Large-Scale Analysis Exploring Evolution of Catalytic Machineries and Mechanisms in Enzyme Superfamilies

Paradigms of convergent evolution in enzymes

There are many occurrences of enzymes catalysing the same reaction but having significantly different structures. Leveraging the comprehensive information on enzymes stored in the Mechanism and Catalytic Site Atlas (M-CSA), we present a collection of 34 cases for which there is sufficient evidence of functional convergence without an evolutionary link. For each case, we compare enzymes which have identical Enzyme Commission numbers (i.e. catalyse the same reaction), but different identifiers in the CATH data resource (i.e. different folds). We focus on similarities between their sequences, structures, active site geometries, cofactors and catalytic mechanisms. These features are then assessed to evaluate whether all the evidence for these structurally diverse proteins supports their independent evolution to catalyse the same chemical reaction. Our approach combines published literature information with knowledge-based computational resources from, amongst others, M-CSA, PDBe and PDBsum, supported by tailor-made software to explore active site structures and assess similarities in mechanism. We find that there are multiple types of convergent functional evolution observed to date, and it is necessary to investigate sequence, structure, active site geometry and enzyme mechanisms to describe such convergence accurately.

Enzyme function and evolution through the lens of bioinformatics

Enzymes have been shaped by evolution over billions of years to catalyse the chemical reactions that support life on earth. Dispersed in the literature, or organised in online databases, knowledge about enzymes can be structured in distinct dimensions, either related to their quality as biological macromolecules, such as their sequence and structure, or related to their chemical functions, such as the catalytic site, kinetics, mechanism, and overall reaction. The evolution of enzymes can only be understood when each of these dimensions is considered. In addition, many of the properties of enzymes only make sense in the light of evolution. We start this review by outlining the main paradigms of enzyme evolution, including gene duplication and divergence, convergent evolution, and evolution by recombination of domains. In the second part, we overview the current collective knowledge about enzymes, as organised by different types of data and collected in several databases. We also highlight some increasingly powerful computational tools that can be used to close gaps in understanding, in particular for types of data that require laborious experimental protocols. We believe that recent advances in protein structure prediction will be a powerful catalyst for the prediction of binding, mechanism, and ultimately, chemical reactions. A comprehensive mapping of enzyme function and evolution may be attainable in the near future.

The 3D Modules of Enzyme Catalysis: Deconstructing Active Sites into Distinct Functional Entities

Enzyme catalysis is governed by a limited toolkit of residues and organic or inorganic co-factors. Therefore, it is expected that recurring residue arrangements will be found across the enzyme space, which perform a defined catalytic function, are structurally similar and occur in unrelated enzymes. Leveraging the integrated information in the Mechanism and Catalytic Site Atlas (M-CSA) (enzyme structure, sequence, catalytic residue annotations, catalysed reaction, detailed mechanism description), 3D templates were derived to represent compact groups of catalytic residues. A fuzzy template-template search, allowed us to identify those recurring motifs, which are conserved or convergent, that we define as the "modules of enzyme catalysis". We show that a large fraction of these modules facilitate binding of metal ions, co-factors and substrates, and are frequently the result of convergent evolution. A smaller number of convergent modules perform a well-defined catalytic role, such as the variants of the catalytic triad (i.e. Ser-His-Asp/Cys-His-Asp) and the saccharide-cleaving Asp/Glu triad. It is also shown that enzymes whose functions have diverged during evolution preserve regions of their active site unaltered, as shown by modules performing similar or identical steps of the catalytic mechanism. We have compiled a comprehensive library of catalytic modules, that characterise a broad spectrum of enzymes. These modules can be used as templates in enzyme design and for better understanding catalysis in 3D.

MAHOMES II: A webserver for predicting if a metal binding site is enzymatic

Recent advances have enabled high-quality computationally generated structures for proteins with no solved crystal structures. However, protein function data remains largely limited to experimental methods and homology mapping. Since structure determines function, it is natural that methods capable of using computationally generated structures for functional annotations need to be advanced. Our laboratory recently developed a method to distinguish between metalloenzyme and nonenzyme sites. Here we report improvements to this method by upgrading our physicochemical features to alleviate the need for structures with sub-angstrom precision and using machine learning to reduce training data labeling error. Our improved classifier identifies protein bound metal sites as enzymatic or nonenzymatic with 94% precision and 92% recall. We demonstrate that both adjustments increased predictive performance and reliability on sites with sub-angstrom variations. We constructed a set of predicted metalloprotein structures with no solved crystal structures and no detectable homology to our training data. Our model had an accuracy of 90%-97.5% depending on the quality of the predicted structures included in our test. Finally, we found the physicochemical trends that drove this model's successful performance were local protein density, second shell ionizable residue burial, and the pocket's accessibility to the site. We anticipate that our model's ability to correctly identify catalytic metal sites could enable identification of new enzymatic mechanisms and improve de novo metalloenzyme design success rates.

MAHOMES II: A webserver for predicting if a metal binding site is enzymatic

Recent advances have enabled high-quality computationally generated structures for proteins with no solved crystal structures. However, protein function data remains largely limited to experimental methods and homology mapping. Since structure determines function, it is natural that methods capable of using computationally generated structures for functional annotations need to be advanced. Our laboratory recently developed a method to distinguish between metalloenzyme and non-enzyme sites. Here we report improvements to this method by upgrading our physicochemical features to alleviate the need for structures with sub-angstrom precision and using machine learning to reduce training data labeling error. Our improved classifier identifies protein bound metal sites as enzymatic or non-enzymatic with 94% precision and 92% recall. We demonstrate that both adjustments increased predictive performance and reliability on sites with sub-angstrom variations. We constructed a set of predicted metalloprotein structures with no solved crystal structures and no detectable homology to our training data. Our model had an accuracy of 90 - 97.5% depending on the quality of the predicted structures included in our test. Finally, we found the physicochemical trends that drove this model's successful performance were local protein density, second shell ionizable residue burial, and the pocket's accessibility to the site. We anticipate that our model's ability to correctly identify catalytic metal sites could enable identification of new enzymatic mechanisms and improve de novo metalloenzyme design success rates.

Significance statement

Identification of enzyme active sites on proteins with unsolved crystallographic structures can accelerate discovery of novel biochemical reactions, which can impact healthcare, industrial processes, and environmental remediation. Our lab has developed an ML tool for predicting sites on computationally generated protein structures as enzymatic and non-enzymatic. We have made our tool available on a webserver, allowing the scientific community to rapidly search previously unknown protein function space.

Cold-Active Lipases and Esterases: A Review on Recombinant Overexpression and Other Essential Issues

Cold environments characterised by diverse temperatures close to or below the water freezing point dominate about 80% of the Earth's biosphere. One of the survival strategies adopted by microorganisms living in cold environments is their expression of cold-active enzymes that enable them to perform an efficient metabolic flux at low temperatures necessary to thrive and reproduce under those constraints. Cold-active enzymes are ideal biocatalysts that can reduce the need for heating procedures and improve industrial processes' quality, sustainability, and cost-effectiveness. Despite their wide applications, their industrial usage is still limited, and the major contributing factor is the lack of complete understanding of their structure and cold adaptation mechanisms. The current review looked at the recombinant overexpression, purification, and recent mechanism of cold adaptation, various approaches for purification, and three-dimensional (3D) crystal structure elucidation of cold-active lipases and esterase.

The Role of Gene Duplication in the Divergence of Enzyme Function: A Comparative Approach

Gene duplication is a crucial process involved in the appearance of new genes and functions. It is thought to have played a major role in the growth of enzyme families and the expansion of metabolism at the biosphere's dawn and in recent times. Here, we analyzed paralogous enzyme content within each of the seven enzymatic classes for a representative sample of prokaryotes by a comparative approach. We found a high ratio of paralogs for three enzymatic classes: oxidoreductases, isomerases, and translocases, and within each of them, most of the paralogs belong to only a few subclasses. Our results suggest an intricate scenario for the evolution of prokaryotic enzymes, involving different fates for duplicated enzymes fixed in the genome, where around 20-40% of prokaryotic enzymes have paralogs. Intracellular organisms have a lesser ratio of duplicated enzymes, whereas free-living enzymes show the highest ratios. We also found that phylogenetically close phyla and some unrelated but with the same lifestyle share similar genomic and biochemical traits, which ultimately support the idea that gene duplication is associated with environmental adaptation.

CATH functional families predict functional sites in proteins

MOTIVATION

Identification of functional sites in proteins is essential for functional characterization, variant interpretation and drug design. Several methods are available for predicting either a generic functional site, or specific types of functional site. Here, we present FunSite, a machine learning predictor that identifies catalytic, ligand-binding and protein-protein interaction functional sites using features derived from protein sequence and structure, and evolutionary data from CATH functional families (FunFams).

RESULTS

FunSite's prediction performance was rigorously benchmarked using cross-validation and a holdout dataset. FunSite outperformed other publicly available functional site prediction methods. We show that conserved residues in FunFams are enriched in functional sites. We found FunSite's performance depends greatly on the quality of functional site annotations and the information content of FunFams in the training data. Finally, we analyze which structural and evolutionary features are most predictive for functional sites.

AVAILABILITYAND IMPLEMENTATION

https://github.com/UCL/cath-funsite-predictor.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Evolution of Enzyme Function and the Development of Catalytic Efficiency: Triosephosphate Isomerase, Jeremy R. Knowles, and W. John Albery

Every reader knows that an enzyme accelerates a reaction by reducing the activation-energy barrier. However, understanding how this is achieved by the structure of the enzyme and its interactions with stable complexes and transition states and, then, using this to (re)design enzymes to catalyze novel reactions remain the "holy grail" of mechanistic enzymology. The necessary foundation is the free-energy profile that specifies the energies of the bound substate, product, and intervening intermediates as well as the transition states by which they are interconverted. When this free-energy profile is compared to that for the uncatalyzed reaction, strategies for establishing and enhancing catalysis can be identified. This Perspective reminds readers that the first free-energy profile determined for an enzyme-catalyzed reaction, that for triosephosphate isomerase, was published in Biochemistry in 1976 by Jeremy R. Knowles, W. John Albery, and co-workers. They used the profile to propose three steps of increasing "subtlety" that can be influenced by evolutionary pressure to increase the flux through the reaction coordinate: (1) "uniform binding" of the substrate, product, and intermediates; (2) "differential binding" of complexes so that these are isoenergetic (to minimize the energy of the intervening transition states); and (3) "catalysis of an elementary step" in which the transition state for the kinetically significant chemical step is stabilized so that flux can be determined by the rate of substrate binding or product dissociation. These papers continue to guide mechanistic studies of enzyme-catalyzed reactions and provide principles for the (re)design of novel enzymes.

Differential Evolution of α-Glucan Water Dikinase (GWD) in Plants

The alpha-glucan water dikinase (GWD) enzyme catalyzes starch phosphorylation, an integral step in transitory starch degradation. The high phosphate content in stored starch has great industrial value, due to its physio–chemical properties making it more versatile, although the phosphate content of stored starch varies depending on the botanical source. In this study, we used various computational approaches to gain insights into the evolution of the GWD protein in 48 plant species with possible roles in enzyme function and alteration of phosphate content in their stored starch. Our analyses identified deleterious mutations, particularly in the highly conserved 5 aromatic amino acid residues in the dual tandem carbohydrate binding modules (CBM-45) of GWD protein in C. zofingiensis, G. hirsutum, A. protothecoides, P. miliaceum, and C. reinhardtii. These findings will inform experimental designs for simultaneous repression of genes coding for GWD and the predicted interacting proteins to elucidate the role this enzyme plays in starch degradation. Our results reveal significant diversity in the evolution of GWD enzyme across plant species, which may be evolutionarily advantageous according to the varying needs for phosphorylated stored starch between plants and environments.

Enzyme evolution in natural products biosynthesis: target- or diversity-oriented?

Natural product biosynthesis (NPB) is the Panda's Thumb of evolutionary biochemistry. Arm races between organisms, and ever-changing environments, result in relentless innovation. This review focusses on enzyme evolution in NPB. First, we review cases of de novo emergence, whereby a completely new enzymatic activity arose in a ligand-binding protein, or a new enzyme emerged including a completely new scaffold. Second, we briefly review the current models for enzyme evolution, and how they explain the inherent promiscuity of NPB enzymes and their tendency to produce multiple related products. We thus suggest that NPB enzymes a priori evolved to generate a specific product; they are, however, trapped in a multifunctional, generalist evolutionary state and thereby produce a diversity of products.

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.

A strategy for large-scale comparison of evolutionary- and reaction-based classifications of enzyme function

Determining the molecular function of enzymes discovered by genome sequencing represents a primary foundation for understanding many aspects of biology. Historically, classification of enzyme reactions has used the enzyme nomenclature system developed to describe the overall reactions performed by biochemically characterized enzymes, irrespective of their associated sequences. In contrast, functional classification and assignment for the millions of protein sequences of unknown function now available is largely done in two computational steps, first by similarity-based assignment of newly obtained sequences to homologous groups, followed by transferring to them the known functions of similar biochemically characterized homologs. Due to the fundamental differences in their etiologies and practice, `how' these chemistry- and evolution-centric functional classification systems relate to each other has been difficult to explore on a large scale. To investigate this issue in a new way, we integrated two published ontologies that had previously described each of these classification systems independently. The resulting infrastructure was then used to compare the functional assignments obtained from each classification system for the well-studied and functionally diverse enolase superfamily. Mapping these function assignments to protein structure and reaction similarity networks shows a profound and complex disconnect between the homology- and chemistry-based classification systems. This conclusion mirrors previous observations suggesting that except for closely related sequences, facile annotation transfer from small numbers of characterized enzymes to the huge number uncharacterized homologs to which they are related is problematic. Our extension of these comparisons to large enzyme superfamilies in a computationally intelligent manner provides a foundation for new directions in protein function prediction for the huge proportion of sequences of unknown function represented in major databases. Interactive sequence, reaction, substrate and product similarity networks computed for this work for the enolase and two other superfamilies are freely available for download from the Structure Function Linkage Database Archive (http://sfld.rbvi.ucsf.edu).

Ligand Binding Site Structure Influences the Evolution of Protein Complex Function and Topology

It has been suggested that the evolution of protein complexes is significantly influenced by stochastic, non-adaptive processes. Using ligand binding as a proxy of function, we show that the structure of ligand-binding sites significantly influences the evolution of protein complexes. We show that homomers with multi-chain binding sites (MBSs) evolve new functions slower than monomers or other homomers, and those binding cofactors and metals have more conserved quaternary structure than other homomers. Moreover, the ligands and ligand-binding pockets of homologous MBS homomers are more similar than monomers and other homomers. Our results suggest strong evolutionary selection for quaternary structure in cofactor-binding MBS homomers, whereas neutral processes are more important in complexes with single-chain binding sites. They also have pharmacological implications, suggesting that complexes with single-chain binding sites are better targets for selective drugs, whereas MBS homomers are good candidates for broad-spectrum antibiotic and multitarget drug design.

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Ligand binding site structure significantly influences protein function evolution

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MBS homomers have more similar ligand binding pockets than monomers and other homomers

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Cofactor and metal-binding MBS homomers have more conserved QS than other homomers

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MBS homomers are promising targets for developing antibiotics and multitarget drugs

Emerging concepts in pseudoenzyme classification, evolution, and signaling

The 21st century is witnessing an explosive surge in our understanding of pseudoenzyme-driven regulatory mechanisms in biology. Pseudoenzymes are proteins that have sequence homology with enzyme families but that are proven or predicted to lack enzyme activity due to mutations in otherwise conserved catalytic amino acids. The best-studied pseudoenzymes are pseudokinases, although examples from other families are emerging at a rapid rate as experimental approaches catch up with an avalanche of freely available informatics data. Kingdom-wide analysis in prokaryotes, archaea and eukaryotes reveals that between 5 and 10% of proteins that make up enzyme families are pseudoenzymes, with notable expansions and contractions seemingly associated with specific signaling niches. Pseudoenzymes can allosterically activate canonical enzymes, act as scaffolds to control assembly of signaling complexes and their localization, serve as molecular switches, or regulate signaling networks through substrate or enzyme sequestration. Molecular analysis of pseudoenzymes is rapidly advancing knowledge of how they perform noncatalytic functions and is enabling the discovery of unexpected, and previously unappreciated, functions of their intensively studied enzyme counterparts. Notably, upon further examination, some pseudoenzymes have previously unknown enzymatic activities that could not have been predicted a priori. Pseudoenzymes can be targeted and manipulated by small molecules and therefore represent new therapeutic targets (or anti-targets, where intervention should be avoided) in various diseases. In this review, which brings together broad bioinformatics and cell signaling approaches in the field, we highlight a selection of findings relevant to a contemporary understanding of pseudoenzyme-based biology.

Protein engineers turned evolutionists-the quest for the optimal starting point

The advent of laboratory directed evolution yielded a fruitful crosstalk between the disciplines of molecular evolution and bio-engineering. Here, we outline recent developments in both disciplines with respect to how one can identify the best starting points for directed evolution, such that highly efficient and robust tailor-made enzymes can be obtained with minimal optimization. Directed evolution studies have highlighted essential features of engineer-able enzymes: highly stable, mutationally robust enzymes with the capacity to accept a broad range of substrates. Robust, evolvable enzymes can be inferred from the natural sequence record. Broad substrate spectrum relates to conformational plasticity and can also be predicted by phylogenetic analyses and/or by computational design. Overall, an increasingly powerful toolkit is becoming available for identifying optimal starting points including network analyses of enzyme superfamilies and other bioinformatics methods.

Exploring Enzyme Evolution from Changes in Sequence, Structure, and Function

The goal of our research is to increase our understanding of how biology works at the molecular level, with a particular focus on how enzymes evolve their functions through adaptations to generate new specificities and mechanisms. FunTree (Sillitoe and Furnham, Nucleic Acids Res 44:D317-D323, 2016) is a resource that brings together sequence, structure, phylogenetic, and chemical and mechanistic information for 2340 CATH superfamilies (Sillitoe et al., Nucleic Acids Res 43:D376-D381, 2015) (which all contain at least one enzyme) to allow evolution to be investigated within a structurally defined superfamily.We will give an overview of FunTree's use of sequence and structural alignments to cluster proteins within a superfamily into structurally similar groups (SSGs) and generate phylogenetic trees augmented by ancestral character estimations (ACE). This core information is supplemented with new measures of functional similarity (Rahman et al., Nat Methods 11:171-174, 2014) to compare enzyme reactions based on overall bond changes, reaction centers (the local environment atoms involved in the reaction), and the structural similarities of the metabolites involved in the reaction. These trees are also decorated with taxonomic and Enzyme Commission (EC) code and GO annotations, forming the basis of a comprehensive web interface that can be found at http://www.funtree.info . In this chapter, we will discuss the various analyses and supporting computational tools in more detail, describing the steps required to extract information.

Structural Studies based on two Lysine Dioxygenases with Distinct Regioselectivity Brings Insights Into Enzyme Specificity within the Clavaminate Synthase-Like Family

Iron(II)/α-ketoacid-dependent oxygenases (αKAOs) are enzymes that catalyze the oxidation of unactivated C-H bonds, mainly through hydroxylation. Among these, those that are active towards amino-acids and their derivatives are grouped in the Clavaminate Synthase Like (CSL) family. CSL enzymes exhibit high regio- and stereoselectivities with strict substrate specificity. This study reports the structural elucidation of two new regiodivergent members, KDO1 and KDO5, active towards lysine, and the structural and computational analysis of the whole family through modelling and classification of active sites. The structures of KDO1 and KDO5 in complex with their ligands show that one exact position in the active site controls the regioselectivity of the reaction. Our results suggest that the substrate specificity and high stereoselectivity typical of this family is linked to a lid that closes up in order to form a sub-pocket around the side chain of the substrate. This dynamic lid is found throughout the family with varying sequence and length and is associated with a conserved stable dimeric interface. Results from this study could be a starting-point for exploring the functional diversity of the CSL family and direct in vitro screening in the search for new enzymatic activities.

Insights into a dual function amide oxidase/macrocyclase from lankacidin biosynthesis

Acquisition of new catalytic activity is a relatively rare evolutionary event. A striking example appears in the pathway to the antibiotic lankacidin, as a monoamine oxidase (MAO) family member, LkcE, catalyzes both an unusual amide oxidation, and a subsequent intramolecular Mannich reaction to form the polyketide macrocycle. We report evidence here for the molecular basis for this dual activity. The reaction sequence involves several essential active site residues and a conformational change likely comprising an interdomain hinge movement. These features, which have not previously been described in the MAO family, both depend on a unique dimerization mode relative to all structurally characterized members. Taken together, these data add weight to the idea that designing new multifunctional enzymes may require changes in both architecture and catalytic machinery. Encouragingly, however, our data also show LkcE to bind alternative substrates, supporting its potential utility as a general cyclization catalyst in synthetic biology.

The monoamine oxidase family member LkcE is an enzyme from the lankacidin polyketide biosynthetic pathway, where it catalyzes an amide oxidation followed by an intramolecular Mannich reaction, yielding the polyketide macrocycle. Here the authors characterize LkcE and present several of its crystal structures, which explains the unusual dual activity of LkcE.

Evolution of cyclohexadienyl dehydratase from an ancestral solute-binding protein

The emergence of enzymes through the neofunctionalization of noncatalytic proteins is ultimately responsible for the extraordinary range of biological catalysts observed in nature. Although the evolution of some enzymes from binding proteins can be inferred by homology, we have a limited understanding of the nature of the biochemical and biophysical adaptations along these evolutionary trajectories and the sequence in which they occurred. Here we reconstructed and characterized evolutionary intermediate states linking an ancestral solute-binding protein to the extant enzyme cyclohexadienyl dehydratase. We show how the intrinsic reactivity of a desolvated general acid was harnessed by a series of mutations radiating from the active site, which optimized enzyme-substrate complementarity and transition-state stabilization and minimized sampling of noncatalytic conformations. Our work reveals the molecular evolutionary processes that underlie the emergence of enzymes de novo, which are notably mirrored by recent examples of computational enzyme design and directed evolution.

Recent advances in enzymatic oxidation of alcohols

Enzymatic alcohol oxidation plays an important role in chemical synthesis. In the past two years, new alcohol oxidation enzymes were developed through genome-mining and protein engineering, such as new copper radical oxidases with broad substrate scope, alcohol dehydrogenases with altered cofactor preference and a flavin-dependent alcohol oxidase with enhanced oxygen coupling. New cofactor recycling methods were reported for alcohol dehydrogenase-catalyzed oxidation with photocatalyst and coupled glutaredoxin-glutathione reductase as promising examples. Different alcohol oxidation systems were used for the oxidation of primary and secondary alcohols, especially in the cascade conversion of alcohols to lactones, lactams, chiral amines, chiral alcohols and hydroxyketones. Among them, biocatalyst with low enantioselectivity demonstrated an interesting feature for complete conversion of racemic secondary alcohols through non-enantioselective oxidation.

Enzyme function and its evolution

With rapid increases over recent years in the determination of protein sequence and structure, alongside knowledge of thousands of enzyme functions and hundreds of chemical mechanisms, it is now possible to combine breadth and depth in our understanding of enzyme evolution. Phylogenetics continues to move forward, though determining correct evolutionary family trees is not trivial. Protein function prediction has spawned a variety of promising methods that offer the prospect of identifying enzymes across the whole range of chemical functions and over numerous species. This knowledge is essential to understand antibiotic resistance, as well as in protein re-engineering and de novo enzyme design.

Differential catalytic promiscuity of the alkaline phosphatase superfamily bimetallo core reveals mechanistic features underlying enzyme evolution

Members of enzyme superfamilies specialize in different reactions but often exhibit catalytic promiscuity for one another's reactions, consistent with catalytic promiscuity as an important driver in the evolution of new enzymes. Wanting to understand how catalytic promiscuity and other factors may influence evolution across a superfamily, we turned to the well-studied alkaline phosphatase (AP) superfamily, comparing three of its members, two evolutionarily distinct phosphatases and a phosphodiesterase. We mutated distinguishing active-site residues to generate enzymes that had a common Zn²⁺ bimetallo core but little sequence similarity and different auxiliary domains. We then tested the catalytic capabilities of these pruned enzymes with a series of substrates. A substantial rate enhancement of ∼10¹¹-fold for both phosphate mono- and diester hydrolysis by each enzyme indicated that the Zn²⁺ bimetallo core is an effective mono/di-esterase generalist and that the bimetallo cores were not evolutionarily tuned to prefer their cognate reactions. In contrast, our pruned enzymes were ineffective sulfatases, and this limited promiscuity may have provided a driving force for founding the distinct one-metal-ion branch that contains all known AP superfamily sulfatases. Finally, our pruned enzymes exhibited 10⁷-10⁸-fold phosphotriesterase rate enhancements, despite absence of such enzymes within the AP superfamily. We speculate that the superfamily active-site architecture involved in nucleophile positioning prevents accommodation of the additional triester substituent. Overall, we suggest that catalytic promiscuity, and the ease or difficulty of remodeling and building onto existing protein scaffolds, have greatly influenced the course of enzyme evolution. Uncovering principles and properties of enzyme function, promiscuity, and repurposing provides lessons for engineering new enzymes.

Understanding enzyme function evolution from a computational perspective

In this review, we will explore recent computational approaches to understand enzyme evolution from the perspective of protein structure, dynamics and promiscuity. We will present quantitative methods to measure the size of evolutionary steps within a structural domain, allowing the correlation between change in substrate and domain structure to be assessed, and giving insights into the evolvability of different domains in terms of the number, types and sizes of evolutionary steps observed. These approaches will help to understand the evolution of new catalytic and non-catalytic functionality in response to environmental demands, showing potential to guide de novoenzyme design and directed evolution experiments.

Genomic Enzymology: Web Tools for Leveraging Protein Family Sequence-Function Space and Genome Context to Discover Novel Functions

The exponentially increasing number of protein and nucleic acid sequences provides opportunities to discover novel enzymes, metabolic pathways, and metabolites/natural products, thereby adding to our knowledge of biochemistry and biology. The challenge has evolved from generating sequence information to mining the databases to integrating and leveraging the available information, i.e., the availability of “genomic enzymology” web tools. Web tools that allow identification of biosynthetic gene clusters are widely used by the natural products/synthetic biology community, thereby facilitating the discovery of novel natural products and the enzymes responsible for their biosynthesis. However, many novel enzymes with interesting mechanisms participate in uncharacterized small-molecule metabolic pathways; their discovery and functional characterization also can be accomplished by leveraging information in protein and nucleic acid databases. This Perspective focuses on two genomic enzymology web tools that assist the discovery novel metabolic pathways: (1) Enzyme Function Initiative-Enzyme Similarity Tool (EFI-EST) for generating sequence similarity networks to visualize and analyze sequence–function space in protein families and (2) Enzyme Function Initiative-Genome Neighborhood Tool (EFI-GNT) for generating genome neighborhood networks to visualize and analyze the genome context in microbial and fungal genomes. Both tools have been adapted to other applications to facilitate target selection for enzyme discovery and functional characterization. As the natural products community has demonstrated, the enzymology community needs to embrace the essential role of web tools that allow the protein and genome sequence databases to be leveraged for novel insights into enzymological problems.

De novo active sites for resurrected Precambrian enzymes

Protein engineering studies often suggest the emergence of completely new enzyme functionalities to be highly improbable. However, enzymes likely catalysed many different reactions already in the last universal common ancestor. Mechanisms for the emergence of completely new active sites must therefore either plausibly exist or at least have existed at the primordial protein stage. Here, we use resurrected Precambrian proteins as scaffolds for protein engineering and demonstrate that a new active site can be generated through a single hydrophobic-to-ionizable amino acid replacement that generates a partially buried group with perturbed physico-chemical properties. We provide experimental and computational evidence that conformational flexibility can assist the emergence and subsequent evolution of new active sites by improving substrate and transition-state binding, through the sampling of many potentially productive conformations. Our results suggest a mechanism for the emergence of primordial enzymes and highlight the potential of ancestral reconstruction as a tool for protein engineering.

Parallel evolution of non-homologous isofunctional enzymes in methionine biosynthesis

Experimental validation of enzyme function is crucial for genome interpretation, but it remains challenging because it cannot be scaled up to accommodate the constant accumulation of genome sequences. We tackled this issue for the MetA and MetX enzyme families, phylogenetically unrelated families of acyl-L-homoserine transferases involved in L-methionine biosynthesis. Members of these families are prone to incorrect annotation because MetX and MetA enzymes are assumed to always use acetyl-CoA and succinyl-CoA, respectively. We determined the enzymatic activities of 100 enzymes from diverse species, and interpreted the results by structural classification of active sites based on protein structure modeling. We predict that >60% of the 10,000 sequences from these families currently present in databases are incorrectly annotated, and suggest that acetyl-CoA was originally the sole substrate of these isofunctional enzymes, which evolved to use exclusively succinyl-CoA in the most recent bacteria. We also uncovered a divergent subgroup of MetX enzymes in fungi that participate only in L-cysteine biosynthesis as O-succinyl-L-serine transferases.

Resolving protein structure-function-binding site relationships from a binding site similarity network perspective

Functional annotation is seldom straightforward with complexities arising due to functional divergence in protein families or functional convergence between non-homologous protein families, leading to mis-annotations. An enzyme may contain multiple domains and not all domains may be involved in a given function, adding to the complexity in function annotation. To address this, we use binding site information from bound cognate ligands and catalytic residues, since it can help in resolving fold-function relationships at a finer level and with higher confidence. A comprehensive database of 2,020 fold-function-binding site relationships has been systematically generated. A network-based approach is employed to capture the complexity in these relationships, from which different types of associations are deciphered, that identify versatile protein folds performing diverse functions, same function associated with multiple folds and one-to-one relationships. Binding site similarity networks integrated with fold, function, and ligand similarity information are generated to understand the depth of these relationships. Apart from the observed continuity in the functional site space, network properties of these revealed versatile families with topologically different or dissimilar binding sites and structural families that perform very similar functions. As a case study, subtle changes in the active site of a set of evolutionarily related superfamilies are studied using these networks. Tracing of such similarities in evolutionarily related proteins provide clues into the transition and evolution of protein functions. Insights from this study will be helpful in accurate and reliable functional annotations of uncharacterized proteins, poly-pharmacology, and designing enzymes with new functional capabilities. Proteins 2017; 85:1319-1335. © 2017 Wiley Periodicals, Inc.

SITEX 2.0: Projections of protein functional sites on eukaryotic genes. Extension with orthologous genes

Functional sites define the diversity of protein functions and are the central object of research of the structural and functional organization of proteins. The mechanisms underlying protein functional sites emergence and their variability during evolution are distinguished by duplication, shuffling, insertion and deletion of the exons in genes. The study of the correlation between a site structure and exon structure serves as the basis for the in-depth understanding of sites organization. In this regard, the development of programming resources that allow the realization of the mutual projection of exon structure of genes and primary and tertiary structures of encoded proteins is still the actual problem. Previously, we developed the SitEx system that provides information about protein and gene sequences with mapped exon borders and protein functional sites amino acid positions. The database included information on proteins with known 3D structure. However, data with respect to orthologs was not available. Therefore, we added the projection of sites positions to the exon structures of orthologs in SitEx 2.0. We implemented a search through database using site conservation variability and site discontinuity through exon structure. Inclusion of the information on orthologs allowed to expand the possibilities of SitEx usage for solving problems regarding the analysis of the structural and functional organization of proteins. Database URL: http://www-bionet.sscc.ru/sitex/ .

Evolutionary studies of ligand binding sites in proteins

Biological processes at their most fundamental molecular aspects are defined by molecular interactions with ligand-protein interactions in particular at the core of cellular functions such as metabolism and signalling. Divergent and convergent processes shape the evolution of ligand binding sites. The competition between similar ligands and binding sites across protein families create evolutionary pressures that affect the specificity and selectivity of interactions. This short review showcases recent studies of the evolution of ligand binding-sites and methods used to detect binding-site similarities.

CATH: an expanded resource to predict protein function through structure and sequence

The latest version of the CATH-Gene3D protein structure classification database has recently been released (version 4.1, http://www.cathdb.info). The resource comprises over 300 000 domain structures and over 53 million protein domains classified into 2737 homologous superfamilies, doubling the number of predicted protein domains in the previous version. The daily-updated CATH-B, which contains our very latest domain assignment data, provides putative classifications for over 100 000 additional protein domains. This article describes developments to the CATH-Gene3D resource over the last two years since the publication in 2015, including: significant increases to our structural and sequence coverage; expansion of the functional families in CATH; building a support vector machine (SVM) to automatically assign domains to superfamilies; improved search facilities to return alignments of query sequences against multiple sequence alignments; the redesign of the web pages and download site.

KinView: a visual comparative sequence analysis tool for integrated kinome research

Multiple sequence alignments (MSAs) are a fundamental analysis tool used throughout biology to investigate relationships between protein sequence, structure, function, evolutionary history, and patterns of disease-associated variants. However, their widespread application in systems biology research is currently hindered by the lack of user-friendly tools to simultaneously visualize, manipulate and query the information conceptualized in large sequence alignments, and the challenges in integrating MSAs with multiple orthogonal data such as cancer variants and post-translational modifications, which are often stored in heterogeneous data sources and formats. Here, we present the Multiple Sequence Alignment Ontology (MSAOnt), which represents a profile or consensus alignment in an ontological format. Subsets of the alignment are easily selected through the SPARQL Protocol and RDF Query Language for downstream statistical analysis or visualization. We have also created the Kinome Viewer (KinView), an interactive integrative visualization that places eukaryotic protein kinase cancer variants in the context of natural sequence variation and experimentally determined post-translational modifications, which play central roles in the regulation of cellular signaling pathways. Using KinView, we identified differential phosphorylation patterns between tyrosine and serine/threonine kinases in the activation segment, a major kinase regulatory region that is often mutated in proliferative diseases. We discuss cancer variants that disrupt phosphorylation sites in the activation segment, and show how KinView can be used as a comparative tool to identify differences and similarities in natural variation, cancer variants and post-translational modifications between kinase groups, families and subfamilies. Based on KinView comparisons, we identify and experimentally characterize a regulatory tyrosine (Y177PLK4) in the PLK4 C-terminal activation segment region termed the P+1 loop. To further demonstrate the application of KinView in hypothesis generation and testing, we formulate and validate a hypothesis explaining a novel predicted loss-of-function variant (D523NPKCβ) in the regulatory spine of PKCβ, a recently identified tumor suppressor kinase. KinView provides a novel, extensible interface for performing comparative analyses between subsets of kinases and for integrating multiple types of residue specific annotations in user friendly formats.

Functional Annotations of Paralogs: A Blessing and a Curse

Gene duplication followed by mutation is a classic mechanism of neofunctionalization, producing gene families with functional diversity. In some cases, a single point mutation is sufficient to change the substrate specificity and/or the chemistry performed by an enzyme, making it difficult to accurately separate enzymes with identical functions from homologs with different functions. Because sequence similarity is often used as a basis for assigning functional annotations to genes, non-isofunctional gene families pose a great challenge for genome annotation pipelines. Here we describe how integrating evolutionary and functional information such as genome context, phylogeny, metabolic reconstruction and signature motifs may be required to correctly annotate multifunctional families. These integrative analyses can also lead to the discovery of novel gene functions, as hints from specific subgroups can guide the functional characterization of other members of the family. We demonstrate how careful manual curation processes using comparative genomics can disambiguate subgroups within large multifunctional families and discover their functions. We present the COG0720 protein family as a case study. We also discuss strategies to automate this process to improve the accuracy of genome functional annotation pipelines.

Diversity in protein domain superfamilies

Whilst ∼93% of domain superfamilies appear to be relatively structurally and functionally conserved based on the available data from the CATH-Gene3D domain classification resource, the remainder are much more diverse. In this review, we consider how domains in some of the most ubiquitous and promiscuous superfamilies have evolved, in particular the plasticity in their functional sites and surfaces which expands the repertoire of molecules they interact with and actions performed on them. To what extent can we identify a core function for these superfamilies which would allow us to develop a ‘domain grammar of function’ whereby a protein's biological role can be proposed from its constituent domains? Clearly the first step is to understand the extent to which these components vary and how changes in their molecular make-up modifies function.

The history of the CATH structural classification of protein domains

This article presents a historical review of the protein structure classification database CATH. Together with the SCOP database, CATH remains comprehensive and reasonably up-to-date with the now more than 100,000 protein structures in the PDB. We review the expansion of the CATH and SCOP resources to capture predicted domain structures in the genome sequence data and to provide information on the likely functions of proteins mediated by their constituent domains. The establishment of comprehensive function annotation resources has also meant that domain families can be functionally annotated allowing insights into functional divergence and evolution within protein families.

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We present a historical review of the protein structure database CATH.

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We review the expansion of the CATH and SCOP resources with sequence data and functional annotations.

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How functional annotation resources allow insights into functional divergence and evolution within protein families.