Repair of DNA Double-Strand Breaks by the Nonhomologous End Joining Pathway.

DNA double-strand breaks pose a serious threat to genome stability. In vertebrates, these breaks are predominantly repaired by nonhomologous end joining (NHEJ), which pairs DNA ends in a multiprotein synaptic complex to promote their direct ligation. NHEJ is a highly versatile pathway that uses an array of processing enzymes to modify damaged DNA ends and enable their ligation. The mechanisms of end synapsis and end processing have important implications for genome stability. Rapid and stable synapsis is necessary to limit chromosome translocations that result from the mispairing of DNA ends. Furthermore, end processing must be tightly regulated to minimize mutations at the break site. Here, we review our current mechanistic understanding of vertebrate NHEJ, with a particular focus on end synapsis and processing.

Metabolite-Enzyme Coevolution: From Single Enzymes to Metabolic Pathways and Networks.

How individual enzymes evolved is relatively well understood. However, individual enzymes rarely confer a physiological advantage on their own. Judging by its current state, the emergence of metabolism seemingly demanded the simultaneous emergence of many enzymes. Indeed, how multicomponent interlocked systems, like metabolic pathways, evolved is largely an open question. This complexity can be unlocked if we assume that survival of the fittest applies not only to genes and enzymes but also to the metabolites they produce. This review develops our current knowledge of enzyme evolution into a wider hypothesis of pathway and network evolution. We describe the current models for pathway evolution and offer an integrative metabolite-enzyme coevolution hypothesis. Our hypothesis addresses the origins of new metabolites and of new enzymes and the order of their recruitment. We aim to not only survey established knowledge but also present open questions and potential ways of addressing them.

Protein Evolution and Design.

This article introduces the Protein Evolution and Design theme of the Annual Review of Biochemistry Volume 87.

Directed Evolution of Protein Catalysts.

Directed evolution is a powerful technique for generating tailor-made enzymes for a wide range of biocatalytic applications. Following the principles of natural evolution, iterative cycles of mutagenesis and screening or selection are applied to modify protein properties, enhance catalytic activities, or develop completely new protein catalysts for non-natural chemical transformations. This review briefly surveys the experimental methods used to generate genetic diversity and screen or select for improved enzyme variants. Emphasis is placed on a key challenge, namely how to generate novel catalytic activities that expand the scope of natural reactions. Two particularly effective strategies, exploiting catalytic promiscuity and rational design, are illustrated by representative examples of successfully evolved enzymes. Opportunities for extending these approaches to more complex biocatalytic systems are also considered.

Boron catalysis in a designer enzyme.

Enzymes play an increasingly important role in improving the benignity and efficiency of chemical production, yet the diversity of their applications lags heavily behind chemical catalysts as a result of the relatively narrow range of reaction mechanisms of enzymes. The creation of enzymes containing non-biological functionalities facilitates reaction mechanisms outside nature's canon and paves the way towards fully programmable biocatalysis1-3. Here we present a completely genetically encoded boronic-acid-containing designer enzyme with organocatalytic reactivity not achievable with natural or engineered biocatalysts4,5. This boron enzyme catalyses the kinetic resolution of hydroxyketones by oxime formation, in which crucial interactions with the protein scaffold assist in the catalysis. A directed evolution campaign led to a variant with natural-enzyme-like enantioselectivities for several different substrates. The unique activation mode of the boron enzyme was confirmed using X-ray crystallography, high-resolution mass spectrometry (HRMS) and 11B NMR spectroscopy. Our study demonstrates that genetic-code expansion can be used to create evolvable enantioselective enzymes that rely on xenobiotic catalytic moieties such as boronic acids and access reaction mechanisms not reachable through catalytic promiscuity of natural or engineered enzymes.

Complete biosynthesis of QS-21 in engineered yeast.

QS-21 is a potent vaccine adjuvant and remains the only saponin-based adjuvant that has been clinically approved for use in humans1,2. However, owing to the complex structure of QS-21, its availability is limited. Today, the supply depends on laborious extraction from the Chilean soapbark tree or on low-yielding total chemical synthesis3,4. Here we demonstrate the complete biosynthesis of QS-21 and its precursors, as well as structural derivatives, in engineered yeast strains. The successful biosynthesis in yeast requires fine-tuning of the host's native pathway fluxes, as well as the functional and balanced expression of 38 heterologous enzymes. The required biosynthetic pathway spans seven enzyme families-a terpene synthase, P450s, nucleotide sugar synthases, glycosyltransferases, a coenzyme A ligase, acyl transferases and polyketide synthases-from six organisms, and mimics in yeast the subcellular compartmentalization of plants from the endoplasmic reticulum membrane to the cytosol. Finally, by taking advantage of the promiscuity of certain pathway enzymes, we produced structural analogues of QS-21 using this biosynthetic platform. This microbial production scheme will allow for the future establishment of a structure-activity relationship, and will thus enable the rational design of potent vaccine adjuvants.

Enantioselective [2+2]-cycloadditions with triplet photoenzymes.

Naturally evolved enzymes, despite their astonishingly large variety and functional diversity, operate predominantly through thermochemical activation. Integrating prominent photocatalysis modes into proteins, such as triplet energy transfer, could create artificial photoenzymes that expand the scope of natural biocatalysis1-3. Here, we exploit genetically reprogrammed, chemically evolved photoenzymes embedded with a synthetic triplet photosensitizer that are capable of excited-state enantio-induction4-6. Structural optimization through four rounds of directed evolution afforded proficient variants for the enantioselective intramolecular [2+2]-photocycloaddition of indole derivatives with good substrate generality and excellent enantioselectivities (up to 99% enantiomeric excess). A crystal structure of the photoenzyme-substrate complex elucidated the non-covalent interactions that mediate the reaction stereochemistry. This study expands the energy transfer reactivity7-10 of artificial triplet photoenzymes in a supramolecular protein cavity and unlocks an integrated approach to valuable enantioselective photochemical synthesis that is not accessible with either the synthetic or the biological world alone.

A designed photoenzyme for enantioselective [2+2] cycloadditions.

The ability to program new modes of catalysis into proteins would allow the development of enzyme families with functions beyond those found in nature. To this end, genetic code expansion methodology holds particular promise, as it allows the site-selective introduction of new functional elements into proteins as noncanonical amino acid side chains1-4. Here we exploit an expanded genetic code to develop a photoenzyme that operates by means of triplet energy transfer (EnT) catalysis, a versatile mode of reactivity in organic synthesis that is not accessible to biocatalysis at present5-12. Installation of a genetically encoded photosensitizer into the beta-propeller scaffold of DA_20_00 (ref. 13) converts a de novo Diels-Alderase into a photoenzyme for [2+2] cycloadditions (EnT1.0). Subsequent development and implementation of a platform for photoenzyme evolution afforded an efficient and enantioselective enzyme (EnT1.3, up to 99% enantiomeric excess (e.e.)) that can promote intramolecular and bimolecular cycloadditions, including transformations that have proved challenging to achieve selectively with small-molecule catalysts. EnT1.3 performs >300 turnovers and, in contrast to small-molecule photocatalysts, can operate effectively under aerobic conditions and at ambient temperatures. An X-ray crystal structure of an EnT1.3-product complex shows how multiple functional components work in synergy to promote efficient and selective photocatalysis. This study opens up a wealth of new excited-state chemistry in protein active sites and establishes the framework for developing a new generation of enantioselective photocatalysts.

Retrograde signaling by the plastidial metabolite MEcPP regulates expression of nuclear stress-response genes.

Plastid-derived signals are known to coordinate expression of nuclear genes encoding plastid-localized proteins in a process termed retrograde signaling. To date, the identity of retrograde-signaling molecules has remained elusive. Here, we show that methylerythritol cyclodiphosphate (MEcPP), a precursor of isoprenoids produced by the plastidial methylerythritol phosphate (MEP) pathway, elicits the expression of selected stress-responsive nuclear-encoded plastidial proteins. Genetic and pharmacological manipulations of the individual MEP pathway metabolite levels demonstrate the high specificity of MEcPP as an inducer of these targeted stress-responsive genes. We further demonstrate that abiotic stresses elevate MEcPP levels, eliciting the expression of the aforementioned genes. We propose that the MEP pathway, in addition to producing isoprenoids, functions as a stress sensor and a coordinator of expression of targeted stress-responsive nuclear genes via modulation of the levels of MEcPP, a specific and critical retrograde-signaling metabolite.

Benchmark reaction rates, the stability of biological molecules in water, and the evolution of catalytic power in enzymes.

The rates of enzyme reactions fall within a relatively narrow range. To estimate the rate enhancements produced by enzymes, and their expected affinities for transition state analog inhibitors, it is necessary to measure the rates of the corresponding reactions in water in the absence of a catalyst. This review describes the spontaneous cleavages of C-C, C-H, C-N, C-O, P-O, and S-O bonds in biological molecules, as well as the uncatalyzed reactions that correspond to phosphoryl transfer reactions catalyzed by kinases and to peptidyl transfer in the ribosome. The rates of these reactions, some with half-lives in excess of one million years, span an overall range of 10¹9-fold. Moreover, the slowest reactions tend to be most sensitive to temperature, with rates that increase as much as 107-fold when the temperature is raised from 25° to 100°C. That tendency collapses, by many orders of magnitude, the time that would have been required for chemical evolution on a warm earth. If the catalytic effect of primitive enzymes, like that of modern enzymes and many nonenzymatic catalysts, were mainly to reduce a reaction's enthalpy of activation, then the resulting rate enhancement would have increased automatically as the surroundings cooled. By reducing the time required for early chemical evolution in a warm environment, these findings counter the view that not enough time has passed for terrestrial life to have evolved to its present level of complexity.

DeepEnzyme: a robust deep learning model for improved enzyme turnover number prediction by utilizing features of protein 3D-structures.

Turnover numbers (kcat), which indicate an enzyme's catalytic efficiency, have a wide range of applications in fields including protein engineering and synthetic biology. Experimentally measuring the enzymes' kcat is always time-consuming. Recently, the prediction of kcat using deep learning models has mitigated this problem. However, the accuracy and robustness in kcat prediction still needs to be improved significantly, particularly when dealing with enzymes with low sequence similarity compared to those within the training dataset. Herein, we present DeepEnzyme, a cutting-edge deep learning model that combines the most recent Transformer and Graph Convolutional Network (GCN) to capture the information of both the sequence and 3D-structure of a protein. To improve the prediction accuracy, DeepEnzyme was trained by leveraging the integrated features from both sequences and 3D-structures. Consequently, DeepEnzyme exhibits remarkable robustness when processing enzymes with low sequence similarity compared to those in the training dataset by utilizing additional features from high-quality protein 3D-structures. DeepEnzyme also makes it possible to evaluate how point mutations affect the catalytic activity of the enzyme, which helps identify residue sites that are crucial for the catalytic function. In summary, DeepEnzyme represents a pioneering effort in predicting enzymes' kcat values with improved accuracy and robustness compared to previous algorithms. This advancement will significantly contribute to our comprehension of enzyme function and its evolutionary patterns across species.

Noncanonical Amino Acids in Biocatalysis.

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

An enzymatic Alder-ene reaction.

An ongoing challenge in chemical research is to design catalysts that select the outcomes of the reactions of complex molecules. Chemists rely on organocatalysts or transition metal catalysts to control stereoselectivity, regioselectivity and periselectivity (selectivity among possible pericyclic reactions). Nature achieves these types of selectivity with a variety of enzymes such as the recently discovered pericyclases-a family of enzymes that catalyse pericyclic reactions1. Most characterized enzymatic pericyclic reactions have been cycloadditions, and it has been difficult to rationalize how the observed selectivities are achieved2-13. Here we report the discovery of two homologous groups of pericyclases that catalyse distinct reactions: one group catalyses an Alder-ene reaction that was, to our knowledge, previously unknown in biology; the second catalyses a stereoselective hetero-Diels-Alder reaction. Guided by computational studies, we have rationalized the observed differences in reactivities and designed mutant enzymes that reverse periselectivities from Alder-ene to hetero-Diels-Alder and vice versa. A combination of in vitro biochemical characterizations, computational studies, enzyme co-crystal structures, and mutational studies illustrate how high regioselectivity and periselectivity are achieved in nearly identical active sites.

Enzyme promiscuity: a mechanistic and evolutionary perspective.

Many, if not most, enzymes can promiscuously catalyze reactions, or act on substrates, other than those for which they evolved. Here, we discuss the structural, mechanistic, and evolutionary implications of this manifestation of infidelity of molecular recognition. We define promiscuity and related phenomena and also address their generality and physiological implications. We discuss the mechanistic enzymology of promiscuity--how enzymes, which generally exert exquisite specificity, catalyze other, and sometimes barely related, reactions. Finally, we address the hypothesis that promiscuous enzymatic activities serve as evolutionary starting points and highlight the unique evolutionary features of promiscuous enzyme functions.

Enzyme-catalysed [6+4] cycloadditions in the biosynthesis of natural products.

Pericyclic reactions are powerful transformations for the construction of carbon-carbon and carbon-heteroatom bonds in organic synthesis. Their role in biosynthesis is increasingly apparent, and mechanisms by which pericyclases can catalyse reactions are of major interest1. [4+2] cycloadditions (Diels-Alder reactions) have been widely used in organic synthesis2 for the formation of six-membered rings and are now well-established in biosynthesis3-6. [6+4] and other 'higher-order' cycloadditions were predicted7 in 1965, and are now increasingly common in the laboratory despite challenges arising from the generation of a highly strained ten-membered ring system8,9. However, although enzyme-catalysed [6+4] cycloadditions have been proposed10-12, they have not been proven to occur. Here we demonstrate a group of enzymes that catalyse a pericyclic [6+4] cycloaddition, which is a crucial step in the biosynthesis of streptoseomycin-type natural products. This type of pericyclase catalyses [6+4] and [4+2] cycloadditions through a single ambimodal transition state, which is consistent with previous proposals11,12. The [6+4] product is transformed to a less stable [4+2] adduct via a facile Cope rearrangement, and the [4+2] adduct is converted into the natural product enzymatically. Crystal structures of three pericyclases, computational simulations of potential energies and molecular dynamics, and site-directed mutagenesis establish the mechanism of this transformation. This work shows how enzymes are able to catalyse concerted pericyclic reactions involving ambimodal transition states.

dbCAN-seq update: CAZyme gene clusters and substrates in microbiomes.

Carbohydrate Active EnZymes (CAZymes) are significantly important for microbial communities to thrive in carbohydrate rich environments such as animal guts, agricultural soils, forest floors, and ocean sediments. Since 2017, microbiome sequencing and assembly have produced numerous metagenome assembled genomes (MAGs). We have updated our dbCAN-seq database (https://bcb.unl.edu/dbCAN_seq) to include the following new data and features: (i) ∼498 000 CAZymes and ∼169 000 CAZyme gene clusters (CGCs) from 9421 MAGs of four ecological (human gut, human oral, cow rumen, and marine) environments; (ii) Glycan substrates for 41 447 (24.54%) CGCs inferred by two novel approaches (dbCAN-PUL homology search and eCAMI subfamily majority voting) (the two approaches agreed on 4183 CGCs for substrate assignments); (iii) A redesigned CGC page to include the graphical display of CGC gene compositions, the alignment of query CGC and subject PUL (polysaccharide utilization loci) of dbCAN-PUL, and the eCAMI subfamily table to support the predicted substrates; (iv) A statistics page to organize all the data for easy CGC access according to substrates and taxonomic phyla; and (v) A batch download page. In summary, this updated dbCAN-seq database highlights glycan substrates predicted for CGCs from microbiomes. Future work will implement the substrate prediction function in our dbCAN2 web server.

Leveraging intrinsic flexibility to engineer enhanced enzyme catalytic activity.

Dynamic motions of enzymes occurring on a broad range of timescales play a pivotal role in all steps of the reaction pathway, including substrate binding, catalysis, and product release. However, it is unknown whether structural information related to conformational flexibility can be exploited for the directed evolution of enzymes with higher catalytic activity. Here, we show that mutagenesis of residues exclusively located at flexible regions distal to the active site of Homo sapiens kynureninase (HsKYNase) resulted in the isolation of a variant (BF-HsKYNase) in which the rate of the chemical step toward kynurenine was increased by 45-fold. Mechanistic pre­steady-state kinetic analysis of the wild type and the evolved enzyme shed light on the underlying effects of distal mutations (>10 Å from the active site) on the rate-limiting step of the catalytic cycle. Hydrogen-deuterium exchange coupled to mass spectrometry and molecular dynamics simulations revealed that the amino acid substitutions in BF-HsKYNase allosterically affect the flexibility of the pyridoxal-5'-phosphate (PLP) binding pocket, thereby impacting the rate of chemistry, presumably by altering the conformational ensemble and sampling states more favorable to the catalyzed reaction.

Frustration can Limit the Adaptation of Promiscuous Enzymes Through Gene Duplication and Specialisation.

Virtually all enzymes catalyse more than one reaction, a phenomenon known as enzyme promiscuity. It is unclear whether promiscuous enzymes are more often generalists that catalyse multiple reactions at similar rates or specialists that catalyse one reaction much more efficiently than other reactions. In addition, the factors that shape whether an enzyme evolves to be a generalist or a specialist are poorly understood. To address these questions, we follow a three-pronged approach. First, we examine the distribution of promiscuity in empirical enzymes reported in the BRENDA database. We find that the promiscuity distribution of empirical enzymes is bimodal. In other words, a large fraction of promiscuous enzymes are either generalists or specialists, with few intermediates. Second, we demonstrate that enzyme biophysics is not sufficient to explain this bimodal distribution. Third, we devise a constraint-based model of promiscuous enzymes undergoing duplication and facing selection pressures favouring subfunctionalization. The model posits the existence of constraints between the catalytic efficiencies of an enzyme for different reactions and is inspired by empirical case studies. The promiscuity distribution predicted by our constraint-based model is consistent with the empirical bimodal distribution. Our results suggest that subfunctionalization is possible and beneficial only in certain enzymes. Furthermore, the model predicts that conflicting constraints and selection pressures can cause promiscuous enzymes to enter a 'frustrated' state, in which competing interactions limit the specialisation of enzymes. We find that frustration can be both a driver and an inhibitor of enzyme evolution by duplication and subfunctionalization. In addition, our model predicts that frustration becomes more likely as enzymes catalyse more reactions, implying that natural selection may prefer catalytically simple enzymes. In sum, our results suggest that frustration may play an important role in enzyme evolution.

Data-Driven Protein Engineering for Improving Catalytic Activity and Selectivity.

Protein engineering is essential for altering the substrate scope, catalytic activity and selectivity of enzymes for applications in biocatalysis. However, traditional approaches, such as directed evolution and rational design, encounter the challenge in dealing with the experimental screening process of a large protein mutation space. Machine learning methods allow the approximation of protein fitness landscapes and the identification of catalytic patterns using limited experimental data, thus providing a new avenue to guide protein engineering campaigns. In this concept article, we review machine learning models that have been developed to assess enzyme-substrate-catalysis performance relationships aiming to improve enzymes through data-driven protein engineering. Furthermore, we prospect the future development of this field to provide additional strategies and tools for achieving desired activities and selectivities.

Systems and Methods for Continuous Evolution of Enzymes.

Directed evolution generates novel biomolecules with desired functions by iteratively diversifying the genetic sequence of wildtype biomolecules, relaying the genetic information to the molecule with function, and selecting the variants that progresses towards the properties of interest. While traditional directed evolution consumes significant labor and time for each step, continuous evolution seeks to automate all steps so directed evolution can proceed with minimum human intervention and dramatically shortened time. A major application of continuous evolution is the generation of novel enzymes, which catalyze reactions under conditions that are not favorable to their wildtype counterparts, or on altered substrates. The challenge to continuously evolve enzymes lies in automating sufficient, unbiased gene diversification, providing selection for a wide array of reaction types, and linking the genetic information to the phenotypic function. Over years of development, continuous evolution has accumulated versatile strategies to address these challenges, enabling its use as a general tool for enzyme engineering. As the capability of continuous evolution continues to expand, its impact will increase across various industries. In this review, we summarize the working mechanisms of recently developed continuous evolution strategies, discuss examples of their applications focusing on enzyme evolution, and point out their limitations and future directions.