Directed enzyme evolution: climbing fitness peaks one amino acid at a time

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.

Mechanistic conformational and substrate selectivity profiles emerging in the evolution of enzymes via parallel trajectories

Laboratory evolution studies have demonstrated that parallel evolutionary trajectories can lead to genetically distinct enzymes with high activity towards a non-preferred substrate. However, it is unknown whether such enzymes have convergent conformational dynamics and mechanistic features. To address this question, we use as a model the wild-type Homo sapiens kynureninase (HsKYNase), which is of great interest for cancer immunotherapy. Earlier, we isolated HsKYNase_66 through an unusual evolutionary trajectory, having a 410-fold increase in the kcat/KM for kynurenine (KYN) and reverse substrate selectivity relative to HsKYNase. Here, by following a different evolutionary trajectory we generate a genetically distinct variant, HsKYNase_93D9, that exhibits KYN catalytic activity comparable to that of HsKYNase_66, but instead it is a "generalist" that accepts 3'-hydroxykynurenine (OH-KYN) with the same proficiency. Pre-steady-state kinetic analysis reveals that while the evolution of HsKYNase_66 is accompanied by a change in the rate-determining step of the reactions, HsKYNase_93D9 retains the same catalytic mechanism as HsKYNase. HDX-MS shows that the conformational dynamics of the two enzymes are markedly different and distinct from ortholog prokaryotic enzymes with high KYN activity. Our work provides a mechanistic framework for understanding the relationship between evolutionary mechanisms and phenotypic traits of evolved generalist and specialist enzyme species.

Diverse models of cavity engineering in enzyme modification: Creation, filling, and reshaping

Most enzyme modification strategies focus on designing the active sites or their surrounding structures. Interestingly, a large portion of the enzymes (60%) feature active sites located within spacious cavities. Despite recent discoveries, cavity-mediated enzyme engineering remains crucial for enhancing enzyme properties and unraveling folding-unfolding mechanisms. Cavity engineering influences enzyme stability, catalytic activity, specificity, substrate recognition, and docking. This article provides a comprehensive review of various cavity engineering models for enzyme modification, including cavity creation, filling, and reshaping. Additionally, it also discusses feasible tools for geometric analysis, functional assessment, and modification of cavities, and explores potential future research directions in this field. Furthermore, a promising universal modification strategy for cavity engineering that leverages state-of-the-art technologies and methodologies to tailor cavities according to the specific requirements of industrial production conditions is proposed.

Ultrahigh Throughput Evolution of Tryptophan Synthase in Droplets via an Aptamer Sensor

Tryptophan synthase catalyzes the synthesis of a wide array of noncanonical amino acids and is an attractive target for directed evolution. Droplet microfluidics offers an ultrahigh throughput approach to directed evolution (up to 107 experiments per day), enabling the search for biocatalysts in wider regions of sequence space with reagent consumption minimized to the picoliter volume (per library member). While the majority of screening campaigns in this format on record relied on an optically active reaction product, a new assay is needed for tryptophan synthase. Tryptophan is not fluorogenic in the visible light spectrum and thus falls outside the scope of conventional droplet microfluidic readouts, which are incompatible with UV light detection at high throughput. Here, we engineer a tryptophan DNA aptamer into a sensor to quantitatively report on tryptophan production in droplets. The utility of the sensor was validated by identifying five-fold improved tryptophan synthases from ∼100,000 protein variants. More generally, this work establishes the use of DNA-aptamer sensors with a fluorogenic read-out in widening the scope of droplet microfluidic evolution.

Screening of Chitinolytic Microfungi and Optimization of Parameters for Hyperproduction of Chitinase Through Solid-State Fermentation Technique

This study is intended for the production of chitinase enzyme from locally isolated fungal strains. Out of 10 isolated fungal strains from district Gujrat, Punjab, Pakistan, Aspergillus terreus SB3 (accession number ON738571) was found with maximum chitinolytic potential (80.8 U/mL/min). By applying central composite design (CCD) through response surface methodology (RSM) under solid-state fermentation (SSF), eight nutritional and physical parameters were optimized. Among these, temperature, substrate concentration, and pH were found as significant factors toward chitinase production in the first phase. Moisture and nitrogen source were found as significant factors during second phase of chitinase production. The effect of incubation period, inoculum size, and magnesium source was observed as non-significant. The chitinase activity was successfully enhanced more than 2 folds up to 198.5 U/mL/min at optimized conditions of 35 °C temperature, 4.5 pH, 20 g substrate concentration, 4-day incubation period, 55% moisture content, 4.5 mL inoculum size, 0.25 g ammonium sulfate, and 0.30 g magnesium sulfate using RSM design. It was also found that Ganoderma lucidum (bracket fungus) has more potential to be used for the production of chitinase compared to fish scales. The present study exhibited Aspergillus terreus SB3 (ON738571) as a potential indigenous strain capable for hyperproduction of chitinase through cheap fermentation technology that might be employed for the eradication of chitin-based sea waste to remove the marine pollution.

Rational and random mutagenesis of GDEst-95 carboxylesterase: New functionality insights

Lipolytic enzymes are important contributors in industrial processes from lipid hydrolysis to biofuel production or even polyester biodegradation. While these enzymes can be used in numerous applications, the genotype-phenotype space of certain promising enzymes is still poorly explored. This limits the effective application of such biocatalysts. In this work the genotype space of a 55 kDa carboxylesterase GDEst-95 from Geobacillus sp. 95 was explored using site-directed mutagenesis and directed evolution methods. In this study four site-directed mutants (Gly108Arg, Ala410Arg, Leu226Arg, Leu411Ala) were created based on previous analysis of GDEst-95 carboxylesterase. Error-prone PCR resulted three mutants: two of them with distal mutations: GDEst-RM1 (Arg75Gln), GDEst-RM2 (Gly20Ser Arg75Gln) and the third, GDEst-RM3, with a distal (Ser210Gly) and Tyr317Ala (amino acid position near to the active site) mutation. Mutants with Ala substitution displayed approximately twofold higher specific activity. Arg mutations lead a reduced specific activity, retaining 2.86 % (Gly108Arg), 10.95 % (Ala410Arg), and 44.23 % (Leu226Arg) of lipolytic activity. All three random mutants displayed increased specific activity as well as improved catalytic properties. This research provides the first deeper insights into the functionality of understudied Geobacillus spp. carboxylesterases with 55 kDa in size.

Have you tried turning it off and on again? Oscillating selection to enhance fitness-landscape traversal in adaptive laboratory evolution experiments

Adaptive Laboratory Evolution (ALE) is a powerful tool for engineering and understanding microbial physiology. ALE relies on the selection and enrichment of mutations that enable survival or faster growth under a selective condition imposed by the experimental setup. Phenotypic fitness landscapes are often underpinned by complex genotypes involving multiple genes, with combinatorial positive and negative effects on fitness. Such genotype relationships result in mutational fitness landscapes with multiple local fitness maxima and valleys. Traversing local maxima to find a global maximum often requires an individual or sub-population of cells to traverse fitness valleys. Traversing involves gaining mutations that are not adaptive for a given local maximum but are necessary to 'peak shift' to another local maximum, or eventually a global maximum. Despite these relatively well understood evolutionary principles, and the combinatorial genotypes that underlie most metabolic phenotypes, the majority of applied ALE experiments are conducted using constant selection pressures. The use of constant pressure can result in populations becoming trapped within local maxima, and often precludes the attainment of optimum phenotypes associated with global maxima. Here, we argue that oscillating selection pressures is an easily accessible mechanism for traversing fitness landscapes in ALE experiments, and provide theoretical and practical frameworks for implementation.

Recent advances in structure-based enzyme engineering for functional reconstruction

Structural information can help engineer enzymes. Usually, specific amino acids in particular regions are targeted for functional reconstruction to enhance the catalytic performance, including activity, stereoselectivity, and thermostability. Appropriate selection of target sites is the key to structure-based design, which requires elucidation of the structure-function relationships. Here, we summarize the mutations of residues in different specific regions, including active center, access tunnels, and flexible loops, on fine-tuning the catalytic performance of enzymes, and discuss the effects of altering the local structural environment on the functions. In addition, we keep up with the recent progress of structure-based approaches for enzyme engineering, aiming to provide some guidance on how to take advantage of the structural information.

Fungal Laccases: Fundamentals, Engineering and Classification Update

Multicopper oxidases (MCOs) share a common catalytic mechanism of activation by oxygen and cupredoxin-like folding, along with some common structural determinants. Laccases constitute the largest group of MCOs, with fungal laccases having the greatest biotechnological applicability due to their superior ability to oxidize a wide range of aromatic compounds and lignin, which is enhanced in the presence of redox mediators. The adaptation of these versatile enzymes to specific application processes can be achieved through the directed evolution of the recombinant enzymes. On the other hand, their substrate versatility and the low sequence homology among laccases make their exact classification difficult. Many of the ever-increasing amounts of MCO entries from fungal genomes are automatically (and often wrongly) annotated as laccases. In a recent comparative genomic study of 52 basidiomycete fungi, MCO classification was revised based on their phylogeny. The enzymes clustered according to common structural motifs and theoretical activities, revealing three novel groups of laccase-like enzymes. This review provides an overview of the structure, catalytic activity, and oxidative mechanism of fungal laccases and how their biotechnological potential as biocatalysts in industry can be greatly enhanced by protein engineering. Finally, recent information on newly identified MCOs with laccase-like activity is included.

Catalytic, asymmetric carbon-nitrogen bond formation using metal nitrenoids: from metal-ligand complexes via metalloporphyrins to enzymes

The introduction of nitrogen atoms into small molecules is of fundamental importance and it is vital that ever more efficient and selective methods for achieving this are developed. With this aim, the potential of nitrene chemistry has long been appreciated but its application has been constrained by the extreme reactivity of these labile species. This liability however can be attenuated by complexation with a transition metal and the resulting metal nitrenoids have unique and highly versatile reactivity which includes the amination of certain types of aliphatic C-H bonds as well as reactions with alkenes to afford aziridines. At least one new chiral centre is typically formed in these processes and the development of catalysts to exert control over enantioselectivity in nitrenoid-mediated amination has become a growing area of research, particularly over the past two decades. Compared with some synthetic methods, metal nitrenoid chemistry is notable in that chemists can draw from a diverse array of metals and catalysts , ranging from metal-ligand complexes, bearing a variety of ligand types, via bio-inspired metalloporphyrins, all the way through to, very recently, engineered enzymes themselves. In the latter category in particular, rapid progress is being made, the rate of which suggests that this approach may be instrumental in addressing some of the outstanding challenges in the field. This review covers key developments and strategies that have shaped the field, in addition to the latest advances, up until September 2023.

Inside Out Computational Redesign of Cavities for Improving Thermostability and Catalytic Activity of Rhizomucor Miehei Lipase

Cavities are created by hydrophobic interactions between residue side chain atoms during the folding of enzymes. Redesigning cavities can improve the thermostability and catalytic activity of the enzyme; however, the synergistic effect of cavities remains unclear. In this study, Rhizomucor miehei lipase (RML) was used as a model to explore volume fluctuation and spatial distribution changes of the internal cavities, which could reveal the roles of internal cavities in the thermostability and catalytic activity. We present an inside out cavity engineering (CE) strategy based on computational techniques to explore how changes in the volumes and spatial distribution of cavities affect the thermostability and catalytic activity of the enzyme. We obtained 12 single-point mutants, among which the melting temperatures (Tm) of 8 mutants showed an increase of more than 2°C. Sixteen multipoint mutations were further designed by spatial distribution rearrangement of internal cavities. The Tm of the most stable triple variant, with mutations including T21V (a change of T to V at position 21), S27A, and T198L (T21V/S27A/T198L), was elevated by 11.0°C, together with a 28.7-fold increase in the half-life at 65°C and a specific activity increase of 9.9-fold (up to 5,828 U mg-1), one of the highest lipase activities reported. The possible mechanism of decreased volumes and spatial rearrangement of the internal cavities improved the stability of the enzyme, optimizing the outer substrate tunnel to improve the catalytic efficiency. Overall, the inside out computational redesign of cavities method could help to deeply understand the effect of cavities on enzymatic stability and activity, which would be beneficial for protein engineering efforts to optimize natural enzymes. IMPORTANCE In the present study, R. miehei lipase, which is widely used in various industries, provides an opportunity to explore the effects of internal cavities on the thermostability and catalytic activity of enzymes. Here, we execute high hydrostatic pressure molecular dynamics (HP-MD) simulations to screen the critical internal cavity and reshape the internal cavities through site-directed mutation. We show that as the global internal cavity volume decreases, cavity rearrangement can improve the stability of the protein while optimizing the substrate channel to improve the catalytic efficiency. Our results provide significant insights into understanding the mechanism of action of the internal cavity. Our strategy is expected to be applied to other enzymes to promote increases in thermostability and catalytic activity.

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.

DNA-Encoded Libraries: Towards Harnessing their Full Power with Darwinian Evolution

DNA-encoded library (DEL) technologies are transforming the drug discovery process, enabling the identification of ligands at unprecedented speed and scale. DEL makes use of libraries that are orders of magnitude larger than traditional high-throughput screens. While a DNA tag alludes to a genotype-phenotype connection that is exploitable for molecular evolution, most of the work in the field is performed with libraries where the tag serves as an amplifiable barcode but does not allow "translation" into the synthetic product it is linked to. In this Review, we cover technologies that enable the "translation" of the genetic tag into synthetic molecules, both biochemically and chemically, and explore how it can be used to harness Darwinian evolutionary pressure.

The community-function landscape of microbial consortia

Quantitatively linking the composition and function of microbial communities is a major aspiration of microbial ecology. Microbial community functions emerge from a complex web of molecular interactions between cells, which give rise to population-level interactions among strains and species. Incorporating this complexity into predictive models is highly challenging. Inspired by a similar problem in genetics of predicting quantitative phenotypes from genotypes, an ecological community-function (or structure-function) landscape could be defined that maps community composition and function. In this piece, we present an overview of our current understanding of these community landscapes, their uses, limitations, and open questions. We argue that exploiting the parallels between both landscapes could bring powerful predictive methodologies from evolution and genetics into ecology, providing a boost to our ability to engineer and optimize microbial consortia.

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.

Logistic Regression-Guided Identification of Cofactor Specificity-Contributing Residues in Enzyme with Sequence Datasets Partitioned by Catalytic Properties

Changing the substrate/cofactor specificity of an enzyme requires multiple mutations at spatially adjacent positions around the substrate pocket. However, this is challenging when solely based on crystal structure information because enzymes undergo dynamic conformational changes during the reaction process. Herein, we proposed a method for estimating the contribution of each amino acid residue to substrate specificity by deploying a phylogenetic analysis with logistic regression. Since this method can estimate the candidate amino acids for mutation by ranking, it is readable and can be used in protein engineering. We demonstrated our concept using redox cofactor conversion of the Escherichia coli malic enzyme as a model, which still lacks crystal structure elucidation. The use of logistic regression with amino acid sequences classified by cofactor specificity showed that the NADP⁺-dependent malic enzyme completely switched cofactor specificity to NAD⁺ dependence without the need for a practical screening step. The model showed that surrounding residues made a greater contribution to cofactor specificity than those in the interior of the substrate pocket. These residues might be difficult to identify from crystal structure observations. We show that a highly accurate and inferential machine learning model was obtained using amino acid sequences of structurally homologous and functionally distinct enzymes as input data.

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.

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.

"Multiagent" Screening Improves Directed Enzyme Evolution by Identifying Epistatic Mutations

Enzyme evolution has enabled numerous advances in biotechnology and synthetic biology, yet still requires many iterative rounds of screening to identify optimal mutant sequences. This is due to the sparsity of the fitness landscape, which is caused by epistatic mutations that only offer improvements when combined with other mutations. We report an approach that incorporates diverse substrate analogues in the screening process, where multiple substrates act like multiple agents navigating the fitness landscape, identifying epistatic mutant residues without a need for testing the entire combinatorial search space. We initially validate this approach by engineering a malonyl-CoA synthetase and identify numerous epistatic mutations improving activity for several diverse substrates. The majority of these mutations would have been missed upon screening for a single substrate alone. We expect that this approach can accelerate a wide array of enzyme engineering programs.

Engineering the 2-Oxoglutarate Dehydrogenase Complex to Understand Catalysis and Alter Substrate Recognition

The E. coli 2-oxoglutarate dehydrogenase complex (OGDHc) is a multienzyme complex in the tricarboxylic acid cycle, consisting of multiple copies of three components, 2-oxoglutarate dehydrogenase (E1o), dihydrolipoamide succinyltransferase (E2o) and dihydrolipoamide dehydrogenase (E3), which catalyze the formation of succinyl-CoA and NADH (+H+) from 2-oxoglutarate. This review summarizes applications of the site saturation mutagenesis (SSM) to engineer E. coli OGDHc with mechanistic and chemoenzymatic synthetic goals. First, E1o was engineered by creating SSM libraries at positions His260 and His298.Variants were identified that: (a) lead to acceptance of substrate analogues lacking the 5-carboxyl group and (b) performed carboligation reactions producing acetoin-like compounds with good enantioselectivity. Engineering the E2o catalytic (core) domain enabled (a) assignment of roles for pivotal residues involved in catalysis, (b) re-construction of the substrate-binding pocket to accept substrates other than succinyllysyldihydrolipoamide and (c) elucidation of the mechanism of trans-thioesterification to involve stabilization of a tetrahedral oxyanionic intermediate with hydrogen bonds by His375 and Asp374, rather than general acid–base catalysis which has been misunderstood for decades. The E. coli OGDHc is the first example of a 2-oxo acid dehydrogenase complex which was evolved to a 2-oxo aliphatic acid dehydrogenase complex by engineering two consecutive E1o and E2o components.

Learning Strategies in Protein Directed Evolution

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

Click-Chemistry-Based Free Azide versus Azido Sugar Detection Enables Rapid In Vivo Screening of Glycosynthase Activity

Engineering of carbohydrate-active enzymes such as glycosynthases to enable chemoenzymatic synthesis of bespoke oligosaccharides has been limited by the lack of suitable ultrahigh-throughput screening methods capable of robustly detecting either starting substrates or end-products of the glycosidic bond formation reaction. Currently, there are limited screening methods available for rapid and highly sensitive single-cell-based screening of glycosynthase enzymes employing azido sugars as activated donor glycosyl substrates. Here, we report a fluorescence-based approach employing click-chemistry for the selective detection of glycosyl azides as substrates versus free inorganic azides as reaction products that facilitated an ultrahigh-throughput in vivo single-cell-based assay of glycosynthase activity. This assay was developed based on the distinct differences observed in relative fluorescence intensity of the triazole-containing fluorophore product formed during the click-chemistry reaction of organic glycosyl azides versus inorganic azides. This discovery formed the basis for proof of concept validation of a directed evolution methodology for screening and sorting glycosynthase mutants capable of synthesis of targeted fucosylated oligosaccharides. Our screening approach facilitated fluorescence-activated cell sorting of an error-prone polymerase chain reaction-based mutant library of fucosynthases expressed in Escherichia coli to identify several novel mutants that showed increased activity for β-fucosyl azide-activated donor sugars toward desired acceptor sugars (e.g., pNP-xylose and lactose). Finally, we discuss avenues for improving this proof of concept in vivo assay method to identify better glycosynthase mutants and further demonstrate the broader applicability of this screening methodology for synthesis of bespoke glycans.

Intelligent host engineering for metabolic flux optimisation in biotechnology

Optimising the function of a protein of length N amino acids by directed evolution involves navigating a 'search space' of possible sequences of some 20N. Optimising the expression levels of P proteins that materially affect host performance, each of which might also take 20 (logarithmically spaced) values, implies a similar search space of 20P. In this combinatorial sense, then, the problems of directed protein evolution and of host engineering are broadly equivalent. In practice, however, they have different means for avoiding the inevitable difficulties of implementation. The spare capacity exhibited in metabolic networks implies that host engineering may admit substantial increases in flux to targets of interest. Thus, we rehearse the relevant issues for those wishing to understand and exploit those modern genome-wide host engineering tools and thinking that have been designed and developed to optimise fluxes towards desirable products in biotechnological processes, with a focus on microbial systems. The aim throughput is 'making such biology predictable'. Strategies have been aimed at both transcription and translation, especially for regulatory processes that can affect multiple targets. However, because there is a limit on how much protein a cell can produce, increasing kcat in selected targets may be a better strategy than increasing protein expression levels for optimal host engineering.

The evolving art of creating genetic diversity: From directed evolution to synthetic biology

The ability to engineer biological systems, whether to introduce novel functionality or improved performance, is a cornerstone of biotechnology and synthetic biology. Typically, this requires the generation of genetic diversity to explore variations in phenotype, a process that can be performed at many levels, from single molecule targets (i.e., in directed evolution of enzymes) to whole organisms (e.g., in chassis engineering). Recent advances in DNA synthesis technology and automation have enhanced our ability to create variant libraries with greater control and throughput. This review highlights the latest developments in approaches to create such a hierarchy of diversity from the enzyme level to entire pathways in vitro, with a focus on the creation of combinatorial libraries that are required to navigate a target's vast design space successfully to uncover significant improvements in function.

Recent advances in the biosynthesis strategies of nitrogen heterocyclic natural products

Covering: 2015 to 2020Nitrogen heterocyclic natural products (NHNPs) are primary or secondary metabolites containing nitrogen heterocyclic (N-heterocyclic) skeletons. Due to the existence of the N-heterocyclic structure, NHNPs exhibit various bioactivities such as anticancer and antibacterial, which makes them widely used in medicines, pesticides, and food additives. However, the low content of these NHNPs in native organisms severely restricts their commercial application. Although a variety of NHNPs have been produced through extraction or chemical synthesis strategies, these methods suffer from several problems. The development of biotechnology provides new options for the production of NHNPs. This review introduces the recent progress of two strategies for the biosynthesis of NHNPs: enzymatic biosynthesis and microbial cell factory. In the enzymatic biosynthesis part, the recent progress in the mining of enzymes that synthesize N-heterocyclic skeletons (e.g., pyrrole, piperidine, diketopiperazine, and isoquinoline), the engineering of tailoring enzymes, and enzyme cascades constructed to synthesize NHNPs are discussed. In the microbial cell factory part, with tropane alkaloids (TAs) and tetrahydroisoquinoline (THIQ) alkaloids as the representative compounds, the strategies of unraveling unknown natural biosynthesis pathways of NHNPs in plants are summarized, and various metabolic engineering strategies to enhance their production in microbes are introduced. Ultimately, future perspectives for accelerating the biosynthesis of NHNPs are discussed.

Ribose-Binding Protein Mutants With Improved Interaction Towards the Non-natural Ligand 1,3-Cyclohexanediol

Bioreporters consist of genetically modified living organisms that respond to the presence of target chemical compounds by production of an easily measurable signal. The central element in a bioreporter is a sensory protein or aptamer, which, upon ligand binding, modifies expression of the reporter signal protein. A variety of naturally occurring or modified versions of sensory elements has been exploited, but it has proven to be challenging to generate elements that recognize non-natural ligands. Bacterial periplasmic binding proteins have been proposed as a general scaffold to design receptor proteins for non-natural ligands, but despite various efforts, with only limited success. Here, we show how combinations of randomized mutagenesis and reporter screening improved the performance of a set of mutants in the ribose binding protein (RbsB) of Escherichia coli, which had been designed based on computational simulations to bind the non-natural ligand 1,3-cyclohexanediol (13CHD). Randomized mutant libraries were constructed that used the initially designed mutants as scaffolds, which were cloned in an appropriate E. coli bioreporter system and screened for improved induction of the GFPmut2 reporter fluorescence in presence of 1,3-cyclohexanediol. Multiple rounds of library screening, sorting, renewed mutagenesis and screening resulted in 4.5-fold improvement of the response to 1,3-cyclohexanediol and a lower detection limit of 0.25 mM. All observed mutations except one were located outside the direct ligand-binding pocket, suggesting they were compensatory and helping protein folding or functional behavior other than interaction with the ligand. Our results thus demonstrate that combinations of ligand-binding-pocket redesign and randomized mutagenesis can indeed lead to the selection and recovery of periplasmic-binding protein mutants with non-natural compound recognition. However, current lack of understanding of the intermolecular movement and ligand-binding in periplasmic binding proteins such as RbsB are limiting the rational production of further and better sensory mutants.

Methods for enzyme library creation: Which one will you choose?: A guide for novices and experts to introduce genetic diversity

Enzyme engineering allows to explore sequence diversity in search for new properties. The scientific literature is populated with methods to create enzyme libraries for engineering purposes, however, choosing a suitable method for the creation of mutant libraries can be daunting, in particular for the novices. Here, we address both novices and experts: how can one enter the arena of enzyme library design and what guidelines can advanced users apply to select strategies best suited to their purpose? Section I is dedicated to the novices and presents an overview of established and standard methods for library creation, as well as available commercial solutions. The expert will discover an up-to-date tool to freshen up their repertoire (Section I) and learn of the newest methods that are likely to become a mainstay (Section II). We focus primarily on in vitro methods, presenting the advantages of each method. Our ultimate aim is to offer a selection of methods/strategies that we believe to be most useful to the enzyme engineer, whether a first-timer or a seasoned user.

Engineering complex communities by directed evolution

Directed evolution has been used for decades to engineer biological systems at or below the organismal level. Above the organismal level, a small number of studies have attempted to artificially select microbial ecosystems, with uneven and generally modest success. Our theoretical understanding of artificial ecosystem selection is limited, particularly for large assemblages of asexual organisms, and we know little about designing efficient methods to direct their evolution. Here, we have developed a flexible modelling framework that allows us to systematically probe any arbitrary selection strategy on any arbitrary set of communities and selected functions. By artificially selecting hundreds of in silico microbial metacommunities under identical conditions, we first show that the main breeding methods used to date, which do not necessarily let communities reach their ecological equilibrium, are outperformed by a simple screen of sufficiently mature communities. We then identify a range of alternative directed evolution strategies that, particularly when applied in combination, are well suited for the top-down engineering of large, diverse and stable microbial consortia. Our results emphasize that directed evolution allows an ecological structure-function landscape to be navigated in search of dynamically stable and ecologically resilient communities with desired quantitative attributes.

Revolutionizing enzyme engineering through artificial intelligence and machine learning

The combinatorial space of an enzyme sequence has astronomical possibilities and exploring it with contemporary experimental techniques is arduous and often ineffective. Multi-target objectives such as concomitantly achieving improved selectivity, solubility and activity of an enzyme have narrow plausibility under approaches of restricted mutagenesis and combinatorial search. Traditional enzyme engineering approaches have a limited scope for complex optimization due to the requirement of a priori knowledge or experimental burden of screening huge protein libraries. The recent surge in high-throughput experimental methods including Next Generation Sequencing and automated screening has flooded the field of molecular biology with big-data, which requires us to re-think our concurrent approaches towards enzyme engineering. Artificial Intelligence (AI) and Machine Learning (ML) have great potential to revolutionize smart enzyme engineering without the explicit need for a complete understanding of the underlying molecular system. Here, we portray the role and position of AI techniques in the field of enzyme engineering along with their scope and limitations. In addition, we explain how the traditional approaches of directed evolution and rational design can be extended through AI tools. Recent successful examples of AI-assisted enzyme engineering projects and their deviation from traditional approaches are highlighted. A comprehensive picture of current challenges and future avenues for AI in enzyme engineering are also discussed.

Directed Evolution of Microbial Communities

Directed evolution is a form of artificial selection that has been used for decades to find biomolecules and organisms with new or enhanced functional traits. Directed evolution can be conceptualized as a guided exploration of the genotype-phenotype map, where genetic variants with desirable phenotypes are first selected and then mutagenized to search the genotype space for an even better mutant. In recent years, the idea of applying artificial selection to microbial communities has gained momentum. In this article, we review the main limitations of artificial selection when applied to large and diverse collectives of asexually dividing microbes and discuss how the tools of directed evolution may be deployed to engineer communities from the top down. We conceptualize directed evolution of microbial communities as a guided exploration of an ecological structure-function landscape and propose practical guidelines for navigating these ecological landscapes.

Pervasive cooperative mutational effects on multiple catalytic enzyme traits emerge via long-range conformational dynamics

Multidimensional fitness landscapes provide insights into the molecular basis of laboratory and natural evolution. To date, such efforts usually focus on limited protein families and a single enzyme trait, with little concern about the relationship between protein epistasis and conformational dynamics. Here, we report a multiparametric fitness landscape for a cytochrome P450 monooxygenase that was engineered for the regio- and stereoselective hydroxylation of a steroid. We develop a computational program to automatically quantify non-additive effects among all possible mutational pathways, finding pervasive cooperative signs and magnitude epistasis on multiple catalytic traits. By using quantum mechanics and molecular dynamics simulations, we show that these effects are modulated by long-range interactions in loops, helices and β-strands that gate the substrate access channel allowing for optimal catalysis. Our work highlights the importance of conformational dynamics on epistasis in an enzyme involved in secondary metabolism and offers insights for engineering P450s.

Connecting conformational dynamics and epistasis has so far been limited to a few proteins and a single fitness trait. Here, the authors provide evidence of positive epistasis on multiple catalytic traits in the evolution and dynamics of engineered cytochrome P450 monooxygenase, offering insights for in silico protein design.

GRAPE, a greedy accumulated strategy for computational protein engineering

Nature harbors fascinating enzymatic catalysts with high efficiency, chemo-, regio- and stereoselectivity. However, the insufficient stability of the enzymes often prevents their widespread utilization for industrial processes. Not content with the finite repertoire of naturally occurring enzymes, protein engineering holds promises to extend the applications of the improved enzymes with desired physical and catalytic properties. Herein, we devised a computational strategy (greedy accumulated strategy for protein engineering, GRAPE) to enhance the thermostability of enzymes. Through scanning of all point mutations of the structural and evolutionary consensus analysis, a library containing fewer than 100 mutations was established for characterization. After preliminary experimental verification, effective mutations are clustered in a multidimensional physical property space and then accumulated via the greedy algorithm to produce the final designed enzyme. Using the recently reported IsPETase from Ideonella sakaiensis that decomposes PET under ambient temperatures as a starting point, we adopted the GRAPE strategy to come up with a DuraPETase (T_M=77°C, raised by 31°C) which showed drastically enhanced degradation performance (300-fold) on semicrystalline PET films at 40°C.

Engineered dual selection for directed evolution of SpCas9 PAM specificity

The widely used Streptococcus pyogenes Cas9 (SpCas9) nuclease derives its DNA targeting specificity from protein-DNA contacts with protospacer adjacent motif (PAM) sequences, in addition to base-pairing interactions between its guide RNA and target DNA. Previous reports have established that the PAM specificity of SpCas9 can be altered via positive selection procedures for directed evolution or other protein engineering strategies. Here we exploit in vivo directed evolution systems that incorporate simultaneous positive and negative selection to evolve SpCas9 variants with commensurate or improved activity on NAG PAMs relative to wild type and reduced activity on NGG PAMs, particularly YGG PAMs. We also show that the PAM preferences of available evolutionary intermediates effectively determine whether similar counterselection PAMs elicit different selection stringencies, and demonstrate that negative selection can be specifically increased in a yeast selection system through the fusion of compensatory zinc fingers to SpCas9.

In vitro evolution of antibody affinity via insertional scanning mutagenesis of an entire antibody variable region

We report a systematic combinatorial exploration of affinity enhancement of antibodies by insertions and deletions (InDels). Transposon-based introduction of InDels via the method TRIAD (transposition-based random insertion and deletion mutagenesis) was used to generate large libraries with random in-frame InDels across the entire single-chain variable fragment gene that were further recombined and screened by ribosome display. Knowledge of potential insertion points from TRIAD libraries formed the basis of exploration of length and sequence diversity of novel insertions by insertional-scanning mutagenesis (InScaM). An overall 256-fold affinity improvement of an anti-IL-13 antibody BAK1 as a result of InDel mutagenesis and combination with known point mutations validates this approach, and suggests that the results of this InDel mutagenesis and conventional exploration of point mutations can synergize to generate antibodies with higher affinity.

Accessing unexplored regions of sequence space in directed enzyme evolution via insertion/deletion mutagenesis

Insertions and deletions (InDels) are frequently observed in natural protein evolution, yet their potential remains untapped in laboratory evolution. Here we introduce a transposon-based mutagenesis approach (TRIAD) to generate libraries of random variants with short in-frame InDels, and screen TRIAD libraries to evolve a promiscuous arylesterase activity in a phosphotriesterase. The evolution exhibits features that differ from previous point mutagenesis campaigns: while the average activity of TRIAD variants is more compromised, a larger proportion has successfully adapted for the activity. Different functional profiles emerge: (i) both strong and weak trade-off between activities are observed; (ii) trade-off is more severe (20- to 35-fold increased kcat/KM in arylesterase with 60-400-fold decreases in phosphotriesterase activity) and (iii) improvements are present in kcat rather than just in KM, suggesting adaptive solutions. These distinct features make TRIAD an alternative to widely used point mutagenesis, accessing functional innovations and traversing unexplored fitness landscape regions.

Organic solvent stability and long-term storage of myoglobin-based carbene transfer biocatalysts

Recent years have witnessed a rapid increase in the application of enzymes for chemical synthesis and manufacturing, including the industrial-scale synthesis of pharmaceuticals using multienzyme processes. From an operational standpoint, these bioprocesses often require robust biocatalysts capable of tolerating high concentrations of organic solvents and possessing long shelflife stability. In this work, we investigated the activity and stability of myoglobin (Mb)-based carbene transfer biocatalysts in the presence of organic solvents and after lyophilization. Our studies demonstrate that Mb-based cyclopropanases possess remarkable organic solvent stability, maintaining high levels of activity and stereoselectivity in the presence of up to 30%-50% (v/v) concentrations of various organic solvents, including ethanol, methanol, N,N-dimethylformamide, acetonitrile, and dimethyl sulfoxide. Furthermore, they tolerate long-term storage in lyophilized form, both as purified protein and as whole cells, without significant loss in activity and stereoselectivity. These stability properties are shared by Mb-based carbene transferases optimized for other type of asymmetric carbene transfer reactions. Finally, we report on simple protocols for catalyst recycling as whole-cell system and for obviating the need for strictly anaerobic conditions to perform these transformations. These findings demonstrate the robustness of Mb-based carbene transferases under operationally relevant conditions and should help guide the application of these biocatalysts for synthetic applications.

Improving the catalytic efficiency of thermostable Geobacillus stearothermophilus xylanase XT6 by single-amino acid substitution

Directed evolution using error-prone polymerase chain reaction was employed in the current study to enhance the catalytic efficiency of a thermostable Geobacillus stearothermophilus xylanase XT6 parent. High-throughput screening identified two variants with enhanced activity. Sequencing analysis revealed the presence of a single-amino acid substitution (P209L or V161L) in each variant. The maximum activity of mutant V161L and P209L was at 85°C and 70°C, respectively. Both mutants exhibited maximum activity at pH 7. The thermal and alkaline tolerance of mutant V161L only were markedly improved. The two mutants were more resistant to ethanol inhibition than the parent. Substrate specificity of the two mutants was shifted from beechwood xylan to birchwood xylan. The potential of the two mutants to hydrolyze rice straw and sugarcane bagasse increased. Both turnover number (kcat) and catalytic efficiency (kcat/kM) increased 12.2- and 5.7-folds for variant P209L and 13- and 6.5-folds for variant V161L, respectively, towards birchwood xylan. Based on the previously published crystal structure of extracellular G. stearothermophilus xylanase XT6, V161L and P209L mutation locate on βα-loops. Conformational changes of the respective loops could potentiate the loop swinging, product release and consequently result in enhancement of the catalytic performance.

Development of a highly efficient and specific L-theanine synthase

γ-Glutamylcysteine synthetase (γ-GCS) from Escherichia coli, which catalyzes the formation of L-glutamylcysteine from L-glutamic acid and L-cysteine, was engineered into an L-theanine synthase using L-glutamic acid and ethylamine as substrates. A high-throughput screening method using a 96-well plate was developed to evaluate the L-theanine synthesis reaction. Both site-saturation mutagenesis and random mutagenesis were applied. After three rounds of directed evolution, 13B6, the best-performing mutant enzyme, exhibited 14.6- and 17.0-fold improvements in L-theanine production and catalytic efficiency for ethylamine, respectively, compared with the wild-type enzyme. In addition, the specific activity of 13B6 for the original substrate, L-cysteine, decreased to approximately 14.6% of that of the wild-type enzyme. Thus, the γ-GCS enzyme was successfully switched to a specific L-theanine synthase by directed evolution. Furthermore, an ATP-regeneration system was introduced based on polyphosphate kinases catalyzing the transfer of phosphates from polyphosphate to ADP, thus lowering the level of ATP consumption and the cost of L-theanine synthesis. The final L-theanine production by mutant 13B6 reached 30.4 ± 0.3 g/L in 2 h, with a conversion rate of 87.1%, which has great potential for industrial applications.

Creating Flavin Reductase Variants with Thermostable and Solvent-Tolerant Properties by Rational-Design Engineering

We have employed computational approaches-FireProt and FRESCO-to predict thermostable variants of the reductase component (C₁ ) of (4-hydroxyphenyl)acetate 3-hydroxylase. With the additional aid of experimental results, two C₁ variants, A166L and A58P, were identified as thermotolerant enzymes, with thermostability improvements of 2.6-5.6 °C and increased catalytic efficiency of 2- to 3.5-fold. After heat treatment at 45 °C, both of the thermostable C₁ variants remain active and generate reduced flavin mononucleotide (FMNH^- ) for reactions catalyzed by bacterial luciferase and by the monooxygenase C₂ more efficiently than the wild type (WT). In addition to thermotolerance, the A166L and A58P variants also exhibited solvent tolerance. Molecular dynamics (MD) simulations (6 ns) at 300-500 K indicated that mutation of A166 to L and of A58 to P resulted in structural changes with increased stabilization of hydrophobic interactions, and thus in improved thermostability. Our findings demonstrated that improvements in the thermostability of C₁ enzyme can lead to broad-spectrum uses of C₁ as a redox biocatalyst for future industrial applications.

Directed modification of a ruminal cellulase gene (CMC-1) from a metagenomic library isolated from Yunnan gayal (Bos frontalis)

Gayal (Bos frontalis) of the Yunnan region is well adapted to harsh environmental conditions. Its diet consists predominantly of bamboo, reeds, and woody plants, suggesting that the rumen of this species contains many fiber-degrading bacteria and cellulases. The aim of this study was to identify and modify specific cellulases found in the gayal rumen. In the present study, a directed evolution strategy of error-prone PCR was employed to improve the activity or optimal temperature of a cellulase gene (CMC-1) isolated from gayal rumen. The CMC-1 gene was heterologously expressed in Escherichia coli (E. coli) BL21, and the recombinant CMC-1 protein hydrolyzed carboxyl methyl cellulose (CMC) with an optimal activity at pH 5.0 and 50 °C. A library of mutated ruminal CMC-1 genes was constructed and a mutant EP-15 gene was identified. Sequencing analysis revealed that EP-15 and CMC-1 belonged to the glycosyl hydrolase family 5 (GHF5) and had the highest homology to a cellulase (Accession No. WP_083429257.1) from Prevotellaceae bacterium, HUN156. There were similar predicted GH5 domains in EP-15 and CMC-1. The EP-15 gene was heterologously expressed and exhibited cellulase activity in E. coli BL21 at pH 5.0, but the optimum temperature for its activity was reduced from that of CMC-1 (50 °C) to 45 °C, which was closer to the physiological temperature of the rumen (40 °C). The cellulase activity of EP-15 was about two times higher than CMC-1 at 45 °C or PH 5.0, and also was more stable in response to temperature and pH changes compared to CMC-1. This study successfully isolated and modified a ruminal cellulase gene from metagenomics library of Yunnan gayal. Our findings may obtain a useful cellulase in future applications and present the first evidence of modified cellulases in the gayal rumen.

Tailoring Tryptophan Synthase TrpB for Selective Quaternary Carbon Bond Formation

We previously engineered the β-subunit of tryptophan synthase (TrpB), which catalyzes the condensation of l-serine and indole to l-tryptophan, to synthesize a range of noncanonical amino acids from l-serine and indole derivatives or other nucleophiles. Here we employ directed evolution to engineer TrpB to accept 3-substituted oxindoles and form C-C bonds leading to new quaternary stereocenters. Initially, the variants that could use 3-substituted oxindoles preferentially formed N-C bonds on N1 of the substrate. Protecting N1 encouraged evolution toward C-alkylation, which persisted when protection was removed. Six generations of directed evolution resulted in TrpB Pfquat with a 400-fold improvement in activity for alkylation of 3-substituted oxindoles and the ability to selectively form a new, all-carbon quaternary stereocenter at the γ-position of the amino acid products. The enzyme can also alkylate and form all-carbon quaternary stereocenters on structurally similar lactones and ketones, where it exhibits excellent regioselectivity for the tertiary carbon. The configurations of the γ-stereocenters of two of the products were determined via microcrystal electron diffraction (MicroED), and we report the MicroED structure of a small molecule obtained using the Falcon III direct electron detector. Highly thermostable and expressed at >500 mg/L E. coli culture, TrpB Pfquat offers an efficient, sustainable, and selective platform for the construction of diverse noncanonical amino acids bearing all-carbon quaternary stereocenters.

Model-guided mechanism discovery and parameter selection for directed evolution

Directed evolution is frequently applied to identify genetic variants with improvements in a single or multiple properties. When used to improve multiple properties simultaneously, a common strategy is to apply alternating rounds of selection criteria to enrich for variants with each desirable trait. In particular, counterselection, or selection against undesired traits rather than for desired ones, has been successfully employed in many studies. Although the sequence and stringency of alternating selective pressures for different traits are known to be highly consequential for the outcome of the screen, the effects of these parameters have not been systematically evaluated. We developed a method for producing a statistical modeling framework to elucidate these effects. The model uses single-cell fluorescence intensity distributions to estimate the proportions of phenotypic populations within a library and then predicts the changes in these proportions depending on specified positive selective or counterselective pressures. We validated the approach using recently described systems for metabolite-responsive bacterial transcription factors and yeast G-protein-coupled receptors. Finally, we applied the model to identify biological sources that exert undesirable selective pressure on libraries during sorting. Notably, these pressures produce substantial artifacts that, if unaddressed, can lead to failure of the screen. This method for model generation can be applied to FACS-based directed evolution experiments to create a quantitative framework that identifies subtle population effects. Such models can guide the choice of experimental design parameters to better enrich for true positive genetic variants and improve the chance of successful directed evolution.

A Continuing Career in Biocatalysis: Frances H. Arnold

On the occasion of Professor Frances H. Arnold's recent acceptance of the 2018 Nobel Prize in Chemistry, we honor her numerous contributions to the fields of directed evolution and biocatalysis. Arnold pioneered the development of directed evolution methods for engineering enzymes as biocatalysts. Her highly interdisciplinary research has provided a ground not only for understanding the mechanisms of enzyme evolution but also for developing commercially viable enzyme biocatalysts and biocatalytic processes. In this Account, we highlight some of her notable contributions in the past three decades in the development of foundational directed evolution methods and their applications in the design and engineering of enzymes with desired functions for biocatalysis. Her work has created a paradigm shift in the broad catalysis field.

Exploring the molecular basis for substrate specificity in homologous macrolide biosynthetic cytochromes P450

Cytochromes P450 (P450s) are nature's catalysts of choice for performing demanding and physiologically vital oxidation reactions. Biochemical characterization of these enzymes over the past decades has provided detailed mechanistic insight and highlighted the diversity of substrates P450s accommodate and the spectrum of oxidative transformations they catalyze. Previously, we discovered that the bacterial P450 MycCI from the mycinamicin biosynthetic pathway in Micromonospora griseorubida possesses an unusually broad substrate scope, whereas the homologous P450 from tylosin-producing Streptomyces fradiae (TylHI) exhibits a high degree of specificity for its native substrate. Here, using biochemical, structural, and computational approaches, we aimed to understand the molecular basis for the disparate reactivity profiles of these two P450s. Turnover and equilibrium binding experiments with substrate analogs revealed that TylHI strictly prefers 16-membered ring macrolides bearing the deoxyamino sugar mycaminose. To help rationalize these results, we solved the X-ray crystal structure of TylHI in complex with its native substrate at 1.99-Å resolution and assayed several site-directed mutants. We also conducted molecular dynamics simulations of TylHI and MycCI and biochemically characterized a third P450 homolog from the chalcomycin biosynthetic pathway in Streptomyces bikiniensis These studies provided a basis for constructing P450 chimeras to gain further insight into the features dictating the differences in reaction profile among these structurally and functionally related enzymes, ultimately unveiling the central roles of key loop regions in influencing substrate binding and turnover. Our work highlights the complex nature of P450/substrate interactions and raises interesting questions regarding the evolution of functional diversity among biosynthetic enzymes.