Enzyme engineering: reaching the maximal catalytic efficiency peak

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

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

New advances in protein engineering for industrial applications: Key takeaways

Recent advancements in protein/enzyme engineering have enabled the production of a diverse array of high-value compounds in microbial systems with the potential for industrial applications. The goal of this review is to articulate some of the most recent protein engineering advances in bacteria, yeast, and other microbial systems to produce valuable substances. These high-value substances include α-farnesene, vitamin B12, fumaric acid, linalool, glucaric acid, carminic acid, mycosporine-like amino acids, patchoulol, orcinol glucoside, d-lactic acid, keratinase, α-glucanotransferases, β-glucosidase, seleno-methylselenocysteine, fatty acids, high-efficiency β-glucosidase enzymes, cellulase, β-carotene, physcion, and glucoamylase. Additionally, recent advances in enzyme engineering for enhancing thermostability will be discussed. These findings have the potential to revolutionize various industries, including biotechnology, food, pharmaceuticals, and biofuels.

Robust genetic codes enhance protein evolvability

The standard genetic code defines the rules of translation for nearly every life form on Earth. It also determines the amino acid changes accessible via single-nucleotide mutations, thus influencing protein evolvability-the ability of mutation to bring forth adaptive variation in protein function. One of the most striking features of the standard genetic code is its robustness to mutation, yet it remains an open question whether such robustness facilitates or frustrates protein evolvability. To answer this question, we use data from massively parallel sequence-to-function assays to construct and analyze 6 empirical adaptive landscapes under hundreds of thousands of rewired genetic codes, including those of codon compression schemes relevant to protein engineering and synthetic biology. We find that robust genetic codes tend to enhance protein evolvability by rendering smooth adaptive landscapes with few peaks, which are readily accessible from throughout sequence space. However, the standard genetic code is rarely exceptional in this regard, because many alternative codes render smoother landscapes than the standard code. By constructing low-dimensional visualizations of these landscapes, which each comprise more than 16 million mRNA sequences, we show that such alternative codes radically alter the topological features of the network of high-fitness genotypes. Whereas the genetic codes that optimize evolvability depend to some extent on the detailed relationship between amino acid sequence and protein function, we also uncover general design principles for engineering nonstandard genetic codes for enhanced and diminished evolvability, which may facilitate directed protein evolution experiments and the bio-containment of synthetic organisms, respectively.

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.

Opportunities and challenges in design and optimization of protein function

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

Protein structure-function continuum model: Emerging nexuses between specificity, evolution, and structure

The rationale for replacing the old binary of structure-function with the trinity of structure, disorder, and function has gained considerable ground in recent years. A continuum model based on the expanded form of the existing paradigm can now subsume importance of both conformational flexibility and intrinsic disorder in protein function. The disorder is actually critical for understanding the protein-protein interactions in many regulatory processes, formation of membrane-less organelles, and our revised notions of specificity as amply illustrated by moonlighting proteins. While its importance in formation of amyloids and function of prions is often discussed, the roles of intrinsic disorder in infectious diseases and protein function under extreme conditions are also becoming clear. This review is an attempt to discuss how our current understanding of protein function, specificity, and evolution fit better with the continuum model. This integration of structure and disorder under a single model may bring greater clarity in our continuing quest for understanding proteins and molecular mechanisms of their functionality.

Recent advances in microbial and enzymatic engineering for the biodegradation of micro- and nanoplastics

This review examines the escalating issue of plastic pollution, specifically highlighting the detrimental effects on the environment and human health caused by microplastics and nanoplastics. The extensive use of synthetic polymers such as polyethylene (PE), polyethylene terephthalate (PET), and polystyrene (PS) has raised significant environmental concerns because of their long-lasting and non-degradable characteristics. This review delves into the role of enzymatic and microbial strategies in breaking down these polymers, showcasing recent advancements in the field. The intricacies of enzymatic degradation are thoroughly examined, including the effectiveness of enzymes such as PETase and MHETase, as well as the contribution of microbial pathways in breaking down resilient polymers into more benign substances. The paper also discusses the impact of chemical composition on plastic degradation kinetics and emphasizes the need for an approach to managing the environmental impact of synthetic polymers. The review highlights the significance of comprehending the physical characteristics and long-term impacts of micro- and nanoplastics in different ecosystems. Furthermore, it points out the environmental and health consequences of these contaminants, such as their ability to cause cancer and interfere with the endocrine system. The paper emphasizes the need for advanced analytical methods and effective strategies for enzymatic degradation, as well as continued research and development in this area. This review highlights the crucial role of enzymatic and microbial strategies in addressing plastic pollution and proposes methods to create effective and environmentally friendly solutions.

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

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

Theoretical Improvements in Enzyme Efficiency Associated with Noisy Rate Constants and Increased Dissipation

Previous studies have revealed the extraordinarily large catalytic efficiency of some enzymes. High catalytic proficiency is an essential accomplishment of biological evolution. Natural selection led to the increased turnover number, kcat, and enzyme efficiency, kcat/KM, of uni-uni enzymes, which convert a single substrate into a single product. We added or multiplied random noise with chosen rate constants to explore the correlation between dissipation and catalytic efficiency for ten enzymes: beta-galactosidase, glucose isomerase, β-lactamases from three bacterial strains, ketosteroid isomerase, triosephosphate isomerase, and carbonic anhydrase I, II, and T200H. Our results highlight the role of biological evolution in accelerating thermodynamic evolution. The catalytic performance of these enzymes is proportional to overall entropy production-the main parameter from irreversible thermodynamics. That parameter is also proportional to the evolutionary distance of β-lactamases PC1, RTEM, and Lac-1 when natural or artificial evolution produces the optimal or maximal possible catalytic efficiency. De novo enzyme design and attempts to speed up the rate-limiting catalytic steps may profit from the described connection between kinetics and thermodynamics.

A broad horizon for sustainable catalytic oxidation of microplastics

Microplastics (MPs) have attracted tremendous attention due to their widespread appearance in the environment and biota, and their adverse effects on organisms. Since plastics are substantially produced to meet human needs, primary and secondary MPs are extensively trapped in wastewater treatment plants, freshwater, drinking water, ocean, air, and soil. The serious MPs pollution calls for efficient treatment strategies Herein, we discuss three catalytic processes (photocatalysis, electrocatalysis, and biocatalysis) for the sustainable management of MPs, and the relevant catalytic mechanisms are clarified. For photocatalysis, three categories (organic, inorganic, hybrid) of photocatalysts are listed, with degradation efficiency of 23%-100%. Next, relative impact factors on photocatalysis, such as characteristics of MPs and photocatalysts, are discussed. Then, some promising electrocatalysts for the degradation/conversion of (micro)plastics and standard electrolyzer designs are briefly introduced. This electrocatalytic method has achieved over 77% of Faradaic efficiency. Next, potential organisms with abundant biocatalysts for degrading different types of MPs are reviewed. Advances in three bioremediation techniques including biositimulation, bioaugmentation, and biosurfactant are outlined. Lastly, perspectives are put forward to promote scientific development in solving environmental issues on MPs pollution in broad fields. This paper provides insights into the development of next-generation techniques for MPs pollution management in a sustainable manner.

Enhancing luciferase activity and stability through generative modeling of natural enzyme sequences

The availability of natural protein sequences synergized with generative AI provides new paradigms to engineer enzymes. Although active enzyme variants with numerous mutations have been designed using generative models, their performance often falls short of their wild type counterparts. Additionally, in practical applications, choosing fewer mutations that can rival the efficacy of extensive sequence alterations is usually more advantageous. Pinpointing beneficial single mutations continues to be a formidable task. In this study, using the generative maximum entropy model to analyze Renilla luciferase (RLuc) homologs, and in conjunction with biochemistry experiments, we demonstrated that natural evolutionary information could be used to predictively improve enzyme activity and stability by engineering the active center and protein scaffold, respectively. The success rate to improve either luciferase activity or stability of designed single mutants is ~50%. This finding highlights nature's ingenious approach to evolving proficient enzymes, wherein diverse evolutionary pressures are preferentially applied to distinct regions of the enzyme, ultimately culminating in an overall high performance. We also reveal an evolutionary preference in RLuc toward emitting blue light that holds advantages in terms of water penetration compared to other light spectra. Taken together, our approach facilitates navigation through enzyme sequence space and offers effective strategies for computer-aided rational enzyme engineering.

Enhancing Luciferase Activity and Stability through Generative Modeling of Natural Enzyme Sequences

The availability of natural protein sequences synergized with generative artificial intelligence (AI) provides new paradigms to create enzymes. Although active enzyme variants with numerous mutations have been produced using generative models, their performance often falls short compared to their wild-type counterparts. Additionally, in practical applications, choosing fewer mutations that can rival the efficacy of extensive sequence alterations is usually more advantageous. Pinpointing beneficial single mutations continues to be a formidable task. In this study, using the generative maximum entropy model to analyze Renilla luciferase homologs, and in conjunction with biochemistry experiments, we demonstrated that natural evolutionary information could be used to predictively improve enzyme activity and stability by engineering the active center and protein scaffold, respectively. The success rate of designed single mutants is ~50% to improve either luciferase activity or stability. These finding highlights nature's ingenious approach to evolving proficient enzymes, wherein diverse evolutionary pressures are preferentially applied to distinct regions of the enzyme, ultimately culminating in an overall high performance. We also reveal an evolutionary preference in Renilla luciferase towards emitting blue light that holds advantages in terms of water penetration compared to other light spectrum. Taken together, our approach facilitates navigation through enzyme sequence space and offers effective strategies for computer-aided rational enzyme engineering.

Activation of oxidoreductases by the formation of enzyme assembly

Biological properties of protein molecules depend on their interaction with other molecules, and enzymes are no exception. Enzyme activities are controlled by their interaction with other molecules in living cells. Enzyme activation and their catalytic properties in the presence of different types of polymers have been studied in vitro, although these studies are restricted to only a few enzymes. In this study, we show that addition of poly-l-lysine (PLL) can increase the enzymatic activity of multiple oxidoreductases through formation of enzyme assemblies. Oxidoreductases with an overall negative charge, such as l-lactate oxidase, d-lactate dehydrogenase, pyruvate oxidase, and acetaldehyde dehydrogenase, each formed assemblies with the positively charged PLL via electrostatic interactions. The enzyme activities of these oxidoreductases in the enzyme assemblies were several-folds higher than those of the enzyme in their natural dispersed state. In the presence of PLL, the turnover number (kcat) improved for all enzymes, whereas the decrease in Michaelis constant (KM) was enzyme dependent. This type of enzyme function regulation through the formation of assemblies via simple addition of polymers has potential for diverse applications, including various industrial and research purposes.

Efficient Myrcene Production Using Linalool Dehydratase Isomerase and Rational Biochemical Process in Escherichia coli

Microbial synthesis of plant-based myrcene is of great interest because of its high demand, however, achieving high biosynthetic titers remains a great challenge. Previous strategies adopted for microbial myrcene production have relied on the recruitment of a multi-step biosynthetic pathway which requires complex metabolic regulation or high activity of myrcene synthase, hindering its application. Here, we present an effective one-step biotransformation system for myrcene biosynthesis from geraniol, using a linalool dehydratase isomerase (LDI) to overcome these limitations. The truncated LDI possesses nominal activity that catalyzes the isomerization of geraniol to linalool and the subsequent dehydration to myrcene in anaerobic environment. In order to improve the robustness of engineered strains for the efficient conversion of geraniol to myrcene, rational enzyme modification and a series of biochemical process engineering were employed to maintain and improve the anaerobic catalytic activity of LDI. Finally, by introducing the optimized myrcene biosynthetic capability in the existing geraniol-production strain, we achieve de novo biosynthesis of myrcene at 1.25g/L from glycerol during 84h aerobic-anaerobic two-stage fermentation, which is much higher than previously reported myrcene levels. This work highlights the value of dehydratase isomerase-based biocatalytic in establishing novel biosynthetic pathways and lays a reliable foundation for the microbial synthesis of myrcene.

De Novo Computational Design of a Lipase with Hydrolysis Activity towards Middle-Chained Fatty Acid Esters

Innovations in biocatalysts provide great prospects for intolerant environments or novel reactions. Due to the limited catalytic capacity and the long-term and labor-intensive characteristics of mining enzymes with the desired functions, de novo enzyme design was developed to obtain industrial application candidates in a rapid and convenient way. Here, based on the catalytic mechanisms and the known structures of proteins, we proposed a computational protein design strategy combining de novo enzyme design and laboratory-directed evolution. Starting with the theozyme constructed using a quantum-mechanical approach, the theoretical enzyme-skeleton combinations were assembled and optimized via the Rosetta "inside-out" protocol. A small number of designed sequences were experimentally screened using SDS-PAGE, mass spectrometry and a qualitative activity assay in which the designed enzyme 1a8uD₁ exhibited a measurable hydrolysis activity of 24.25 ± 0.57 U/g towards p-nitrophenyl octanoate. To improve the activity of the designed enzyme, molecular dynamics simulations and the RosettaDesign application were utilized to further optimize the substrate binding mode and amino acid sequence, thus keeping the residues of theozyme intact. The redesigned lipase 1a8uD₁-M8 displayed enhanced hydrolysis activity towards p-nitrophenyl octanoate-3.34 times higher than that of 1a8uD₁. Meanwhile, the natural skeleton protein (PDB entry 1a8u) did not display any hydrolysis activity, confirming that the hydrolysis abilities of the designed 1a8uD₁ and the redesigned 1a8uD₁-M8 were devised from scratch. More importantly, the designed 1a8uD₁-M8 was also able to hydrolyze the natural middle-chained substrate (glycerol trioctanoate), for which the activity was 27.67 ± 0.69 U/g. This study indicates that the strategy employed here has great potential to generate novel enzymes exhibiting the desired reactions.

Moonlighting enzymes: when cellular context defines specificity

It is not often realized that the absolute protein specificity is an exception rather than a rule. Two major kinds of protein multi-specificities are promiscuity and moonlighting. This review discusses the idea of enzyme specificity and then focusses on moonlighting. Some important examples of protein moonlighting, such as crystallins, ceruloplasmin, metallothioniens, macrophage migration inhibitory factor, and enzymes of carbohydrate metabolism are discussed. How protein plasticity and intrinsic disorder enable the removing the distinction between enzymes and other biologically active proteins are outlined. Finally, information on important roles of moonlighting in human diseases is updated.

A Genetic Programming Approach to Engineering MRI Reporter Genes

Here we develop a mechanism of protein optimization using a computational approach known as "genetic programming". We developed an algorithm called Protein Optimization Engineering Tool (POET). Starting from a small library of literature values, the use of this tool allowed us to develop proteins that produce four times more MRI contrast than what was previously state-of-the-art. Interestingly, many of the peptides produced using POET were dramatically different with respect to their sequence and chemical environment than existing CEST producing peptides, and challenge prior understandings of how those peptides function. While existing algorithms for protein engineering rely on divergent evolution, POET relies on convergent evolution and consequently allows discovery of peptides with completely different sequences that perform the same function with as good or even better efficiency. Thus, this novel approach can be expanded beyond developing imaging agents and can be used widely in protein engineering.

Designer installation of a substrate recruitment domain to tailor enzyme specificity

Promiscuous enzymes that modify peptides and proteins are powerful tools for labeling biomolecules; however, directing these modifications to desired substrates can be challenging. Here, we use computational interface design to install a substrate recognition domain adjacent to the active site of a promiscuous enzyme, catechol O-methyltransferase. This design approach effectively decouples substrate recognition from the site of catalysis and promotes modification of peptides recognized by the recruitment domain. We determined the crystal structure of this novel multidomain enzyme, SH3-588, which shows that it closely matches our design. SH3-588 methylates directed peptides with catalytic efficiencies exceeding the wild-type enzyme by over 1,000-fold, whereas peptides lacking the directing recognition sequence do not display enhanced efficiencies. In competition experiments, the designer enzyme preferentially modifies directed substrates over undirected substrates, suggesting that we can use designed recruitment domains to direct post-translational modifications to specific sequence motifs on target proteins in complex multisubstrate environments.

Machine learning for metabolic pathway optimization: A review

Optimizing the metabolic pathways of microbial cell factories is essential for establishing viable biotechnological production processes. However, due to the limited understanding of the complex setup of cellular machinery, building efficient microbial cell factories remains tedious and time-consuming. Machine learning (ML), a powerful tool capable of identifying patterns within large datasets, has been used to analyze biological datasets generated using various high-throughput technologies to build data-driven models for complex bioprocesses. In addition, ML can also be integrated with Design-Build-Test-Learn to accelerate development. This review focuses on recent ML applications in genome-scale metabolic model construction, multistep pathway optimization, rate-limiting enzyme engineering, and gene regulatory element designing. In addition, we have discussed some limitations of these methods as well as potential solutions.

Enlightening the Path to Protein Engineering: Chemoselective Turn-On Probes for High-Throughput Screening of Enzymatic Activity

Many successful stories in enzyme engineering are based on the creation of randomized diversity in large mutant libraries, containing millions to billions of enzyme variants. Methods that enabled their evaluation with high throughput are dominated by spectroscopic techniques due to their high speed and sensitivity. A large proportion of studies relies on fluorogenic substrates that mimic the chemical properties of the target or coupled enzymatic assays with an optical read-out that assesses the desired catalytic efficiency indirectly. The most reliable hits, however, are achieved by screening for conversions of the starting material to the desired product. For this purpose, functional group assays offer a general approach to achieve a fast, optical read-out. They use the chemoselectivity, differences in electronic and steric properties of various functional groups, to reduce the number of false-positive results and the analytical noise stemming from enzymatic background activities. This review summarizes the developments and use of functional group probes for chemoselective derivatizations, with a clear focus on screening for enzymatic activity in protein engineering.

Evo-devo beyond development: Generalizing evo-devo to all levels of the phenotypic evolution

A foundational idea of evo-devo is that morphological variation is not isotropic, that is, it does not occur in all directions. Instead, some directions of morphological variation are more likely than others from DNA-level variation and these largely depend on development. We argue that this evo-devo perspective should apply not only to morphology but to evolution at all phenotypic levels. At other phenotypic levels there is no development, but there are processes that can be seen, in analogy to development, as constructing the phenotype (e.g., protein folding, learning for behavior, etc.). We argue that to explain the direction of evolution two types of arguments need to be combined: generative arguments about which phenotypic variation arises in each generation and selective arguments about which of it passes to the next generation. We explain how a full consideration of the two types of arguments improves the explanatory power of evolutionary theory. Also see the video abstract here: https://youtu.be/Egbvma_uaKc.

Activation of L-lactate oxidase by the formation of enzyme assemblies through liquid-liquid phase separation

The assembly state of enzymes is gaining interest as a mechanism for regulating the function of enzymes in living cells. One of the current topics in enzymology is the relationship between enzyme activity and the assembly state due to liquid-liquid phase separation. In this study, we demonstrated enzyme activation via the formation of enzyme assemblies using L-lactate oxidase (LOX). LOX formed hundreds of nanometer-scale assemblies with poly-L-lysine (PLL). In the presence of ammonium sulfate, the LOX-PLL clusters formed micrometer-scale liquid droplets. The enzyme activities of LOX in clusters and droplets were one order of magnitude higher than those in the dispersed state, owing to a decrease in K_M and an increase in k_cat. Moreover, the clusters exhibited a higher activation effect than the droplets. In addition, the conformation of LOX changed in the clusters, resulting in increased enzyme activation. Understanding enzyme activation and assembly states provides important information regarding enzyme function in living cells, in addition to biotechnology applications.

Toward real-world automated antibody design with combinatorial Bayesian optimization

Antibodies are multimeric proteins capable of highly specific molecular recognition. The complementarity determining region 3 of the antibody variable heavy chain (CDRH3) often dominates antigen-binding specificity. Hence, it is a priority to design optimal antigen-specific CDRH3 to develop therapeutic antibodies. The combinatorial structure of CDRH3 sequences makes it impossible to query binding-affinity oracles exhaustively. Moreover, antibodies are expected to have high target specificity and developability. Here, we present AntBO, a combinatorial Bayesian optimization framework utilizing a CDRH3 trust region for an in silico design of antibodies with favorable developability scores. The in silico experiments on 159 antigens demonstrate that AntBO is a step toward practically viable in vitro antibody design. In under 200 calls to the oracle, AntBO suggests antibodies outperforming the best binding sequence from 6.9 million experimentally obtained CDRH3s. Additionally, AntBO finds very-high-affinity CDRH3 in only 38 protein designs while requiring no domain knowledge.

Insertions and Deletions (Indels): A Missing Piece of the Protein Engineering Jigsaw

Over the years, protein engineers have studied nature and borrowed its tricks to accelerate protein evolution in the test tube. While there have been considerable advances, our ability to generate new proteins in the laboratory is seemingly limited. One explanation for these shortcomings may be that insertions and deletions (indels), which frequently arise in nature, are largely overlooked during protein engineering campaigns. The profound effect of indels on protein structures, by way of drastic backbone alterations, could be perceived as "saltation" events that bring about significant phenotypic changes in a single mutational step. Should we leverage these effects to accelerate protein engineering and gain access to unexplored regions of adaptive landscapes? In this Perspective, we describe the role played by indels in the functional diversification of proteins in nature and discuss their untapped potential for protein engineering, despite their often-destabilizing nature. We hope to spark a renewed interest in indels, emphasizing that their wider study and use may prove insightful and shape the future of protein engineering by unlocking unique functional changes that substitutions alone could never achieve.

Efficient Exploration of Sequence Space by Sequence-Guided Protein Engineering and Design

The rapid growth of sequence databases over the past two decades means that protein engineers faced with optimizing a protein for any given task will often have immediate access to a vast number of related protein sequences. These sequences encode information about the evolutionary history of the protein and the underlying sequence requirements to produce folded, stable, and functional protein variants. Methods that can take advantage of this information are an increasingly important part of the protein engineering tool kit. In this Perspective, we discuss the utility of sequence data in protein engineering and design, focusing on recent advances in three main areas: the use of ancestral sequence reconstruction as an engineering tool to generate thermostable and multifunctional proteins, the use of sequence data to guide engineering of multipoint mutants by structure-based computational protein design, and the use of unlabeled sequence data for unsupervised and semisupervised machine learning, allowing the generation of diverse and functional protein sequences in unexplored regions of sequence space. Altogether, these methods enable the rapid exploration of sequence space within regions enriched with functional proteins and therefore have great potential for accelerating the engineering of stable, functional, and diverse proteins for industrial and biomedical applications.

Supramolecular enzyme-mimicking catalysts self-assembled from peptides

Natural enzymes catalyze biochemical transformations in superior catalytic efficiency and remarkable substrate specificity. The excellent catalytic repertoire of enzymes is attributed to the sophisticated chemical structures of their active sites, as a result of billions-of-years natural evolution. However, large-scale practical applications of natural enzymes are restricted due to their poor stability, difficulty in modification, and high costs of production. One viable solution is to fabricate supramolecular catalysts with enzyme-mimetic active sites. In this review, we introduce the principles and strategies of designing peptide-based artificial enzymes which display catalytic activities similar to those of natural enzymes, such as aldolases, laccases, peroxidases, and hydrolases (mainly the esterases and phosphatases). We also discuss some multifunctional enzyme-mimicking systems which are capable of catalyzing orthogonal or cascade reactions. We highlight the relationship between structures of enzyme-like active sites and the catalytic properties, as well as the significance of these studies from an evolutionary point of view.

Nature-inspired engineering of an artificial ligase enzyme by domain fusion

The function of most proteins is accomplished through the interplay of two or more protein domains and fine-tuned by natural evolution. In contrast, artificial enzymes have often been engineered from a single domain scaffold and frequently have lower catalytic activity than natural enzymes. We previously generated an artificial enzyme that catalyzed an RNA ligation by >2 million-fold but was likely limited in its activity by low substrate affinity. Inspired by nature's concept of domain fusion, we fused the artificial enzyme to a series of protein domains known to bind nucleic acids with the goal of improving its catalytic activity. The effect of the fused domains on catalytic activity varied greatly, yielding severalfold increases but also reductions caused by domains that previously enhanced nucleic acid binding in other protein engineering projects. The combination of the two better performing binding domains improved the activity of the parental ligase by more than an order of magnitude. These results demonstrate for the first time that nature's successful evolutionary mechanism of domain fusion can also improve an unevolved primordial-like protein whose structure and function had just been created in the test tube. The generation of multi-domain proteins might therefore be an ancient evolutionary process.

Bypassing evolutionary dead ends and switching the rate-limiting step of a human immunotherapeutic enzyme

The Trp metabolite kynurenine (KYN) accumulates in numerous solid tumours and mediates potent immunosuppression. Bacterial kynureninases (KYNases), which preferentially degrade kynurenine, can relieve immunosuppression in multiple cancer models, but immunogenicity concerns preclude their clinical use, while the human enzyme (HsKYNase) has very low activity for kynurenine and shows no therapeutic effect. Using fitness selections, we evolved a HsKYNase variant with 27-fold higher activity, beyond which exploration of >30 evolutionary trajectories involving the interrogation of >109 variants led to no further improvements. Introduction of two amino acid substitutions conserved in bacterial KYNases reduced enzyme fitness but potentiated rapid evolution of variants with ~500-fold improved activity and reversed substrate specificity, resulting in an enzyme capable of mediating strong anti-tumour effects in mice. Pre-steady-state kinetics revealed a switch in rate-determining step attributable to changes in both enzyme structure and conformational dynamics. Apart from its clinical significance, our work highlights how rationally designed substitutions can potentiate trajectories that overcome barriers in protein evolution.

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+.

Enzyme directed evolution using genetically encodable biosensors

Directed evolution has been remarkably successful in identifying enzyme variants with new or improved properties, such as altered substrate scope or novel reactivity. Genetically encodable biosensors (GEBs), which convert the concentration of a small molecule ligand into an easily detectable output signal, have seen increasing application to enzyme directed evolution in the last decade. GEBs enable the use of high-throughput methods to assess enzyme activity of very large libraries, which can accelerate the search for variants with desirable activity. Here, we review different classes of GEBs and their properties in the context of enzyme evolution, how GEBs have been integrated into directed evolution workflows, and recent examples of enzyme evolution efforts utilizing GEBs. Finally, we discuss the advantages, challenges, and opportunities for using GEBs in the directed evolution of enzymes.

A combination of bioinformatics analysis and rational design strategies to enhance keratinase thermostability for efficient biodegradation of feathers

Keratinase has shown great significance and application potentials in the biodegradation and recycle of keratin waste due to its unique and efficient hydrolysis ability. However, the inherent instability of the enzyme limits its practical utilization. Herein, we obtained a thermostability-enhanced keratinase based on a combination of bioinformatics analysis and rational design strategies for the efficient biodegradation of feathers. A systematical in silico analysis combined with filtering of virtual libraries derived a smart library for experimental validation. Synergistic mutations around the highly flexible loop, the calcium binding site and the non-consensus amino acids generated a dominant mutant which increased the optimal temperature of keratinase from 40 °C to 60 °C, and the half-life at 60 °C was increased from 17.3 min to 66.1 min. The mutant could achieve more than 66% biodegradation of 50 g/L feathers to high-valued keratin product with a major molecular weight of 36 kDa. Collectively, this work provided a promising keratinase variant with enhanced thermostability for efficient conversion of keratin wastes to valuable products. It also generated a general strategy to facilitate enzyme thermostability design which is more targeted and predictable.

Computational Design of Homotetrameric Peptide Bundle Variants Spanning a Wide Range of Charge States

With the ability to design their sequences and structures, peptides can be engineered to realize a wide variety of functionalities and structures. Herein, computational design was used to identify a set of 17 peptides having a wide range of putative charge states but the same tetrameric coiled-coil bundle structure. Calculations were performed to identify suitable locations for ionizable residues (D, E, K, and R) at the bundle's exterior sites, while interior hydrophobic interactions were retained. The designed bundle structures spanned putative charge states of -32 to +32 in units of electron charge. The peptides were experimentally investigated using spectroscopic and scattering techniques. Thermal stabilities of the bundles were investigated using circular dichroism. Molecular dynamics simulations assessed structural fluctuations within the bundles. The cylindrical peptide bundles, 4 nm long by 2 nm in diameter, were covalently linked to form rigid, micron-scale polymers and characterized using transmission electron microscopy. The designed suite of sequences provides a set of readily realized nanometer-scale structures of tunable charge that can also be polymerized to yield rigid-rod polyelectrolytes.

Making the cut with protease engineering

Proteases cut with enviable precision and regulate diverse molecular events in biology. Such qualities drive a seemingly inexhaustible appetite for proteases with new activities and capabilities. Comprising 25% of the total industrial enzyme market, proteases appear in consumer goods, such as detergents, textile processing, and numerous foods; additionally, proteases include 25 US Food and Drug Administration-approved medicines and various research tools. Recent advances in protease engineering strategies address target specificity, catalytic efficiency, and stability. This guide to protease engineering surveys best practices and emerging strategies. We further highlight gaps and flexibilities inherent to each system that suggest opportunities for new technology development along with engineered proteases to solve challenges in proteomics, protein sequencing, and synthetic gene circuits.

Facilitating Machine Learning-Guided Protein Engineering with Smart Library Design and Massively Parallel Assays

Protein design plays an important role in recent medical advances from antibody therapy to vaccine design. Typically, exhaustive mutational screens or directed evolution experiments are used for the identification of the best design or for improvements to the wild-type variant. Even with a high-throughput screening on pooled libraries and Next-Generation Sequencing to boost the scale of read-outs, surveying all the variants with combinatorial mutations for their empirical fitness scores is still of magnitudes beyond the capacity of existing experimental settings. To tackle this challenge, in-silico approaches using machine learning to predict the fitness of novel variants based on a subset of empirical measurements are now employed. These machine learning models turn out to be useful in many cases, with the premise that the experimentally determined fitness scores and the amino-acid descriptors of the models are informative. The machine learning models can guide the search for the highest fitness variants, resolve complex epistatic relationships, and highlight bio-physical rules for protein folding. Using machine learning-guided approaches, researchers can build more focused libraries, thus relieving themselves from labor-intensive screens and fast-tracking the optimization process. Here, we describe the current advances in massive-scale variant screens, and how machine learning and mutagenesis strategies can be integrated to accelerate protein engineering. More specifically, we examine strategies to make screens more economical, informative, and effective in discovery of useful variants.

A new biomimetic nanozyme of hemin/graphdiyne oxide with superior peroxidase-like activity for colorimetric bioassays

Graphdiyne oxide (GDYO) is a novel type of two-dimensional carbon allotrope nanomaterial consisting of a large conjugated system and excellent chemical stability. To date, application of GDYO as a nanozyme in biosensing has been rarely reported. In this study, a novel ultrasensitive colorimetric bioassay was constructed using a hemin/GDYO nanocomposite (H/GDYO) as a new nanozyme with superior peroxidase-like activity for the detection of H₂O₂ and glucose. It was discovered that H/GDYO exhibited 6-fold higher peroxidase-like activity than pure hemin. Catalytic kinetic analysis showed that H/GDYO had a much higher affinity for H₂O₂ and glucose than that of hemin. The designed colorimetric bioassay displayed excellent sensitivity for H₂O₂ and glucose detection with a wide linear range of 0.015-0.5 mM and 0.1-10 mM, respectively, while the limit of detection (LOD) was as low as 4.39 μM and 38 μM, respectively. Moreover, it was successfully applied for the analysis of H₂O₂ in milk and glucose in real human serum samples with acceptable recoveries. Importantly, the developed colorimetric bioassay shows good agreement with the results obtained from a commercial blood glucose meter. We believe that the proposed method could provide a promising prospect for medical diagnosis and biotechnology.

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.

The Unexplored Importance of Fleeting Chiral Intermediates in Enzyme-Catalyzed Reactions

Decades of extensive research efforts by biochemists, organic chemists, and protein engineers have led to an understanding of the basic mechanisms of essentially all known types of enzymes, but in a formidable number of cases an essential aspect has been overlooked. The occurrence of short-lived chiral intermediates formed by symmetry-breaking of prochiral precursors in enzyme catalyzed reactions has been systematically neglected. We designate these elusive species as fleeting chiral intermediates and analyze such crucial questions as "Do such intermediates occur in homochiral form?" If so, what is the absolute configuration, and why did Nature choose that particular stereoisomeric form, even when the isolable final product may be achiral? Does the absolute configuration of a chiral product depend in any way on the absolute configuration of the fleeting chiral precursor? How does this affect the catalytic proficiency of the enzyme? If these issues continue to be unexplored, then an understanding of the mechanisms of many enzyme types remains incomplete. We have systematized the occurrence of these chiral intermediates according to their structures and enzyme types. This is followed by critical analyses of selected case studies and by final conclusions and perspectives. We hope that the fascinating concept of fleeting chiral intermediates will attract the attention of scientists, thereby opening an exciting new research field.

Rational engineering strategies for achieving high-yield, high-quality and high-stability of natural product production in actinomycetes

Actinomycetes are recognized as excellent producers of microbial natural products, which have a wide range of applications, especially in medicine, agriculture and stockbreeding. The three main indexes of industrialization (titer, purity and stability) must be taken into overall consideration in the manufacturing process of natural products. Over the past decades, synthetic biology techniques have expedited the development of industrially competitive strains with excellent performances. Here, we summarize various rational engineering strategies for upgrading the performance of industrial actinomycetes, which include enhancing the yield of natural products, eliminating the by-products and improving the genetic stability of engineered strains. Furthermore, the current challenges and future perspectives for optimizing the industrial strains more systematically through combinatorial engineering strategies are also discussed.

Random Mutagenesis by Insertion of Error-Prone PCR Products to the Chromosome of Bacillus subtilis

Bacillus subtilis is an attractive host for the directed evolution of the enzymes whose substrates cannot be transported across cell membrane. However, the generation of a mutant library in B. subtilis suffers problems of small library size, plasmid instability, and heterozygosity. Here, a large library of random mutant was created by inserting error-prone PCR (epPCR) products to the chromosome of B. subtilis. Specifically, the epPCR product was fused with flanking regions and antibiotic resistant marker using a PCR-based multimerization method, generating insertion construct. The epPCR product was integrated into the chromosome via homologous recombination after the insertion construct was transformed into the supercompetent cells of B. subtilis strain SCK6. The transformation efficiency of the insertion construct was improved through co-expressing homologous recombination-promoting protein NgAgo, raising the number of competent cells, and increasing the length of flanking regions. A library containing 5.31 × 105 random mutants was constructed using per μg insertion construct, which is sufficient for directed evolution. The library generation process was accomplished within 1 day. The effectiveness of this method was confirmed by improving the activity of Methyl Parathion Hydrolase (MPH) toward chlorpyrifos and by enhancing the secretion level of MPH in B. subtilis. Taken together, the present work provides a fast and efficient method to integrate epPCR products into the chromosome of B. subtilis, facilitating directed evolution and expression optimization of target proteins.

Discovery, Design, and Structural Characterization of Alkane-Producing Enzymes across the Ferritin-like Superfamily

To complement established rational and evolutionary protein design approaches, significant efforts are being made to utilize computational modeling and the diversity of naturally occurring protein sequences. Here, we combine structural biology, genomic mining, and computational modeling to identify structural features critical to aldehyde deformylating oxygenases (ADOs), an enzyme family that has significant implications in synthetic biology and chemoenzymatic synthesis. Through these efforts, we discovered latent ADO-like function across the ferritin-like superfamily in various species of Bacteria and Archaea. We created a machine learning model that uses protein structural features to discriminate ADO-like activity. Computational enzyme design tools were then utilized to introduce ADO-like activity into the small subunit of Escherichia coli class I ribonucleotide reductase. The integrated approach of genomic mining, structural biology, molecular modeling, and machine learning has the potential to be utilized for rapid discovery and modulation of functions across enzyme families.

Phylogeny and Structure of Fatty Acid Photodecarboxylases and Glucose-Methanol-Choline Oxidoreductases

Glucose-methanol-choline (GMC) oxidoreductases are a large and diverse family of flavin-binding enzymes found in all kingdoms of life. Recently, a new related family of proteins has been discovered in algae named fatty acid photodecarboxylases (FAPs). These enzymes use the energy of light to convert fatty acids to the corresponding Cn-1 alkanes or alkenes, and hold great potential for biotechnological application. In this work, we aimed at uncovering the natural diversity of FAPs and their relations with other GMC oxidoreductases. We reviewed the available GMC structures, assembled a large dataset of GMC sequences, and found that one active site amino acid, a histidine, is extremely well conserved among the GMC proteins but not among FAPs, where it is replaced with alanine. Using this criterion, we found several new potential FAP genes, both in genomic and metagenomic databases, and showed that related bacterial, archaeal and fungal genes are unlikely to be FAPs. We also identified several uncharacterized clusters of GMC-like proteins as well as subfamilies of proteins that lack the conserved histidine but are not FAPs. Finally, the analysis of the collected dataset of potential photodecarboxylase sequences revealed the key active site residues that are strictly conserved, whereas other residues in the vicinity of the flavin adenine dinucleotide (FAD) cofactor and in the fatty acid-binding pocket are more variable. The identified variants may have different FAP activity and selectivity and consequently may prove useful for new biotechnological applications, thereby fostering the transition from a fossil carbon-based economy to a bio-economy by enabling the sustainable production of hydrocarbon fuels.

Potential for Applying Continuous Directed Evolution to Plant Enzymes: An Exploratory Study

Plant evolution has produced enzymes that may not be optimal for maximizing yield and quality in today's agricultural environments and plant biotechnology applications. By improving enzyme performance, it should be possible to alleviate constraints on yield and quality currently imposed by kinetic properties or enzyme instability. Enzymes can be optimized more quickly than naturally possible by applying directed evolution, which entails mutating a target gene in vitro and screening or selecting the mutated gene products for the desired characteristics. Continuous directed evolution is a more efficient and scalable version that accomplishes the mutagenesis and selection steps simultaneously in vivo via error-prone replication of the target gene and coupling of the host cell's growth rate to the target gene's function. However, published continuous systems require custom plasmid assembly, and convenient multipurpose platforms are not available. We discuss two systems suitable for continuous directed evolution of enzymes, OrthoRep in Saccharomyces cerevisiae and EvolvR in Escherichia coli, and our pilot efforts to adapt each system for high-throughput plant enzyme engineering. To test our modified systems, we used the thiamin synthesis enzyme THI4, previously identified as a prime candidate for improvement. Our adapted OrthoRep system shows promise for efficient plant enzyme engineering.

De novo engineering of intracellular condensates using artificial disordered proteins

Phase separation of intrinsically disordered proteins (IDPs) is a remarkable feature of living cells to dynamically control intracellular partitioning. Despite the numerous new IDPs that have been identified, progress towards rational engineering in cells has been limited. To address this limitation, we systematically scanned the sequence space of native IDPs and designed artificial IDPs (A-IDPs) with different molecular weights and aromatic content, which exhibit variable condensate saturation concentrations and temperature cloud points in vitro and in cells. We created A-IDP puncta using these simple principles, which are capable of sequestering an enzyme and whose catalytic efficiency can be manipulated by the molecular weight of the A-IDP. These results provide a robust engineered platform for creating puncta with new, phase-separation-mediated control of biological function in living cells.

A mechanistic view of enzyme evolution

New enzyme functions often evolve through the recruitment and optimization of latent promiscuous activities. How do mutations alter the molecular architecture of enzymes to enhance their activities? Can we infer general mechanisms that are common to most enzymes, or does each enzyme require a unique optimization process? The ability to predict the location and type of mutations necessary to enhance an enzyme's activity is critical to protein engineering and rational design. In this review, via the detailed examination of recent studies that have shed new light on the molecular changes underlying the optimization of enzyme function, we provide a mechanistic perspective of enzyme evolution. We first present a global survey of the prevalence of activity-enhancing mutations and their distribution within protein structures. We then delve into the molecular solutions that mediate functional optimization, specifically highlighting several common mechanisms that have been observed across multiple examples. As distinct protein sequences encounter different evolutionary bottlenecks, different mechanisms are likely to emerge along evolutionary trajectories toward improved function. Identifying the specific mechanism(s) that need to be improved upon, and tailoring our engineering efforts to each sequence, may considerably improve our chances to succeed in generating highly efficient catalysts in the future.

How Do Cells Adapt? Stories Told in Landscapes

Cells adapt to changing environments. Perturb a cell and it returns to a point of homeostasis. Perturb a population and it evolves toward a fitness peak. We review quantitative models of the forces of adaptation and their visualizations on landscapes. While some adaptations result from single mutations or few-gene effects, others are more cooperative, more delocalized in the genome, and more universal and physical. For example, homeostasis and evolution depend on protein folding and aggregation, energy and protein production, protein diffusion, molecular motor speeds and efficiencies, and protein expression levels. Models provide a way to learn about the fitness of cells and cell populations by making and testing hypotheses.