Enhancing a de novo enzyme activity by computationally-focused ultra-low-throughput screening

Efficient Searches in Protein Sequence Space Through AI-Driven Iterative Learning

The protein sequence space is vast. This fact, together with the prevalence of epistasis, hampers the engineering of novel enzymes through library screening and is a major obstacle to any attempt to predict natural protein evolution. Recently, specialized methodologies have been used to determine fitness data on ~260,000 sequences for the gene of the enzyme dihydrofolate reductase and antibody affinity data for all combinations of the mutations present in the receptor-binding domain (RBD) of the Omicron strain of SARS-CoV-2 (~30,000 variants). We show that upon iterative training on a total of just a few hundred variants, various state-of-the-art AI tools (multi-layer perceptron, random forest, and XGBoost algorithms) find very high fitness variants of the enzyme and predict the antibody evasion patterns of the RBD. This work provides a basis for efficient, widely applicable, low-throughput experimental approaches to assess viral protein evolution and to engineer enzymes for biotechnological applications.

Enzyme Enhancement Through Computational Stability Design Targeting NMR-Determined Catalytic Hotspots

Enzymes are the quintessential green catalysts, but realizing their full potential for biotechnology typically requires improvement of their biomolecular properties. Catalysis enhancement, however, is often accompanied by impaired stability. Here, we show how the interplay between activity and stability in enzyme optimization can be efficiently addressed by coupling two recently proposed methodologies for guiding directed evolution. We first identify catalytic hotspots from chemical shift perturbations induced by transition-state-analogue binding and then use computational/phylogenetic design (FuncLib) to predict stabilizing combinations of mutations at sets of such hotspots. We test this approach on a previously designed de novo Kemp eliminase, which is already highly optimized in terms of both activity and stability. Most tested variants displayed substantially increased denaturation temperatures and purification yields. Notably, our most efficient engineered variant shows a ∼3-fold enhancement in activity (kcat ∼ 1700 s-1, kcat/KM ∼ 4.3 × 105 M-1 s-1) from an already heavily optimized starting variant, resulting in the most proficient proton-abstraction Kemp eliminase designed to date, with a catalytic efficiency on a par with naturally occurring enzymes. Molecular simulations pinpoint the origin of this catalytic enhancement as being due to the progressive elimination of a catalytically inefficient substrate conformation that is present in the original design. Remarkably, interaction network analysis identifies a significant fraction of catalytic hotspots, thus providing a computational tool which we show to be useful even for natural-enzyme engineering. Overall, our work showcases the power of dynamically guided enzyme engineering as a design principle for obtaining novel biocatalysts with tailored physicochemical properties, toward even anthropogenic reactions.

Long-Range Electrostatics in Serine Proteases: Machine Learning-Driven Reaction Sampling Yields Insights for Enzyme Design

Computational enzyme design is a promising technique for producing novel enzymes for industrial and clinical needs. A key challenge that this technique faces is to consistently achieve the desired activity. Fundamental studies of natural enzymes revealed critical contributions from second-shell - and even more distant - residues to their remarkable efficiency. In particular, such residues organize the internal electrostatic field to promote the reaction. Engineering such fields computationally proved to be a promising strategy, which, however, has some limitations. Charged residues necessarily form specific patterns of local interactions that may be exploited for structural integrity. As a result, it is impossible to probe the electrostatic field alone by substituting amino acids. We hypothesize that an approach that isolates the influences of residues' charges from other influences could yield deeper insights. We use molecular modeling with AI-enhanced QM/MM reaction sampling to implement such an approach and apply it to a model serine protease subtilisin. We find that the negative charge 8 Å away from the catalytic site is crucial to achieving the enzyme's catalytic efficiency, contributing more than 2 kcal/mol to lowering the barrier. In contrast, a positive charge from the second-closest charged residue opposes the efficiency of the reaction by raising the barrier by 0.8 kcal/mol. This result invites discussion into the role of this residue and trade-offs that might have taken place in the evolution of such enzymes. Our approach is transferable and can help investigate the evolution of electrostatic preorganization in other enzymes. We believe that the study and engineering of electrostatic fields in enzymes is a promising direction to advance both fundamental and applied enzymology and lead to the design of new powerful biocatalysts.

3D variability analysis reveals a hidden conformational change controlling ammonia transport in human asparagine synthetase

Advances in X-ray crystallography and cryogenic electron microscopy (cryo-EM) offer the promise of elucidating functionally relevant conformational changes that are not easily studied by other biophysical methods. Here we show that 3D variability analysis (3DVA) of the cryo-EM map for wild-type (WT) human asparagine synthetase (ASNS) identifies a functional role for the Arg-142 side chain and test this hypothesis experimentally by characterizing the R142I variant in which Arg-142 is replaced by isoleucine. Support for Arg-142 playing a role in the intramolecular translocation of ammonia between the active site of the enzyme is provided by the glutamine-dependent synthetase activity of the R142 variant relative to WT ASNS, and MD simulations provide a possible molecular mechanism for these findings. Combining 3DVA with MD simulations is a generally applicable approach to generate testable hypotheses of how conformational changes in buried side chains might regulate function in enzymes.

Friends and relatives: insight into conformational regulation from orthologues and evolutionary lineages using KIF and KIN

Noncovalent interaction networks provide a powerful means to represent and analyze protein structure. Such networks can represent both static structures and dynamic conformational ensembles. We have recently developed two tools for analyzing such interaction networks and generating hypotheses for protein engineering. Here, we apply these tools to the conformational regulation of substrate specificity in class A β-lactamases, particularly the evolutionary development from generalist to specialist catalytic function and how that can be recapitulated or reversed by protein engineering. These tools, KIF and KIN, generate a set of prioritized residues and interactions as targets for experimental protein engineering.

Enhancing catalytic efficiency of Bacillus subtilis laccase BsCotA through active site pocket design

BsCotA laccase is a promising candidate for industrial application due to its excellent thermal stability. In this research, our objective was to enhance the catalytic efficiency of BsCotA by modifying the active site pocket. We utilized a strategy combining the diversity design of the active site pocket with molecular docking screening, which resulted in selecting five variants for characterization. All five variants proved functional, with four demonstrating improved turnover rates. The most effective variants exhibited a remarkable 7.7-fold increase in catalytic efficiency, evolved from 1.54 × 105 M-1 s-1 to 1.18 × 106 M-1 s-1, without any stability loss. To investigate the underlying molecular mechanisms, we conducted a comprehensive structural analysis of our variants. The analysis suggested that substituting Leu386 with aromatic residues could enhance BsCotA's ability to accommodate the 2,2'-azino-di-(3-ethylbenzothiazoline)-6-sulfonate (ABTS) substrate. However, the inclusion of charged residues, G323D and G417H, into the active site pocket reduced kcat. Ultimately, our research contributes to a deeper understanding of the role played by residues in the laccases' active site pocket, while successfully demonstrating a method to lift the catalytic efficiency of BsCotA. KEY POINTS: • Active site pocket design that enhanced BsCotA laccase efficiency • 7.7-fold improved in catalytic rate • All tested variants retain thermal stability.

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

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

Addressing epistasis in the design of protein function

Mutations in protein active sites can dramatically improve function. The active site, however, is densely packed and extremely sensitive to mutations. Therefore, some mutations may only be tolerated in combination with others in a phenomenon known as epistasis. Epistasis reduces the likelihood of obtaining improved functional variants and dramatically slows natural and lab evolutionary processes. Research has shed light on the molecular origins of epistasis and its role in shaping evolutionary trajectories and outcomes. In addition, sequence- and AI-based strategies that infer epistatic relationships from mutational patterns in natural or experimental evolution data have been used to design functional protein variants. In recent years, combinations of such approaches and atomistic design calculations have successfully predicted highly functional combinatorial mutations in active sites. These were used to design thousands of functional active-site variants, demonstrating that, while our understanding of epistasis remains incomplete, some of the determinants that are critical for accurate design are now sufficiently understood. We conclude that the space of active-site variants that has been explored by evolution may be expanded dramatically to enhance natural activities or discover new ones. Furthermore, design opens the way to systematically exploring sequence and structure space and mutational impacts on function, deepening our understanding and control over protein activity.

In silico design of multipoint mutants for enhanced performance of Thermomyces lanuginosus lipase for efficient biodiesel production

BACKGROUND

Biodiesel, an emerging sustainable and renewable clean energy, has garnered considerable attention as an alternative to fossil fuels. Although lipases are promising catalysts for biodiesel production, their efficiency in industrial-scale application still requires improvement.

RESULTS

In this study, a novel strategy for multi-site mutagenesis in the binding pocket was developed via FuncLib (for mutant enzyme design) and Rosetta Cartesian_ddg (for free energy calculation) to improve the reaction rate and yield of lipase-catalyzed biodiesel production. Thermomyces lanuginosus lipase (TLL) with high activity and thermostability was obtained using the Pichia pastoris expression system. The specific activities of the mutants M11 and M21 (each with 5 and 4 mutations) were 1.50- and 3.10-fold higher, respectively, than those of the wild-type (wt-TLL). Their corresponding melting temperature profiles increased by 10.53 and 6.01 °C, [Formula: see text] (the temperature at which the activity is reduced to 50% after 15 min incubation) increased from 60.88 to 68.46 °C and 66.30 °C, and the optimum temperatures shifted from 45 to 50 °C. After incubation in 60% methanol for 1 h, the mutants M11 and M21 retained more than 60% activity, and 45% higher activity than that of wt-TLL. Molecular dynamics simulations indicated that the increase in thermostability could be explained by reduced atomic fluctuation, and the improved catalytic properties were attributed to a reduced binding free energy and newly formed hydrophobic interaction. Yields of biodiesel production catalyzed by mutants M11 and M21 for 48 h at an elevated temperature (50 °C) were 94.03% and 98.56%, respectively, markedly higher than that of the wt-TLL (88.56%) at its optimal temperature (45 °C) by transesterification of soybean oil.

CONCLUSIONS

An integrating strategy was first adopted to realize the co-evolution of catalytic efficiency and thermostability of lipase. Two promising mutants M11 and M21 with excellent properties exhibited great potential for practical applications for in biodiesel production.

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

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

Mutexa: A Computational Ecosystem for Intelligent Protein Engineering

Protein engineering holds immense promise in shaping the future of biomedicine and biotechnology. This Review focuses on our ongoing development of Mutexa, a computational ecosystem designed to enable "intelligent protein engineering". In this vision, researchers will seamlessly acquire sequences of protein variants with desired functions as biocatalysts, therapeutic peptides, and diagnostic proteins through a finely-tuned computational machine, akin to Amazon Alexa's role as a versatile virtual assistant. The technical foundation of Mutexa has been established through the development of a database that combines and relates enzyme structures and their respective functions (e.g., IntEnzyDB), workflow software packages that enable high-throughput protein modeling (e.g., EnzyHTP and LassoHTP), and scoring functions that map the sequence-structure-function relationship of proteins (e.g., EnzyKR and DeepLasso). We will showcase the applications of these tools in benchmarking the convergence conditions of enzyme functional descriptors across mutants, investigating protein electrostatics and cavity distributions in SAM-dependent methyltransferases, and understanding the role of nonelectrostatic dynamic effects in enzyme catalysis. Finally, we will conclude by addressing the future steps and fundamental challenges in our endeavor to develop new Mutexa applications that assist the identification of beneficial mutants in protein engineering.

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

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

Loop dynamics and the evolution of enzyme activity

In the early 2000s, Tawfik presented his 'New View' on enzyme evolution, highlighting the role of conformational plasticity in expanding the functional diversity of limited repertoires of sequences. This view is gaining increasing traction with increasing evidence of the importance of conformational dynamics in both natural and laboratory evolution of enzymes. The past years have seen several elegant examples of harnessing conformational (particularly loop) dynamics to successfully manipulate protein function. This Review revisits flexible loops as critical participants in regulating enzyme activity. We showcase several systems of particular interest: triosephosphate isomerase barrel proteins, protein tyrosine phosphatases and β-lactamases, while briefly discussing other systems in which loop dynamics are important for selectivity and turnover. We then discuss the implications for engineering, presenting examples of successful loop manipulation in either improving catalytic efficiency, or changing selectivity completely. Overall, it is becoming clearer that mimicking nature by manipulating the conformational dynamics of key protein loops is a powerful method of tailoring enzyme activity, without needing to target active-site residues.

Repertoire of Computationally Designed Peroxygenases for Enantiodivergent C-H Oxyfunctionalization Reactions

The generation of enantiodivergent biocatalysts for C-H oxyfunctionalizations is ever more important in modern synthetic chemistry. Here, we have applied the FuncLib algorithm based on phylogenetic and Rosetta calculations to design a diverse repertoire of active, stable, and enantiodivergent fungal peroxygenases. 24 designs, each carrying 4-5 mutations in the catalytic core, were expressed functionally in yeast and benchmarked against characteristic model compounds. Several designs were active and stable in a range of temperature and pH, displaying unprecedented enantiodivergence, changing regioselectivity from alkyl to aromatic hydroxylation, and increasing catalytic efficiencies up to 10-fold, with 15-fold improvements in total turnover numbers over the parental enzyme. We find that this dramatic functional divergence stems from beneficial epistasis among the mutations and an extensive reorganization of the heme channel. Our work demonstrates that FuncLib can rapidly design highly functional libraries enriched in enantioselective peroxygenases not seen in nature for a range of biotechnological applications.

Between Protein Fold and Nucleophile Identity: Multiscale Modeling of the TEV Protease Enzyme-Substrate Complex

The cysteine protease from the tobacco etch virus (TEVp) is a well-known and widely utilized enzyme. TEVp's chymotrypsin-like fold is generally associated with serine catalytic triads that differ in terms of a reaction mechanism from the most well-studied papain-like cysteine proteases. The question of what dominates the TEVp mechanism, nucleophile identity, or structural composition has never been previously addressed. Here, we use enhanced sampling multiscale modeling to uncover that TEVp combines the features of two worlds in such a way that potentially hampers its activity. We show that TEVp cysteine is strictly in the anionic form in a free enzyme similar to papain. Peptide binding shifts the equilibrium toward the nucleophile's protonated form, characteristic of chymotrypsin-like proteases, although the cysteinyl anion form is still present and interconversion is rapid. This way cysteine protonation generates enzyme states that are a diversion from the most effective course of action, with only 13.2% of Michaelis complex sub-states able to initiate the reaction. As a result, we propose an updated view on the reaction mechanism catalyzed by TEVp. We also demonstrate that AlphaFold is able to construct protease-substrate complexes with high accuracy. We propose that our findings open a way for its industrious use in enzymological tasks. Unique features of TEVp discovered in this work open a discussion on the evolutionary history and trade-offs of optimizing serine triad-associated folds to cysteine as a nucleophile.

NMR-guided directed evolution

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

Efficient Base-Catalyzed Kemp Elimination in an Engineered Ancestral Enzyme

The routine generation of enzymes with completely new active sites is a major unsolved problem in protein engineering. Advances in this field have thus far been modest, perhaps due, at least in part, to the widespread use of modern natural proteins as scaffolds for de novo engineering. Most modern proteins are highly evolved and specialized and, consequently, difficult to repurpose for completely new functionalities. Conceivably, resurrected ancestral proteins with the biophysical properties that promote evolvability, such as high stability and conformational diversity, could provide better scaffolds for de novo enzyme generation. Kemp elimination, a non-natural reaction that provides a simple model of proton abstraction from carbon, has been extensively used as a benchmark in de novo enzyme engineering. Here, we present an engineered ancestral β-lactamase with a new active site that is capable of efficiently catalyzing Kemp elimination. The engineering of our Kemp eliminase involved minimalist design based on a single function-generating mutation, inclusion of an extra polypeptide segment at a position close to the de novo active site, and sharply focused, low-throughput library screening. Nevertheless, its catalytic parameters (kcat/KM~2·105 M-1 s-1, kcat~635 s-1) compare favorably with the average modern natural enzyme and match the best proton-abstraction de novo Kemp eliminases that are reported in the literature. The general implications of our results for de novo enzyme engineering are discussed.

Natural Evolution Provides Strong Hints about Laboratory Evolution of Designer Enzymes

Laboratory evolution combined with computational enzyme design provides the opportunity to generate novel biocatalysts. Nevertheless, it has been challenging to understand how laboratory evolution optimizes designer enzymes by introducing seemingly random mutations. A typical enzyme optimized with laboratory evolution is the abiological Kemp eliminase, initially designed by grafting active site residues into a natural protein scaffold. Here, we relate the catalytic power of laboratory-evolved Kemp eliminases to the statistical energy ([Formula: see text]) inferred from their natural homologous sequences using the maximum entropy model. The [Formula: see text] of designs generated by directed evolution is correlated with enhanced activity and reduced stability, thus displaying a stability-activity trade-off. In contrast, the [Formula: see text] for mutants in catalytic-active remote regions (in which remote residues are important for catalysis) is strongly anticorrelated with the activity. These findings provide an insight into the role of protein scaffolds in the adaption to new enzymatic functions. It also indicates that the valley in the [Formula: see text] landscape can guide enzyme design for abiological catalysis. Overall, the connection between laboratory and natural evolution contributes to understanding what is optimized in the laboratory and how new enzymatic function emerges in nature, and provides guidance for computational enzyme design.

Recent Advances in Biocatalysis for Drug Synthesis

Biocatalysis is constantly providing novel options for the synthesis of active pharmaceutical ingredients (APIs). In addition to drug development and manufacturing, biocatalysis also plays a role in drug discovery and can support many active ingredient syntheses at an early stage to build up entire scaffolds in a targeted and preparative manner. Recent progress in recruiting new enzymes by genome mining and screening or adapting their substrate, as well as product scope, by protein engineering has made biocatalysts a competitive tool applied in academic and industrial spheres. This is especially true for the advances in the field of nonribosomal peptide synthesis and enzyme cascades that are expanding the capabilities for the discovery and synthesis of new bioactive compounds via biotransformation. Here we highlight some of the most recent developments to add to the portfolio of biocatalysis with special relevance for the synthesis and late-stage functionalization of APIs, in order to bypass pure chemical processes.

Production of microhomogeneous glycopeptide by a mutated NGT according FuncLib with unique sugar as substrate

N-glycosylation is one of the most important post-translational modifications of proteins. Cytoplasmic soluble N-glycosyltransferase (NGT) exists in bacteria, which is able to transfer different monosaccharide from sugar nucleotide to the NXS/T(X ≠ Pro) consensus sequence in a polypeptide. At present, the NGT enzymes reported could transfer a variety of different sugars to protein, which will lead to the heterogeneity of the sugar chain and the complexity and instability of the structure and function of glycopeptides. According to the FuncLib algorithm, we obtained mutant ApNGT-P1 from ApNGT (the NGT from Actinobacillus pleuropneumoniae) with increased substrate specificity. Compared with the wild-type ApNGT, mutant ApNGT-P1 could only utilize UDP-Glc as sugar donors. The optimum temperature of ApNGT-P1 was about 40 °C and the optimum pH was 7.5-8.0 in PBS buffer. ApNGT-P1 exhibited better tolerance for K⁺, Mn²⁺, Ca²⁺, and Mg²⁺, but was strongly inhibited by Na⁺, Cu²⁺ and Zn²⁺. The mutant can be applied to the efficient production of glycosylated peptides or proteins with uniform glucose at their glycosylation sites. Besides, this work provided a feasible pathway for further studies on the improving donor substrates selectivity of NGTs.

Exploiting enzyme evolution for computational protein design

Recent years have seen an explosion of interest in understanding the physicochemical parameters that shape enzyme evolution, as well as substantial advances in computational enzyme design. This review discusses three areas where evolutionary information can be used as part of the design process: (i) using ancestral sequence reconstruction (ASR) to generate new starting points for enzyme design efforts; (ii) learning from how nature uses conformational dynamics in enzyme evolution to mimic this process in silico; and (iii) modular design of enzymes from smaller fragments, again mimicking the process by which nature appears to create new protein folds. Using showcase examples, we highlight the importance of incorporating evolutionary information to continue to push forward the boundaries of enzyme design studies.

A rationally designed c-di-AMP FRET biosensor to monitor nucleotide dynamics

3'3'-cyclic di-adenosine monophosphate (c-di-AMP) is an important nucleotide second messenger found throughout the bacterial domain of life. C-di-AMP is essential in many bacteria and regulates a diverse array of effector proteins controlling pathogenesis, cell wall homeostasis, osmoregulation, and central metabolism. Despite the ubiquity and importance of c-di-AMP, methods to detect this signaling molecule are limited, particularly at single cell resolution. In this work, crystallization of the Listeria monocytogenes c-di-AMP effector protein Lmo0553 enabled structure guided design of a Förster resonance energy transfer (FRET) based biosensor, which we have named CDA5. CDA5 is a fully genetically encodable, specific, and reversible biosensor which allows for the detection of c-di-AMP dynamics both in vitro and within live cells in a nondestructive manner. Our initial studies identify a distribution of c-di-AMP in Bacillus subtilis populations first grown in Luria Broth and then resuspended in diluted Luria Broth compatible with florescence analysis. Furthermore, we find that B. subtilis mutants lacking either a c-di-AMP phosphodiesterase or cyclase have respectively higher and lower FRET responses. These findings provide novel insight into the c-di-AMP distribution within bacterial populations and establish CDA5 as a powerful platform for characterizing new aspects of c-di-AMP regulation. Importance C-di-AMP is an important nucleotide second messenger for which detection methods are severely limited. In this work we engineer and implement a c-di-AMP specific FRET biosensor to remedy this dearth. We present this biosensor, CDA5, as a versatile tool to investigate previously intractable facets of c-di-AMP biology.

Computational Enzyme Engineering Pipelines for Optimized Production of Renewable Chemicals

To enable a sustainable supply of chemicals, novel biotechnological solutions are required that replace the reliance on fossil resources. One potential solution is to utilize tailored biosynthetic modules for the metabolic conversion of CO₂ or organic waste to chemicals and fuel by microorganisms. Currently, it is challenging to commercialize biotechnological processes for renewable chemical biomanufacturing because of a lack of highly active and specific biocatalysts. As experimental methods to engineer biocatalysts are time- and cost-intensive, it is important to establish efficient and reliable computational tools that can speed up the identification or optimization of selective, highly active, and stable enzyme variants for utilization in the biotechnological industry. Here, we review and suggest combinations of effective state-of-the-art software and online tools available for computational enzyme engineering pipelines to optimize metabolic pathways for the biosynthesis of renewable chemicals. Using examples relevant for biotechnology, we explain the underlying principles of enzyme engineering and design and illuminate future directions for automated optimization of biocatalysts for the assembly of synthetic metabolic pathways.

Prospects of Using Biocatalysis for the Synthesis and Modification of Polymers

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

Recent Advances in Biocatalysis with Chemical Modification and Expanded Amino Acid Alphabet

The two main strategies for enzyme engineering, directed evolution and rational design, have found widespread applications in improving the intrinsic activities of proteins. Although numerous advances have been achieved using these ground-breaking methods, the limited chemical diversity of the biopolymers, restricted to the 20 canonical amino acids, hampers creation of novel enzymes that Nature has never made thus far. To address this, much research has been devoted to expanding the protein sequence space via chemical modifications and/or incorporation of noncanonical amino acids (ncAAs). This review provides a balanced discussion and critical evaluation of the applications, recent advances, and technical breakthroughs in biocatalysis for three approaches: (i) chemical modification of cAAs, (ii) incorporation of ncAAs, and (iii) chemical modification of incorporated ncAAs. Furthermore, the applications of these approaches and the result on the functional properties and mechanistic study of the enzymes are extensively reviewed. We also discuss the design of artificial enzymes and directed evolution strategies for enzymes with ncAAs incorporated. Finally, we discuss the current challenges and future perspectives for biocatalysis using the expanded amino acid alphabet.

Designing better enzymes: Insights from directed evolution

De novo enzymes can be created by computational design and directed evolution. Here, we review recent insights into the origins of catalytic power in evolved designer enzymes to pinpoint opportunities for next-generation designs: Evolution precisely organizes active sites, introduces catalytic H-bonding networks, invokes electrostatic catalysis, and creates dynamical networks embedding the active site in a reactive protein scaffold. Such insights foster our fundamental knowledge of enzyme catalysis and fuel the future design of tailor-made enzymes.

Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently

The number of 'small' molecules that may be of interest to chemical biologists - chemical space - is enormous, but the fraction that have ever been made is tiny. Most strategies are discriminative, i.e. have involved 'forward' problems (have molecule, establish properties). However, we normally wish to solve the much harder generative or inverse problem (describe desired properties, find molecule). 'Deep' (machine) learning based on large-scale neural networks underpins technologies such as computer vision, natural language processing, driverless cars, and world-leading performance in games such as Go; it can also be applied to the solution of inverse problems in chemical biology. In particular, recent developments in deep learning admit the in silico generation of candidate molecular structures and the prediction of their properties, thereby allowing one to navigate (bio)chemical space intelligently. These methods are revolutionary but require an understanding of both (bio)chemistry and computer science to be exploited to best advantage. We give a high-level (non-mathematical) background to the deep learning revolution, and set out the crucial issue for chemical biology and informatics as a two-way mapping from the discrete nature of individual molecules to the continuous but high-dimensional latent representation that may best reflect chemical space. A variety of architectures can do this; we focus on a particular type known as variational autoencoders. We then provide some examples of recent successes of these kinds of approach, and a look towards the future.

Harnessing Conformational Plasticity to Generate Designer Enzymes

Recent years have witnessed an explosion of interest in understanding the role of conformational dynamics both in the evolution of new enzymatic activities from existing enzymes and in facilitating the emergence of enzymatic activity de novo on scaffolds that were previously non-catalytic. There are also an increasing number of examples in the literature of targeted engineering of conformational dynamics being successfully used to alter enzyme selectivity and activity. Despite the obvious importance of conformational dynamics to both enzyme function and evolvability, many (although not all) computational design approaches still focus either on pure sequence-based approaches or on using structures with limited flexibility to guide the design. However, there exist a wide variety of computational approaches that can be (re)purposed to introduce conformational dynamics as a key consideration in the design process. Coupled with laboratory evolution and more conventional existing sequence- and structure-based approaches, these techniques provide powerful tools for greatly expanding the protein engineering toolkit. This Perspective provides an overview of evolutionary studies that have dissected the role of conformational dynamics in facilitating the emergence of novel enzymes, as well as advances in computational approaches that allow one to target conformational dynamics as part of enzyme design. Harnessing conformational dynamics in engineering studies is a powerful paradigm with which to engineer the next generation of designer biocatalysts.