. In the realm of mathematics, there are two major constraints in the protein sequence space: (1) the number of residue substitutions (M); and (2) the number of codons encoding amino acids as building blocks (N). This feature review highlights protein engineering strategies to reduce screening efforts from two dimensions by reducing the numbers M and N, and also discusses representative seminal studies of rationally engineered natural enzymes to deliver new catalytic functions.

Biocatalysis in Non-Conventional Media: Unlocking the Potential for Sustainable Chiral Amine Synthesis

The application of biocatalysis has become essential in both academic and industrial domains for the asymmetric synthesis of chiral amines, and it serves as an alternative tool to transition-metal catalysis and complements traditional chemical methods. It relies on the swift expansion of available processes, primarily as a result of advanced tools for enzyme discovery, combined with high-throughput laboratory evolution techniques for optimizing biocatalysts. This concept paper explores the utilization of non-conventional media such as ether-type solvents, deep eutectic solvents, and micellar catalysis to enhance biocatalytic reactions for chiral amine synthesis. Each section focuses on the unique properties of these media, including their ability to stabilize enzymes, alter substrate solubility, and modulate enzyme selectivity. The paper aims to provide insights into how these innovative media can overcome traditional limitations, offering new avenues for sustainable and efficient chiral amine production through biocatalytic processes.

Enriching productive mutational paths accelerates enzyme evolution

Darwinian evolution has given rise to all the enzymes that enable life on Earth. Mimicking natural selection, scientists have learned to tailor these biocatalysts through recursive cycles of mutation, selection and amplification, often relying on screening large protein libraries to productively modulate the complex interplay between protein structure, dynamics and function. Here we show that by removing destabilizing mutations at the library design stage and taking advantage of recent advances in gene synthesis, we can accelerate the evolution of a computationally designed enzyme. In only five rounds of evolution, we generated a Kemp eliminase-an enzymatic model system for proton transfer from carbon-that accelerates the proton abstraction step >108-fold over the uncatalyzed reaction. Recombining the resulting variant with a previously evolved Kemp eliminase HG3.17, which exhibits similar activity but differs by 29 substitutions, allowed us to chart the topography of the designer enzyme's fitness landscape, highlighting that a given protein scaffold can accommodate several, equally viable solutions to a specific catalytic problem.

Synthetic directed evolution for targeted engineering of plant traits

Improving crop traits requires genetic diversity, which allows breeders to select advantageous alleles of key genes. In species or loci that lack sufficient genetic diversity, synthetic directed evolution (SDE) can supplement natural variation, thus expanding the possibilities for trait engineering. In this review, we explore recent advances and applications of SDE for crop improvement, highlighting potential targets (coding sequences and cis-regulatory elements) and computational tools to enhance crop resilience and performance across diverse environments. Recent advancements in SDE approaches have streamlined the generation of variants and the selection processes; by leveraging these advanced technologies and principles, we can minimize concerns about host fitness and unintended effects, thus opening promising avenues for effectively enhancing crop traits.

C-H functionalization reactions catalyzed by artificial metalloenzymes

CH functionalization, a promising frontier in modern organic chemistry, facilitates the direct conversion of inert CH bonds into many valuable functional groups. Despite its merits, traditional homogeneous catalysis, often faces challenges in efficiency, selectivity, and sustainability towards this transformation. In this context, artificial metalloenzymes (ArMs), resulting from the incorporation of a catalytically-competent metal cofactor within an evolvable protein scaffold, bridges the gap between the efficiency of enzymatic transformations and the versatility of transition metal catalysis. Accordingly, ArMs have emerged as attractive tools for various challenging catalytic transformations. Additionally, the coming of age of directed evolution has unlocked unprecedented avenues for optimizing enzymatic catalysis. Taking advantage of their genetically-encoded protein scaffold, ArMs have been evolved to catalyze various CH functionalization reactions. This review delves into the recent developments of ArM-catalyzed CH functionalization reactions, highlighting the benefits of engineering the second coordination sphere around a metal cofactor within a host protein.

Synthetic gene circuit evolution: Insights and opportunities at the mid-scale

Directed evolution focuses on optimizing single genetic components for predefined engineering goals by artificial mutagenesis and selection. In contrast, experimental evolution studies the adaptation of entire genomes in serially propagated cell populations, to provide an experimental basis for evolutionary theory. There is a relatively unexplored gap at the middle ground between these two techniques, to evolve in vivo entire synthetic gene circuits with nontrivial dynamic function instead of single parts or whole genomes. We discuss the requirements for such mid-scale evolution, with hypothetical examples for evolving synthetic gene circuits by appropriate selection and targeted shuffling of a seed set of genetic components in vivo. Implementing similar methods should aid the rapid generation, functionalization, and optimization of synthetic gene circuits in various organisms and environments, accelerating both the development of biomedical and technological applications and the understanding of principles guiding regulatory network evolution.

Directed evolution of material-producing microorganisms

Nature is home to a variety of microorganisms that create materials under environmentally friendly conditions. While this offers an attractive approach for sustainable manufacturing, the production of materials by native microorganisms is usually slow and synthetic biology tools to engineer faster microorganisms are only available when prior knowledge of genotype-phenotype links is available. Here, we utilize a high-throughput directed evolution platform to enhance the fitness of whole microorganisms under selection pressure and identify genetic pathways to enhance the material production capabilities of native species. Using Komagataeibacter sucrofermentans as a model cellulose-producing microorganism, we show that our droplet-based microfluidic platform enables the directed evolution of these bacteria toward a small number of cellulose overproducers from an initial pool of 40,000 random mutants. Sequencing of the evolved strains reveals an unexpected link between the cellulose-forming ability of the bacteria and a gene encoding a protease complex responsible for protein turnover in the cell. The ability to enhance the fitness of microorganisms toward a specific phenotype and to unravel genotype-phenotype links makes this high-throughput directed evolution platform a promising tool for the development of new strains for the sustainable manufacturing of materials.

Stability Oracle: a structure-based graph-transformer framework for identifying stabilizing mutations

Engineering stabilized proteins is a fundamental challenge in the development of industrial and pharmaceutical biotechnologies. We present Stability Oracle: a structure-based graph-transformer framework that achieves SOTA performance on accurately identifying thermodynamically stabilizing mutations. Our framework introduces several innovations to overcome well-known challenges in data scarcity and bias, generalization, and computation time, such as: Thermodynamic Permutations for data augmentation, structural amino acid embeddings to model a mutation with a single structure, a protein structure-specific attention-bias mechanism that makes transformers a viable alternative to graph neural networks. We provide training/test splits that mitigate data leakage and ensure proper model evaluation. Furthermore, to examine our data engineering contributions, we fine-tune ESM2 representations (Prostata-IFML) and achieve SOTA for sequence-based models. Notably, Stability Oracle outperforms Prostata-IFML even though it was pretrained on 2000X less proteins and has 548X less parameters. Our framework establishes a path for fine-tuning structure-based transformers to virtually any phenotype, a necessary task for accelerating the development of protein-based biotechnologies.

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.

Simple Sol-Gel Protein Stabilization toward Rainbow and White Lighting Devices

Fluorescent proteins (FPs) are heralded as a paradigm of sustainable materials for photonics/optoelectronics. However, their stabilization under non-physiological environments and/or harsh operation conditions is the major challenge. Among the FP-stabilization methods, classical sol-gel is the most effective, but less versatile, as most of the proteins/enzymes are easily degraded due to the need of multi-step processes, surfactants, and mixed water/organic solvents in extreme pH. Herein, sol-gel chemistry with archetypal FPs (mGreenLantern; mCherry) is revisited, simplifying the method by one-pot, surfactant-free, and aqueous media (phosphate buffer saline pH = 7.4). The synthesis mechanism involves the direct reaction of the carboxylic groups at the FP surface with the silica precursor, generating a positively charged FP intermediate that acts as a seed for the formation of size-controlled mesoporous FP@SiO2 nanoparticles. Green-/red-emissive (single-FP component) and dual-emissive (multi-FPs component; kinetic studies not required) FP@SiO2 are prepared without affecting the FP photoluminescence and stabilities (>6 months) under dry storage and organic solvent suspensions. Finally, FP@SiO2 color filters are applied to rainbow and white bio-hybrid light-emitting diodes featuring up to 15-fold enhanced stabilities without reducing luminous efficacy compared to references with native FPs. Overall, an easy, versatile, and effective FP-stabilization method is demonstrated in FP@SiO2 toward sustainable protein lighting.

Engineering a Metal Reductase for the Bioremediation of Anthropogenic Electronic Wastes: From Hg(II) to Au(III) and Ag(I) Enzymatic Reduction

Recovering precious metals from electronic waste (e-waste) using microbes presents a sustainable methodology that can contribute toward the maintenance of planetary health. To better realize the potential of bioremediation using engineered microbes, enzymes that mediate the reduction of Au(III) to Au(0) have been the subject of intense research. In this study, we report the successful engineering of a metal reductase, MerA, whose cognate substrate is mercury(II), toward other precious metals such as Au(III) and Ag(I). The engineered variant, G415I, exhibited a 15-fold increase in catalytic efficiency (k cat/K M) in Au(III) reduction to Au(0) and a 200-fold increase in catalytic efficiency in Ag(I) reduction to Ag(0) with respect to the wild-type enzyme. The apparent shift in preference toward noncognate metal ions may be attributed to the energetics of valency preference. The improved Au(III) reductase has an apparent increased preference toward monovalent cations such as Au(I) and Ag(I), with respect to divalent cations such as Hg(II), the cognate substrate of the progenitor MerA (an increase in K M of 5.0-fold for Hg(II), compared to a decrease in K M of 5.8-fold for Au(III) and 1.8-fold for Ag(I), respectively). This study further extends the mechanistic understanding of Au(III) bioreduction that could proceed through the stabilization of Au(I) en route to Au(0) and suggests that the biosynthesis of Au nanoparticles with high efficiency can be realized through the engineering of promiscuous metal reductases for precious metal recovery from e-wastes.

Current status and emerging frontiers in enzyme engineering: An industrial perspective

Protein engineering mechanisms can be an efficient approach to enhance the biochemical properties of various biocatalysts. Immobilization of biocatalysts and the introduction of new-to-nature chemical reactivities are also possible through the same mechanism. Discovering new protocols that enhance the catalytic active protein that possesses novelty in terms of being stable, active, and, stereoselectivity with functions could be identified as essential areas in terms of concurrent bioorganic chemistry (synergistic relationship between organic chemistry and biochemistry in the context of enzyme engineering). However, with our current level of knowledge about protein folding and its correlation with protein conformation and activities, it is almost impossible to design proteins with specific biological and physical properties. Hence, contemporary protein engineering typically involves reprogramming existing enzymes by mutagenesis to generate new phenotypes with desired properties. These processes ensure that limitations of naturally occurring enzymes are not encountered. For example, researchers have engineered cellulases and hemicellulases to withstand harsh conditions encountered during biomass pretreatment, such as high temperatures and acidic environments. By enhancing the activity and robustness of these enzymes, biofuel production becomes more economically viable and environmentally sustainable. Recent trends in enzyme engineering have enabled the development of tailored biocatalysts for pharmaceutical applications. For instance, researchers have engineered enzymes such as cytochrome P450s and amine oxidases to catalyze challenging reactions involved in drug synthesis. In addition to conventional methods, there has been an increasing application of machine learning techniques to identify patterns in data. These patterns are then used to predict protein structures, enhance enzyme solubility, stability, and function, forecast substrate specificity, and assist in rational protein design. In this review, we discussed recent trends in enzyme engineering to optimize the biochemical properties of various biocatalysts. Using examples relevant to biotechnology in engineering enzymes, we try to expatiate the significance of enzyme engineering with how these methods could be applied to optimize the biochemical properties of a naturally occurring enzyme.

Improving the enzymatic activity and stability of N-carbamoyl hydrolase using deep learning approach

BACKGROUND

Optically active D-amino acids are widely used as intermediates in the synthesis of antibiotics, insecticides, and peptide hormones. Currently, the two-enzyme cascade reaction is the most efficient way to produce D-amino acids using enzymes DHdt and DCase, but DCase is susceptible to heat inactivation. Here, to enhance the enzymatic activity and thermal stability of DCase, a rational design software "Feitian" was developed based on kcat prediction using the deep learning approach.

RESULTS

According to empirical design and prediction of "Feitian" software, six single-point mutants with high kcat value were selected and successfully constructed by site-directed mutagenesis. Out of six, three mutants (Q4C, T212S, and A302C) showed higher enzymatic activity than the wild-type. Furthermore, the combined triple-point mutant DCase-M3 (Q4C/T212S/A302C) exhibited a 4.25-fold increase in activity (29.77 ± 4.52 U) and a 2.25-fold increase in thermal stability as compared to the wild-type, respectively. Through the whole-cell reaction, the high titer of D-HPG (2.57 ± 0.43 mM) was produced by the mutant Q4C/T212S/A302C, which was about 2.04-fold of the wild-type. Molecular dynamics simulation results showed that DCase-M3 significantly enhances the rigidity of the catalytic site and thus increases the activity of DCase-M3.

CONCLUSIONS

In this study, an efficient rational design software "Feitian" was successfully developed with a prediction accuracy of about 50% in enzymatic activity. A triple-point mutant DCase-M3 (Q4C/T212S/A302C) with enhanced enzymatic activity and thermostability was successfully obtained, which could be applied to the development of a fully enzymatic process for the industrial production of D-HPG.

Droplet-based microfluidics and enzyme evolution

Enzymes are widely used as catalysts in the chemical and pharmaceutical industries. While successful in many situations, they must usually be adapted to operate efficiently under nonnatural conditions. Enzyme engineering allows the creation of novel enzymes that are stable at elevated temperatures or have higher activities and selectivities. Current enzyme engineering techniques require the production and testing of enzyme variant libraries to identify members with desired attributes. Unfortunately, traditional screening methods cannot screen such large mutagenesis libraries in a robust and timely manner. Droplet-based microfluidic systems can produce, process, and sort picoliter droplets at kilohertz rates and have emerged as powerful tools for library screening and thus enzyme engineering. We describe how droplet-based microfluidics has been used to advance directed evolution.

Utilization of low-stability variants in protein evolutionary engineering

Evolutionary engineering involves repeated mutations and screening and is widely used to modify protein functions. However, it is important to diversify evolutionary pathways to eliminate the bias and limitations of the variants by using traditionally unselected variants. In this study, we focused on low-stability variants that are commonly excluded from evolutionary processes and tested a method that included an additional restabilization step. The esterase from the thermophilic bacterium Alicyclobacillus acidocaldarius was used as a model protein, and its activity at its optimum temperature of 65 °C was improved by evolutionary experiments using random mutations by error-prone PCR. After restabilization using low-stability variants with low-temperature (37 °C) activity, several re-stabilizing variants were obtained from a large number of variant libraries. Some of the restabilized variants achieved by removing the destabilizing mutations showed higher activity than that of the wild-type protein. This implies that low-stability variants with low-temperature activity can be re-evolved for future use. This method will enable further diversification of evolutionary pathways.

A New Architecture for DNA-Templated Synthesis in Which Abasic Sites Protect Reactants from Degradation

The synthesis of artificial sequence-defined polymers that match and extend the functionality of proteins is an important goal in materials science. One way of achieving this is to program a sequence of chemical reactions between precursor building blocks by means of attached oligonucleotide adapters. However, hydrolysis of the reactive building blocks has so far limited the length and yield of product that can be obtained using DNA-templated reactions. Here, we report an architecture for DNA-templated synthesis in which reactants are tethered at internal abasic sites on opposite strands of a DNA duplex. We show that an abasic site within a DNA duplex can protect a nearby thioester from degradation, significantly increasing the yield of a DNA-templated reaction. This protective effect has the potential to overcome the challenges associated with programmable, sequence-controlled synthesis of long non-natural polymers by extending the lifetime of the reactive building blocks.

Biocatalysis: landmark discoveries and applications in chemical synthesis

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

Human GST P1-1 Redesigned for Enhanced Catalytic Activity with the Anticancer Prodrug Telcyta and Improved Thermostability

Protein engineering can be used to tailor enzymes for medical purposes, including antibody-directed enzyme prodrug therapy (ADEPT), which can act as a tumor-targeted alternative to conventional chemotherapy for cancer. In ADEPT, the antibody serves as a vector, delivering a drug-activating enzyme selectively to the tumor site. Glutathione transferases (GSTs) are a family of naturally occurring detoxication enzymes, and the finding that some of them are overexpressed in tumors has been exploited to develop GST-activated prodrugs. The prodrug Telcyta is activated by GST P1-1, which is the GST most commonly elevated in cancer cells, implying that tumors overexpressing GST P1-1 should be particularly vulnerable to Telcyta. Promising antitumor activity has been noted in clinical trials, but the wildtype enzyme has modest activity with Telcyta, and further functional improvement would enhance its usefulness for ADEPT. We utilized protein engineering to construct human GST P1-1 gene variants in the search for enzymes with enhanced activity with Telcyta. The variant Y109H displayed a 2.9-fold higher enzyme activity compared to the wild-type GST P1-1. However, increased catalytic potency was accompanied by decreased thermal stability of the Y109H enzyme, losing 99% of its activity in 8 min at 50 °C. Thermal stability was restored by four additional mutations simultaneously introduced without loss of the enhanced activity with Telcyta. The mutation Q85R was identified as an important contributor to the regained thermostability. These results represent a first step towards a functional ADEPT application for Telcyta.

Size matters: the biochemical logic of ligand type in endocrine crosstalk

The endocrine system is a fundamental type of long-range cell-cell communication that is important for maintaining metabolism, physiology, and other aspects of organismal homeostasis. Endocrine signaling is mediated by diverse blood-borne ligands, also called hormones, including metabolites, lipids, steroids, peptides, and proteins. The size and structure of these hormones are fine-tuned to make them bioactive, responsive, and adaptable to meet the demands of changing environments. Why has nature selected such diverse ligand types to mediate communication in the endocrine system? What is the chemical, signaling, or physiologic logic of these ligands? What fundamental principles from our knowledge of endocrine communication can be applied as we continue as a field to uncover additional new circulating molecules that are claimed to mediate long-range cell and tissue crosstalk? This review provides a framework based on the biochemical logic behind this crosstalk with respect to their chemistry, temporal regulation in physiology, specificity, signaling actions, and evolutionary development.

Understanding the intricacy of protein in hydrated deep eutectic solvent: Solvation dynamics, conformational fluctuation dynamics, and stability

Deep eutectic solvents (DESs) are potential biocatalytic media due to their easy preparation, fine-tuneability, biocompatibility, and most importantly, due to their ability to keep protein stable and active. However, there are many unanswered questions and gaps in our knowledge about how proteins behave in these alternate media. Herein, we investigated solvation dynamics, conformational fluctuation dynamics, and stability of human serum albumin (HSA) in 0.5 Acetamide/0.3 Urea/0.2 Sorbitol (0.5Ac/0.3Ur/0.2Sor) DES of varying concentrations to understand the intricacy of protein behaviour in DES. Our result revealed a gradual decrease in the side-chain flexibility and thermal stability of HSA beyond 30 % DES. On the other hand, the associated water dynamics around domain-I of HSA decelerate only marginally with increasing DES content, although viscosity rises considerably. We propose that even though macroscopic solvent properties are altered, a protein feels only an aqueous type of environment in the presence of DES. This is probably the first experimental study to delineate the role of the associated water structure of the enzyme for maintaining its stability inside DES. Although considerable effort is necessary to generalize such claims, it might serve as the basis for understanding why proteins remain stable and active in DES.

ACIDES: on-line monitoring of forward genetic screens for protein engineering

Forward genetic screens of mutated variants are a versatile strategy for protein engineering and investigation, which has been successfully applied to various studies like directed evolution (DE) and deep mutational scanning (DMS). While next-generation sequencing can track millions of variants during the screening rounds, the vast and noisy nature of the sequencing data impedes the estimation of the performance of individual variants. Here, we propose ACIDES that combines statistical inference and in-silico simulations to improve performance estimation in the library selection process by attributing accurate statistical scores to individual variants. We tested ACIDES first on a random-peptide-insertion experiment and then on multiple public datasets from DE and DMS studies. ACIDES allows experimentalists to reliably estimate variant performance on the fly and can aid protein engineering and research pipelines in a range of applications, including gene therapy.

Replication of synthetic recognition-encoded oligomers by ligation of trimer building blocks

The development of methods for replication of synthetic information oligomers will underpin the use of directed evolution to search new chemical space. Template-directed replication of triazole oligomers has been achieved using a covalent primer in conjunction with non-covalent binding of complementary building blocks. A phenol primer equipped with an alkyne was first attached to a benzoic recognition unit on a mixed sequence template via selective covalent ester base-pair formation. The remaining phenol recognition units on the template were then used for non-covalent binding of phosphine oxide oligomers equipped with an azide. The efficiency of the templated CuAAC reaction between the primer and phosphine oxide building blocks was investigated as a function of the number of H-bonds formed with the template. Increasing the strength of the non-covalent interaction between the template and the azide lead to a significant acceleration of the templated reaction. For shorter phosphine oxide oligomers intermolecular reactions compete with the templated process, but quantitative templated primer elongation was achieved with a phosphine oxide 3-mer building block that was able to form three H-bonds with the template. NMR spectroscopy and molecular models suggest that the template can fold, but addition of the phosphine oxide 3-mer leads to a complex with three H-bonds between phosphine oxide and phenol groups, aligning the azide and alkyne groups in a favourable geometry for the CuAAC reaction. In the product duplex, 1H and 31P NMR data confirm the presence of the three H-bonded base-pairs, demonstrating that the covalent and non-covalent base-pairs are geometrically compatible. A complete replication cycle was carried out starting from the oligotriazole template by covalent attachment of the primer, followed by template-directed elongation, and hydrolysis of the the ester base-pair in the resulting duplex to regenerate the template and liberate the copy strand. We have previously demonstrated sequence-selective oligomer replication using covalent base-pairing, but the trimer building block approach described here is suitable for replication of sequence information using non-covalent binding of the monomer building blocks to a template.

Genetically Encoded Oligomerization for Protein-Based Lighting Devices

Implementing proteins in optoelectronics represents a fresh idea toward a sustainable new class of materials with bio-functions that can replace environmentally unfriendly and/or toxic components without losing device performance. However, their native activity (fluorescence, catalysis, and so on) is easily lost under device fabrication/operation as non-native environments (organic solvents, organic/inorganic interfaces, and so on) and severe stress (temperature, irradiation, and so on) are involved. Herein, a gift bow genetically-encoded macro-oligomerization strategy is showcased to promote protein-protein solid interaction enabling i) high versatility with arbitrary proteins, ii) straightforward electrostatic driven control of the macro-oligomer size by ionic strength, and iii) stabilities over months in pure organic solvents and stress scenarios, allowing to integrate them into classical water-free polymer-based materials/components for optoelectronics. Indeed, rainbow-/white-emitting protein-based light-emitting diodes are fabricated, attesting a first-class performance compared to those with their respective native proteins: significantly enhanced device stabilities from a few minutes up to 100 h keeping device efficiency at high power driving conditions. Thus, the oligomerization concept is a solid bridge between biological systems and materials/components to meet expectations in bio-optoelectronics, in general, and lighting schemes, in particular.

Understanding the Multifaceted Mechanism of Compound I Formation in Unspecific Peroxygenases through Multiscale Simulations

Unspecific peroxygenases (UPOs) can selectively oxyfunctionalize unactivated hydrocarbons by using peroxides under mild conditions. They circumvent the oxygen dilemma faced by cytochrome P450s and exhibit greater stability than the latter. As such, they hold great potential for industrial applications. A thorough understanding of their catalysis is needed to improve their catalytic performance. However, it remains elusive how UPOs effectively convert peroxide to Compound I (CpdI), the principal oxidizing intermediate in the catalytic cycle. Previous computational studies of this process primarily focused on heme peroxidases and P450s, which have significant differences in the active site from UPOs. Additionally, the roles of peroxide unbinding in the kinetics of CpdI formation, which is essential for interpreting existing experiments, have been understudied. Moreover, there has been a lack of free energy characterizations with explicit sampling of protein and hydration dynamics, which is critical for understanding the thermodynamics of the proton transport (PT) events involved in CpdI formation. To bridge these gaps, we employed multiscale simulations to comprehensively characterize the CpdI formation in wild-type UPO from Agrocybe aegerita (AaeUPO). Extensive free energy and potential energy calculations were performed in a quantum mechanics/molecular mechanics setting. Our results indicate that substrate-binding dehydrates the active site, impeding the PT from H2O2 to a nearby catalytic base (Glu196). Furthermore, the PT is coupled with considerable hydrogen bond network rearrangements near the active site, facilitating subsequent O-O bond cleavage. Finally, large unbinding free energy barriers kinetically stabilize H2O2 at the active site. These findings reveal a delicate balance among PT, hydration dynamics, hydrogen bond rearrangement, and cosubstrate unbinding, which collectively enable efficient CpdI formation. Our simulation results are consistent with kinetic measurements and offer new insights into the CpdI formation mechanism at atomic-level details, which can potentially aid the design of next-generation biocatalysts for sustainable chemical transformations of feedstocks.

Fishing for Catalysis: Experimental Approaches to Narrowing Search Space in Directed Evolution of Enzymes

Directed evolution has transformed protein engineering offering a path to rapid improvement of protein properties. Yet, in practice it is limited by the hyper-astronomic protein sequence search space, and approaches to identify mutagenic hot spots, i.e., locations where mutations are most likely to have a productive impact, are needed. In this perspective, we categorize and discuss recent progress in the experimental approaches (broadly defined as structural, bioinformatic, and dynamic) to hot spot identification. Recent successes in harnessing protein dynamics and machine learning approaches provide new opportunities for the field and will undoubtedly help directed evolution reach its full potential.

Developing and enhancing promiscuous activity for NAD(P)H-dependent flavin reductase via elimination of cofactor

Promiscuous enzymes have shown their synthetic abilities in generating various organic compounds with high selectively and efficiency under mild conditions. Therefore, the design and development of conditions to raise promiscuity to the enzymes have been under the spotlight in recent years. Flavin reductase, that reduces flavins by using NADH as a cofactor, has not been studied in promiscuous reactions. In the present study, it was aimed to develop a catalytic promiscuous activity in the recombinant E.coli flavin reductase by removing its cofactor. The flavin reductase demonstrated a promiscuous activity for Knoevenagel condensation and Michael addition reactions individually. The cofactor-independent promiscuous activity of the flavin reductase was further enhanced by altering the reaction conditions to proceed a Knoevenagel-Michael addition cascade for tetraketone synthesis. Yet, the presence of the cofactor blocked the promiscuous Knoevenagel condensation, Michael addition, and therefore the cascade reaction, demonstrating that the removal of NADH was pivotal in inducing the promiscuous activity. Furthermore, molecular docking and MD simulations were performed to obtain more structural and mechanistic details of the transformation. The computational studies identified the most likely catalytic sites of the flavin reductase in the reaction. Additionally, a truncated variant of the enzyme that lacked 28 residues from the C-terminus displayed comparable activity to the wild-type enzyme, indicating the robustness of the enzyme in performing the cascade reaction. In brief, the cofactor-elimination method presented in this work could be considered as a straightforward and economical approach for inducing enzyme promiscuity in promoting organic transformations.

Nature-inspired Enzyme engineering and sustainable catalysis: biochemical clues from the world of plants and extremophiles

The use of enzymes to accelerate chemical reactions for the synthesis of industrially important products is rapidly gaining popularity. Biocatalysis is an environment-friendly approach as it not only uses non-toxic, biodegradable, and renewable raw materials but also helps to reduce waste generation. In this context, enzymes from organisms living in extreme conditions (extremozymes) have been studied extensively and used in industries (food and pharmaceutical), agriculture, and molecular biology, as they are adapted to catalyze reactions withstanding harsh environmental conditions. Enzyme engineering plays a key role in integrating the structure-function insights from reference enzymes and their utilization for developing improvised catalysts. It helps to transform the enzymes to enhance their activity, stability, substrates-specificity, and substrate-versatility by suitably modifying enzyme structure, thereby creating new variants of the enzyme with improved physical and chemical properties. Here, we have illustrated the relatively less-tapped potentials of plant enzymes in general and their sub-class of extremozymes for industrial applications. Plants are exposed to a wide range of abiotic and biotic stresses due to their sessile nature, for which they have developed various mechanisms, including the production of stress-response enzymes. While extremozymes from microorganisms have been extensively studied, there are clear indications that plants and algae also produce extremophilic enzymes as their survival strategy, which may find industrial applications. Typical plant enzymes, such as ascorbate peroxidase, papain, carbonic anhydrase, glycoside hydrolases and others have been examined in this review with respect to their stress-tolerant features and further improvement via enzyme engineering. Some rare instances of plant-derived enzymes that point to greater exploration for industrial use have also been presented here. The overall implication is to utilize biochemical clues from the plant-based enzymes for robust, efficient, and substrate/reaction conditions-versatile scaffolds or reference leads for enzyme engineering.

Designing artificial pathways for improving chemical production

Metabolic engineering exploits manipulation of catalytic and regulatory elements to improve a specific function of the host cell, often the synthesis of interesting chemicals. Although naturally occurring pathways are significant resources for metabolic engineering, these pathways are frequently inefficient and suffer from a series of inherent drawbacks. Designing artificial pathways in a rational manner provides a promising alternative for chemicals production. However, the entry barrier of designing artificial pathway is relatively high, which requires researchers a comprehensive and deep understanding of physical, chemical and biological principles. On the other hand, the designed artificial pathways frequently suffer from low efficiencies, which impair their further applications in host cells. Here, we illustrate the concept and basic workflow of retrobiosynthesis in designing artificial pathways, as well as the most currently used methods including the knowledge- and computer-based approaches. Then, we discuss how to obtain desired enzymes for novel biochemistries, and how to trim the initially designed artificial pathways for further improving their functionalities. Finally, we summarize the current applications of artificial pathways from feedstocks utilization to various products synthesis, as well as our future perspectives on designing artificial pathways.

A primer to directed evolution: current methodologies and future directions

Directed evolution is one of the most powerful tools for protein engineering and functions by harnessing natural evolution, but on a shorter timescale. It enables the rapid selection of variants of biomolecules with properties that make them more suitable for specific applications. Since the first in vitro evolution experiments performed by Sol Spiegelman in 1967, a wide range of techniques have been developed to tackle the main two steps of directed evolution: genetic diversification (library generation), and isolation of the variants of interest. This review covers the main modern methodologies, discussing the advantages and drawbacks of each, and hence the considerations for designing directed evolution experiments. Furthermore, the most recent developments are discussed, showing how advances in the handling of ever larger library sizes are enabling new research questions to be tackled.

In the business of base editors: Evolution from bench to bedside

With the advent of recombinant DNA technology in the 1970s, the idea of using gene therapies to treat human genetic diseases captured the interest and imagination of scientists around the world. Years later, enabled largely by the development of CRISPR-based genome editing tools, the field has exploded, with academic labs, startup biotechnology companies, and large pharmaceutical corporations working in concert to develop life-changing therapeutics. In this Essay, we highlight base editing technologies and their development from bench to bedside. Base editing, first reported in 2016, is capable of installing C•G to T•A and A•T to G•C point mutations, while largely circumventing some of the pitfalls of traditional CRISPR/Cas9 gene editing. Despite their youth, these technologies have been widely used by both academic labs and therapeutics-based companies. Here, we provide an overview of the mechanics of base editing and its use in clinical trials.

Alleviating the unwanted effects of oxidative stress on Aβ clearance: a review of related concepts and strategies for the development of computational modelling

Treatment for Alzheimer's disease (AD) can be more effective in the early stages. Although we do not completely understand the aetiology of the early stages of AD, potential pathological factors (amyloid beta [Aβ] and tau) and other co-factors have been identified as causes of AD, which may indicate some of the mechanism at work in the early stages of AD. Today, one of the primary techniques used to help delay or prevent AD in the early stages involves alleviating the unwanted effects of oxidative stress on Aβ clearance. 4-Hydroxynonenal (HNE), a product of lipid peroxidation caused by oxidative stress, plays a key role in the adduction of the degrading proteases. This HNE employs a mechanism which decreases catalytic activity. This process ultimately impairs Aβ clearance. The degradation of HNE-modified proteins helps to alleviate the unwanted effects of oxidative stress. Having a clear understanding of the mechanisms associated with the degradation of the HNE-modified proteins is essential for the development of strategies and for alleviating the unwanted effects of oxidative stress. The strategies which could be employed to decrease the effects of oxidative stress include enhancing antioxidant activity, as well as the use of nanozymes and/or specific inhibitors. One area which shows promise in reducing oxidative stress is protein design. However, more research is needed to improve the effectiveness and accuracy of this technique. This paper discusses the interplay of potential pathological factors and AD. In particular, it focuses on the effect of oxidative stress on the expression of the Aβ-degrading proteases through adduction of the degrading proteases caused by HNE. The paper also elucidates other strategies that can be used to alleviate the unwanted effects of oxidative stress on Aβ clearance. To improve the effectiveness and accuracy of protein design, we explain the application of quantum mechanical/molecular mechanical approach.

Role of distal sites in enzyme engineering

The limitations associated with natural enzyme catalysis have triggered the rise of the field of protein engineering. Traditional rational design was based on the analysis of protein structural information and catalytic mechanisms to identify key active sites or ligand binding sites to reshape the substrate pocket. The role and significance of functional sites in the active center have been studied extensively. With a deeper understanding of the structure-catalysis relationship map, the entire protein molecule can be filled with residues that play a substantial role in its structure and function. However, the catalytic mechanism underlying distal mutations remains unclear. The aim of this review was to highlight the criticality of the distal site in enzyme engineering based on the following three aspects: What can distal mutations exert on function from mutability landscape? How do distal sites influence enzyme function? How to predict and design distal mutations? This review provides insights into the catalytic mechanism of enzymes from the global interaction network, knowledge from sequence-structure-dynamics-function relationships, and strategies for distal mutation-based protein engineering.

Structure-informed microbial population genetics elucidate selective pressures that shape protein evolution

Comprehensive sampling of natural genetic diversity with metagenomics enables highly resolved insights into the interplay between ecology and evolution. However, resolving adaptive, neutral, or purifying processes of evolution from intrapopulation genomic variation remains a challenge, partly due to the sole reliance on gene sequences to interpret variants. Here, we describe an approach to analyze genetic variation in the context of predicted protein structures and apply it to a marine microbial population within the SAR11 subclade 1a.3.V, which dominates low-latitude surface oceans. Our analyses reveal a tight association between genetic variation and protein structure. In a central gene in nitrogen metabolism, we observe decreased occurrence of nonsynonymous variants from ligand-binding sites as a function of nitrate concentrations, revealing genetic targets of distinct evolutionary pressures maintained by nutrient availability. Our work yields insights into the governing principles of evolution and enables structure-aware investigations of microbial population genetics.

Progresses in Cell-Free In Vitro Evolution

Biopolymers, such as proteins and RNA, are integral components of living organisms and have evolved through a process of repeated mutation and selection. The technique of "cell-free in vitro evolution" is a powerful experimental approach for developing biopolymers with desired functions and structural properties. Since Spiegelman's pioneering work over 50 years ago, biopolymers with a wide range of functions have been developed using in vitro evolution in cell-free systems. The use of cell-free systems offers several advantages, including the ability to synthesize a wider range of proteins without the limitations imposed by cytotoxicity, and the capacity for higher throughput and larger library sizes than cell-based evolutionary experiments. In this chapter, we provide a comprehensive overview of the progress made in the field of cell-free in vitro evolution by categorizing evolution into directed and undirected. The biopolymers produced by these methods are valuable assets in medicine and industry, and as a means of exploring the potential of biopolymers.

Ultrahigh-Throughput Screening of an Artificial Metalloenzyme using Double Emulsions

The potential for ultrahigh-throughput compartmentalization renders droplet microfluidics an attractive tool for the directed evolution of enzymes. Importantly, it ensures maintenance of the phenotype-genotype linkage, enabling reliable identification of improved mutants. Herein, we report an approach for ultrahigh-throughput screening of an artificial metalloenzyme in double emulsion droplets (DEs) using commercially available fluorescence-activated cell sorters (FACS). This protocol was validated by screening a 400 double-mutant streptavidin library for ruthenium-catalyzed deallylation of an alloc-protected aminocoumarin. The most active variants, identified by next-generation sequencing, were in good agreement with hits obtained using a 96-well plate procedure. These findings pave the way for the systematic implementation of FACS for the directed evolution of (artificial) enzymes and will significantly expand the accessibility of ultrahigh-throughput DE screening protocols.

Effect of backbone flexibility on covalent template-directed synthesis of linear oligomers

Covalent template-directed synthesis can be used to replicate synthetic oligomers, but success depends critically on the conformational properties of the backbone. Here we investigate how the choice of monomer building block affects the flexibility of the backbone and in turn the efficiency of the replication process for a series of different triazole oligomers. Two competing reaction pathways were identified for monomers attached to a template, resulting in the formation of either macrocyclic or linear products. For flexible backbones, macrocycles and linear oligomers are formed at similar rates, but a more rigid backbone gave exclusively the linear product. The experimental results are consistent with ring strain calculations using molecular mechanics: products with low ring strain (20-30 kJ mol^-1) formed rapidly, and products with high ring strain (>100 kJ mol^-1) were not observed. Template-directed replication of linear oligomers requires monomers that rigid enough to prevent the formation of undesired macrocycles, but not so rigid that the linear templating pathway leading to the duplex is inhibited. Molecular mechanics calculations of ring strain provide a straightforward tool for assessing the flexibility of potential backbones and the viability different monomer designs before embarking on synthesis.

A patent-based consideration of latest platforms in the art of directed evolution: a decade long untold story

Directed (or in vitro) evolution of proteins and metabolic pathways requires tools for creating genetic diversity and identifying protein variants with new or improved functional properties. Besides simplicity, reliability, speed, versatility, universal applicability and economy of the technique, the new science of synthetic biology requires improved means for construction of smart and high-quality mutant libraries to better navigate the sequence diversity. In vitro CRISPR/Cas9-mediated mutagenic (ICM) system and machine-learning (ML)-assisted approaches to directed evolution are now in the field to achieve the goal. This review describes the gene diversification strategies, screening and selection methods, in silico (computer-aided), Cas9-mediated and ML-based approaches to mutagenesis, developed especially in the last decade, and their patent position. The objective behind is to emphasize researchers the need for noting which mutagenesis, screening or selection method is patented and then selecting a suitable restriction-free approach to sequence diversity. Techniques and evolved products subject to patent rights need commercial license if their use is for purposes other than private or experimental research.

Rational Engineering of 3α-Hydroxysteroid Dehydrogenase/Carbonyl Reductase for a Biomimetic Nicotinamide Mononucleotide Cofactor

Enzymes are powerful biological catalysts for natural substrates but they have low catalytic efficiency for non-natural substrates. Protein engineering can be used to optimize enzymes for catalysis and stability. 3α-Hydroxysteroid dehydrogenase/carbonyl reductase (3α-HSD/CR) catalyzes the oxidoreduction reaction of NAD+ with androsterone. Based on the structure and catalytic mechanism, we mutated the residues of T11, I13, D41, A70, and I112 and they interacted with different portions of NAD+ to switch cofactor specificity to biomimetic cofactor nicotinamide mononucleotide (NMN+). Compared to wild-type 3α-HSD/CR, the catalytic efficiency of these mutants for NAD+ decreased significantly except for the T11 mutants but changed slightly for NMN+ except for the A70K mutant. The A70K mutant increased the catalytic efficiency for NMN+ by 8.7-fold, concomitant with a significant decrease in NAD+ by 1.4 × 104-fold, resulting in 9.6 × 104-fold cofactor specificity switch toward NMN+ over NAD+. Meanwhile, the I112K variant increased the thermal stability and changed to a three-state transition from a two-state transition of thermal unfolding of wild-type 3α-HSD/CR by differential scanning fluorimetry. Molecular docking analysis indicated that mutations on these residues affect the position and conformation of the docked NAD+ and NMN+, thereby affecting their activity. A70K variant sterically blocks the binding with NAD+, restores the H-bonding interactions of catalytic residues of Y155 and K159 with NMN+, and enhances the catalytic efficiency for NMN+.

H-Bond Templated Oligomer Synthesis Using a Covalent Primer

Template-directed synthesis of nucleic acids in the polymerase chain reaction is based on the use of a primer, which is elongated in the replication process. The attachment of a high affinity primer to the end of a template chain has been implemented for templating the synthesis of triazole oligomers. A covalent ester base-pair was used to attach a primer to a mixed sequence template. The resulting primed template has phenol recognition units on the template, which can form noncovalent base-pairs with phosphine oxide monomers via H-bonding, and an alkyne group on the primer, which can react with the azide group on a phosphine oxide monomer. Competition reactions between azides bearing phosphine oxide and phenol recognition groups were used to demonstrate a substantial template effect, due to H-bonding interactions between the phenols on the template and phosphine oxides on the azide. The largest rate acceleration was observed when a phosphine oxide 2-mer was used, because this compound binds to the template with a higher affinity than compounds that can only make one H-bond. The 31P NMR spectrum of the product duplex shows that the H-bonds responsible for the template effect are present in the product, and this result indicates that the covalent ester base-pairs and noncovalent H-bonded base-pairs developed here are geometrically compatible. Following the templated reaction, it is possible to regenerate the template and liberate the copy strand by hydrolysis of the ester base-pair used to attach the primer, thus completing a formal replication cycle.

Artificial selection methods from evolutionary computing show promise for directed evolution of microbes

Directed microbial evolution harnesses evolutionary processes in the laboratory to construct microorganisms with enhanced or novel functional traits. Attempting to direct evolutionary processes for applied goals is fundamental to evolutionary computation, which harnesses the principles of Darwinian evolution as a general-purpose search engine for solutions to challenging computational problems. Despite their overlapping approaches, artificial selection methods from evolutionary computing are not commonly applied to living systems in the laboratory. In this work, we ask whether parent selection algorithms-procedures for choosing promising progenitors-from evolutionary computation might be useful for directing the evolution of microbial populations when selecting for multiple functional traits. To do so, we introduce an agent-based model of directed microbial evolution, which we used to evaluate how well three selection algorithms from evolutionary computing (tournament selection, lexicase selection, and non-dominated elite selection) performed relative to methods commonly used in the laboratory (elite and top 10% selection). We found that multiobjective selection techniques from evolutionary computing (lexicase and non-dominated elite) generally outperformed the commonly used directed evolution approaches when selecting for multiple traits of interest. Our results motivate ongoing work transferring these multiobjective selection procedures into the laboratory and a continued evaluation of more sophisticated artificial selection methods.

Redesigning Enzymes for Biocatalysis: Exploiting Structural Understanding for Improved Selectivity

The discovery of new enzymes, alongside the push to make chemical processes more sustainable, has resulted in increased industrial interest in the use of biocatalytic processes to produce high-value and chiral precursor chemicals. Huge strides in protein engineering methodology and in silico tools have facilitated significant progress in the discovery and production of enzymes for biocatalytic processes. However, there are significant gaps in our knowledge of the relationship between enzyme structure and function. This has demonstrated the need for improved computational methods to model mechanisms and understand structure dynamics. Here, we explore efforts to rationally modify enzymes toward changing aspects of their catalyzed chemistry. We highlight examples of enzymes where links between enzyme function and structure have been made, thus enabling rational changes to the enzyme structure to give predictable chemical outcomes. We look at future directions the field could take and the technologies that will enable it.

Making Enzymes Suitable for Organic Chemistry by Rational Protein Design

This review outlines recent developments in protein engineering of stereo- and regioselective enzymes, which are of prime interest in organic and pharmaceutical chemistry as well as biotechnology. The widespread application of enzymes was hampered for decades due to limited enantio-, diastereo- and regioselectivity, which was the reason why most organic chemists were not interested in biocatalysis. This attitude began to change with the advent of semi-rational directed evolution methods based on focused saturation mutagenesis at sites lining the binding pocket. Screening constitutes the labor-intensive step (bottleneck), which is the reason why various research groups are continuing to develop techniques for the generation of small and smart mutant libraries. Rational enzyme design, traditionally an alternative to directed evolution, provides small collections of mutants which require minimal screening. This approach first focused on thermostabilization, and did not enter the field of stereoselectivity until later. Computational guides such as the Rosetta algorithms, HotSpot Wizard metric, and machine learning (ML) contribute significantly to decision making. The newest advancements show that semi-rational directed evolution such as CAST/ISM and rational enzyme design no longer develop on separate tracks, instead, they have started to merge. Indeed, researchers utilizing the two approaches have learned from each other. Today, the toolbox of organic chemists includes enzymes, primarily because the possibility of controlling stereoselectivity by protein engineering has ensured reliability when facing synthetic challenges. This review was also written with the hope that undergraduate and graduate education will include enzymes more so than in the past.

Thermostability engineering of industrial enzymes through structure modification

Thermostability is an essential requirement of enzymes in the industrial processes to catalyze the reactions at high temperatures; thus, enzyme engineering through directed evolution, semi-rational design and rational design are commonly employed to construct desired thermostable mutants. Several strategies are implemented to fulfill enzymes' thermostability demand including decreasing the entropy of the unfolded state through substitutions Gly → Xxx or Xxx → Pro, hydrogen bond, salt bridge, introducing two different simultaneous interactions through single mutant, hydrophobic interaction, filling the hydrophobic cavity core, decreasing surface hydrophobicity, truncating loop, aromatic-aromatic interaction and introducing positively charged residues to enzyme surface. In the current review, horizons about compatibility between secondary structures and substitutions at preferable structural positions to generate the most desirable thermostability in industrial enzymes are broadened. KEY POINTS: • Protein engineering is a powerful tool for generating thermostable industrial enzymes. • Directed evolution and rational design are practical approaches in enzyme engineering. • Substitutions in preferable structural positions can increase thermostability.

A synthetic tactic to substitute axial ligands in sterically demanding Ru(II)porphyrinates

We report a synthetic strategy that allows for the preparation of sterically encumbered heteroleptic Ru(II)porphyrinates with the desired configuration of stable/inert and weak/labile axial ligands to direct reactions between substrates to exclusively occur at the sterically encumbered face. To demonstrate the method, we describe the synthesis of a strapped-Ru(II)porphyrinate bearing a stable/inert triphenylphosphine (PPh₃) bulky axial ligand coordinated exo to the central cavity and a weak/labile methanol molecule coordinated at the internal axial position. With this axial ligand configuration, the reported Ru(II)porphyrinate exclusively promotes carbene transfer reactions to olefins through the central cavity, which has been verified by the selective formation of cycloprane-linked [2]rotaxanes.

Sequence and Structure-Guided Engineering of Urethanase from Agrobacterium tumefaciens d3 for Improved Catalytic Activity

The amidase from Agrobacterium tumefaciens d3 (AmdA) degrades the carcinogenic ethyl carbamate (EC) in alcoholic beverages. However, its limited catalytic activity hinders practical applications. Here, multiple sequence alignment was first used to predict single variants with improved activity. Afterward, AlphaFold 2 was applied to predict the three-dimensional structure of AmdA and 21 amino acids near the catalytic triad were randomized by saturation mutagenesis. Each of the mutation libraries was then screened, and the improved single variants were combined to obtain the best double variant I97L/G195A that showed a 3.1-fold increase in the urethanase activity and a 1.5-fold increase in ethanol tolerance. MD simulations revealed that the mutations shortened the distance between catalytic residues and the substrate and enhanced the occurrence of a critical hydrogen bond in the catalytic pocket. This study displayed a useful strategy to engineer an amidase for the improvement of urethanase activity, and the variant obtained provided a good candidate for applications in the food industry.

Computational design of an amidase by combining the best electrostatic features of two promiscuous hydrolases

While there has been emerging interest in designing new enzymes to solve practical challenges, computer-based options to redesign catalytically active proteins are rather limited. Here, a rational QM/MM molecular dynamics strategy based on combining the best electrostatic properties of enzymes with activity in a common reaction is presented. The computational protocol has been applied to the re-design of the protein scaffold of an existing promiscuous esterase from Bacillus subtilis Bs2 to enhance its secondary amidase activity. After the alignment of Bs2 with a non-homologous amidase Candida antarctica lipase B (CALB) within rotation quaternions, a relevant spatial aspartate residue of the latter was transferred to the former as a means to favor the electrostatics of transition state formation, where a clear separation of charges takes place. Deep computational insights, however, revealed a significant conformational change caused by the amino acid replacement, provoking a shift in the pK a of the inserted aspartate and counteracting the anticipated catalytic effect. This prediction was experimentally confirmed with a 1.3-fold increase in activity. The good agreement between theoretical and experimental results, as well as the linear correlation between the electrostatic properties and the activation energy barriers, suggest that the presented computational-based investigation can transform in an enzyme engineering approach.