New insights about enzyme evolution from large scale studies of sequence and structure relationships

Enhanced thermostability of nattokinase by rational design of disulfide bond

Nattokinase, the thrombolytically active substance in the health food natto, nevertheless, its lower thermostability restricts its use in food and pharmaceutical applications. In this study, two heat-resistant variants of nattokinase, designated 50-109 (M1) and 15-271 (M2), were successfully obtained by introducing a disulfide bonding strategy. Their half-lives at 55℃ were found to be 2.50-fold and 5.17-fold higher, respectively, than that of the wild type. Furthermore, the specific enzyme activities of the variants, M1 and M2, were also increased by 2.37 and 1.66-fold, respectively. Meanwhile, the combination of two mutants increased the thermostability of nattokinase by 8.0-fold. Bioinformatics analyses indicated that the enhanced thermostability of the M1 and M2 variants was due to the increased rigidity and structural contraction of the overall structure. Finally, the fermentation process of mutant M1 was optimized to increase the expression of nattokinase. Study provides substantial molecular and theoretical support for the industrial production and application of nattokinase.

An objective criterion to evaluate sequence-similarity networks helps in dividing the protein family sequence space

The deluge of genomic data raises various challenges for computational protein annotation. The definition of superfamilies, based on conserved folds, or of families, showing more recent homology signatures, allow a first categorization of the sequence space. However, for precise functional annotation or the identification of the unexplored parts within a family, a division into subfamilies is essential. As curators of an expert database, the Carbohydrate Active Enzymes database (CAZy), we began, more than 15 years ago, to manually define subfamilies based on phylogeny reconstruction. However, facing the increasing amount of sequence and functional data, we required more scalable and reproducible methods. The recently popularized sequence similarity networks (SSNs), allows to cope with very large families and computation of many subfamily schemes. Still, the choice of the optimal SSN subfamily scheme only relies on expert knowledge so far, without any data-driven guidance from within the network. In this study, we therefore decided to investigate several network properties to determine a criterion which can be used by curators to evaluate the quality of subfamily assignments. The performance of the closeness centrality criterion, a network property to indicate the connectedness within the network, shows high similarity to the decisions of expert curators from eight distinct protein families. Closeness centrality also suggests that in some cases multiple levels of subfamilies could be possible, depending on the granularity of the research question, while it indicates when no subfamily emerged in some family evolution. We finally used closeness centrality to create subfamilies in four families of the CAZy database, providing a finer functional annotation and highlighting subfamilies without biochemically characterized members for potential future discoveries.

Metallo-Beta-Lactamase-like Encoding Genes in Candidate Phyla Radiation: Widespread and Highly Divergent Proteins with Potential Multifunctionality

The Candidate Phyla Radiation (CPR) was found to harbor a vast repertoire of genes encoding for enzymes with potential antibiotic resistance activity. Among these, as many as 3349 genes were predicted in silico to contain a metallo-beta-lactamase-like (MBL-like) fold. These proteins were subject to an in silico functional characterization by comparing their protein profiles (presence/absence of conserved protein domains) to other MBLs, including 24 already expressed in vitro, along with those of the beta-lactamase database (BLDB) (n = 761). The sequence similarity network (SSN) was then used to predict the functional clusters of CPR MBL-like sequences. Our findings showed that CPR MBL-like sequences were longer and more diverse than bacterial MBL sequences, with a high content of functional domains. Most CPR MBL-like sequences did not show any SSN connectivity with expressed MBLs, indicating the presence of many potential, yet unidentified, functions in CPR. In conclusion, CPR was shown to have many protein functions and a large sequence variability of MBL-like folds, exceeding all known MBLs. Further experimental and evolutionary studies of this superfamily of hydrolyzing enzymes are necessary to illustrate their functional annotation, origin, and expansion for adaptation or specialization within a given niche or compared to a specific substrate.

TMEM164 is an acyltransferase that forms ferroptotic C20:4 ether phospholipids

Ferroptosis is an iron-dependent form of cell death driven by oxidation of polyunsaturated fatty acid (PUFA) phospholipids. Large-scale genetic screens have uncovered a specialized role for PUFA ether phospholipids (ePLs) in promoting ferroptosis. Understanding of the enzymes involved in PUFA-ePL production, however, remains incomplete. Here we show, using a combination of pathway mining of genetic dependency maps, AlphaFold-guided structure predictions and targeted lipidomics, that the uncharacterized transmembrane protein TMEM164-the genetic ablation of which has been shown to protect cells from ferroptosis-is a cysteine active center enzyme that selectively transfers C20:4 acyl chains from phosphatidylcholine to lyso-ePLs to produce PUFA ePLs. Genetic deletion of TMEM164 across a set of ferroptosis-sensitive cancer cell lines caused selective reductions in C20:4 ePLs with minimal effects on C20:4 diacyl PLs, and this lipid profile produced a variable range of protection from ferroptosis, supportive of an important but contextualized role for C20:4 ePLs in this form of cell death.

Rational Redesign of Enzyme via the Combination of Quantum Mechanics/Molecular Mechanics, Molecular Dynamics, and Structural Biology Study

Increasing demands for efficient and versatile chemical reactions have prompted innovations in enzyme engineering. A major challenge in engineering α-ketoglutarate-dependent oxygenases is to develop a rational strategy which can be widely used for directly evolving the desired mutant to generate new products. Herein, we report a strategy for rational redesign of a model enzyme, 4-hydroxyphenylpyruvate dioxygenase (HPPD), based on quantum mechanics/molecular mechanics (QM/MM) calculation and molecular dynamic simulations. This strategy enriched our understanding of the HPPD catalytic reaction pathway and led to the discovery of a series of HPPD mutants producing hydroxyphenylacetate (HPA) as the alternative product other than the native product homogentisate. The predicted HPPD-Fe(IV)═O-HPA intermediate was further confirmed by the crystal structure of Arabidopsis thaliana HPPD/S267W complexed with HPA. These findings not only provide a good understanding of the structure-function relationship of HPPD but also demonstrate a generally applicable platform for the development of biocatalysts.

Exploring Protein Space: From Hydrolase to Ligase by Substitution

The understanding of how proteins evolve to perform novel functions has long been sought by biologists. In this regard, two homologous bacterial enzymes, PafA and Dop, pose an insightful case study, as both rely on similar mechanistic properties, yet catalyze different reactions. PafA conjugates a small protein tag to target proteins, whereas Dop removes the tag by hydrolysis. Given that both enzymes present a similar fold and high sequence similarity, we sought to identify the differences in the amino acid sequence and folding responsible for each distinct activity. We tackled this question using analysis of sequence–function relationships, and identified a set of uniquely conserved residues in each enzyme. Reciprocal mutagenesis of the hydrolase, Dop, completely abolished the native activity, at the same time yielding a catalytically active ligase. Based on the available Dop and PafA crystal structures, this change of activity required a conformational change of a critical loop at the vicinity of the active site. We identified the conserved positions essential for stabilization of the alternative loop conformation, and tracked alternative mutational pathways that lead to a change in activity. Remarkably, all these pathways were combined in the evolution of PafA and Dop, despite their redundant effect on activity. Overall, we identified the residues and structural elements in PafA and Dop responsible for their activity differences. This analysis delineated, in molecular terms, the changes required for the emergence of a new catalytic function from a preexisting one.

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.

Characterizing lineage-specific evolution and the processes driving genomic diversification in chordates

Background

Understanding the origins of genome content has long been a goal of molecular evolution and comparative genomics. By examining genome evolution through the guise of lineage-specific evolution, it is possible to make inferences about the evolutionary events that have given rise to species-specific diversification. Here we characterize the evolutionary trends found in chordate species using The Adaptive Evolution Database (TAED). TAED is a database of phylogenetically indexed gene families designed to detect episodes of directional or diversifying selection across chordates. Gene families within the database have been assessed for lineage-specific estimates of dN/dS and have been reconciled to the chordate species to identify retained duplicates. Gene families have also been mapped to the functional pathways and amino acid changes which occurred on high dN/dS lineages have been mapped to protein structures.

Results

An analysis of this exhaustive database has enabled a characterization of the processes of lineage-specific diversification in chordates. A pathway level enrichment analysis of TAED determined that pathways most commonly found to have elevated rates of evolution included those involved in metabolism, immunity, and cell signaling. An analysis of protein fold presence on proteins, after normalizing for frequency in the database, found common folds such as Rossmann folds, Jelly Roll folds, and TIM barrels were overrepresented on proteins most likely to undergo directional selection. A set of gene families which experience increased numbers of duplications within short evolutionary times are associated with pathways involved in metabolism, olfactory reception, and signaling. An analysis of protein secondary structure indicated more relaxed constraint in β-sheets and stronger constraint on alpha Helices, amidst a general preference for substitutions at exposed sites. Lastly a detailed analysis of the ornithine decarboxylase gene family, a key enzyme in the pathway for polyamine synthesis, revealed lineage-specific evolution along the lineage leading to Cetacea through rapid sequence evolution in a duplicate gene with amino acid substitutions causing active site rearrangement.

Conclusion

Episodes of lineage-specific evolution are frequent throughout chordate species. Both duplication and directional selection have played large roles in the evolution of the phylum. TAED is a powerful tool for facilitating this understanding of lineage-specific evolution.

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).

Evolutionary dynamics of natural product biosynthesis in bacteria

Covering: 2008 up to 2019The forces of biochemical adaptive evolution operate at the level of genes, manifesting in complex phenotypes and the global biodiversity of proteins and metabolites. While evolutionary histories have been deciphered for some other complex traits, the origins of natural product biosynthesis largely remain a mystery. This fundamental knowledge gap is surprising given the many decades of research probing the genetic, chemical, and biophysical mechanisms of bacterial natural product biosynthesis. Recently, evolutionary thinking has begun to permeate this otherwise mechanistically dominated field. Natural products are now sometimes referred to as 'specialized' rather than 'secondary' metabolites, reinforcing the importance of their biological and ecological functions. Here, we review known evolutionary mechanisms underlying the overwhelming chemical diversity of bacterial secondary metabolism, focusing on enzyme promiscuity and the evolution of enzymatic domains that enable metabolic traits. We discuss the mechanisms that drive the assembly of natural product biosynthetic gene clusters and propose formal definitions for 'specialized' and 'secondary' metabolism. We further explore how biosynthetic gene clusters evolve to synthesize related molecular species, and in turn how the biological and ecological roles that emerge from metabolic diversity are acted on by selection. Finally, we reconcile chemical, functional, and genetic data into an evolutionary model, the dynamic chemical matrix evolutionary hypothesis, in which the relationships between chemical distance, biomolecular activity, and relative fitness shape adaptive landscapes.

Analysis of Electrochemical Properties of S-Adenosyl-l-methionine and Implications for Its Role in Radical SAM Enzymes

S-Adenosyl-l-methionine (SAM) is the central cofactor in the radical SAM enzyme superfamily, responsible for a vast number of transformations in primary and secondary metabolism. In nearly all of these reactions, the reductive cleavage of SAM is proposed to produce a reactive species, 5′-deoxyadenosyl radical, which initiates catalysis. While the mechanistic details in many cases are well-understood, the reductive cleavage of SAM remains elusive. In this manuscript, we have measured the solution peak potential of SAM to be ∼−1.4 V (v SHE) and show that under controlled potential conditions, it undergoes irreversible fragmentation to the 5′-deoxyadenosyl radical. While the radical intermediate is not directly observed, its presence as an initial intermediate is inferred by the formation of 8,5′-cycloadenosine and by H atom incorporation into 5′-deoxyadenosine from solvent exchangeable site. Similarly, 2-aminobutyrate is also observed under electrolysis conditions. The implications of these results in the context of the reductive cleavage of SAM by radical SAM enzymes are discussed.

Nature of Long-Range Evolutionary Constraint in Enzymes: Insights from Comparison to Pseudoenzymes with Similar Structures

Enzymes are known to fine-tune their sequences to optimize catalytic function, yet quantitative evolutionary design principles of enzymes remain elusive on the proteomic scale. Recently, it was found that the catalytic site in enzymes induces long-range evolutionary constraint, where even sites distant to the catalytic site are more conserved than expected. Given that protein-fold usage is generally different between enzymes and nonenzymes, it remains an open question to what extent this long-range evolutionary constraint in enzymes is dictated, either directly or indirectly, by the special three-dimensional structure of the enzyme. To investigate this question, we have compared evolutionary properties of enzymes with those of counterpart pseudoenzymes that share the same protein fold but are catalytically inactive. We found that the long-range evolutionary constraint observed in enzymes is significantly reduced in pseudoenzyme counterparts, despite very high structural similarity (∼1.5 Å RMSD on average). Furthermore, this significant reduction in long-range evolutionary constraint is observed even in pseudoenzyme counterparts which retain the ligand-binding ability of enzymes. Finally, the distance between the site that induces the highest gradient of sequence conservation and the pseudocatalytic site in pseudoenzymes is significantly larger than the corresponding distance in enzymes. Taken together, our results suggest that the long-range evolutionary constraint in enzymes is induced mainly by the presence of the catalytic site rather than by the special three-dimensional structure of the enzyme, and that such long-range evolutionary constraint in enzymes depends mainly on the catalytic function of the active site rather than on the ligand-binding ability of the enzyme.

The GMC superfamily of oxidoreductases revisited: analysis and evolution of fungal GMC oxidoreductases

BACKGROUND

The glucose-methanol-choline (GMC) superfamily is a large and functionally diverse family of oxidoreductases that share a common structural fold. Fungal members of this superfamily that are characterised and relevant for lignocellulose degradation include aryl-alcohol oxidoreductase, alcohol oxidase, cellobiose dehydrogenase, glucose oxidase, glucose dehydrogenase, pyranose dehydrogenase, and pyranose oxidase, which together form family AA3 of the auxiliary activities in the CAZy database of carbohydrate-active enzymes. Overall, little is known about the extant sequence space of these GMC oxidoreductases and their phylogenetic relations. Although some individual forms are well characterised, it is still unclear how they compare in respect of the complete enzyme class and, therefore, also how generalizable are their characteristics.

RESULTS

To improve the understanding of the GMC superfamily as a whole, we used sequence similarity networks to cluster large numbers of fungal GMC sequences and annotate them according to functionality. Subsequently, different members of the GMC superfamily were analysed in detail with regard to their sequences and phylogeny. This allowed us to define the currently characterised sequence space and show that complete clades of some enzymes have not been studied in any detail to date. Finally, we interpret our results from an evolutionary perspective, where we could show, for example, that pyranose dehydrogenase evolved from aryl-alcohol oxidoreductase after a change in substrate specificity and that the cytochrome domain of cellobiose dehydrogenase was regularly lost during evolution.

CONCLUSIONS

This study offers new insights into the sequence variation and phylogenetic relationships of fungal GMC/AA3 sequences. Certain clades of these GMC enzymes identified in our phylogenetic analyses are completely uncharacterised to date, and might include enzyme activities of varying specificities and/or activities that are hitherto unstudied.

Exploring the sequence, function, and evolutionary space of protein superfamilies using sequence similarity networks and phylogenetic reconstructions

Integrative computational methods can facilitate the discovery of new protein functions and enzymatic reactions by enabling the observation and investigation of complex sequence-structure-function and evolutionary relationships within protein superfamilies. Here, we highlight the use of sequence similarity networks (SSNs) and phylogenetic reconstructions to map the functional divergence and evolutionary history of protein superfamilies. We exemplify this approach using the nitroreductase (NTR) flavoenzyme superfamily, demonstrating that SSN investigations can provide a rapid and effective means to classify groups of proteins, expose sequence similarity relationships across the global scale of a protein superfamily, and efficiently support detailed phylogenetic analyses. Integration of such approaches with systematic experimental characterization will expand our understanding of the functional diversity of enzymes, their evolution, and their associated physiological roles.

Harnessing glycoenzyme engineering for synthesis of bioactive oligosaccharides

Combined with chemical synthesis, the use of glycoenzyme biocatalysts has shown great synthetic potential over recent decades owing to their remarkable versatility in terms of substrates and regio- and stereoselectivity that allow structurally controlled synthesis of carbohydrates and glycoconjugates. Nonetheless, the lack of appropriate enzymatic tools with requisite properties in the natural diversity has hampered extensive exploration of enzyme-based synthetic routes to access relevant bioactive oligosaccharides, such as cell-surface glycans or prebiotics. With the remarkable progress in enzyme engineering, it has become possible to improve catalytic efficiency and physico-chemical properties of enzymes but also considerably extend the repertoire of accessible catalytic reactions and tailor novel substrate specificities. In this review, we intend to give a brief overview of the advantageous use of engineered glycoenzymes, sometimes in combination with chemical steps, for the synthesis of natural bioactive oligosaccharides or their precursors. The focus will be on examples resulting from the three main classes of glycoenzymes specialized in carbohydrate synthesis: glycosyltransferases, glycoside hydrolases and glycoside phosphorylases.

Effusion: prediction of protein function from sequence similarity networks

Motivation

Critical evaluation of methods for protein function prediction shows that data integration improves the performance of methods that predict protein function, but a basic BLAST-based method is still a top contender. We sought to engineer a method that modernizes the classical approach while avoiding pitfalls common to state-of-the-art methods.

Results

We present a method for predicting protein function, Effusion, which uses a sequence similarity network to add context for homology transfer, a probabilistic model to account for the uncertainty in labels and function propagation, and the structure of the Gene Ontology (GO) to best utilize sparse input labels and make consistent output predictions. Effusion's model makes it practical to integrate rare experimental data and abundant primary sequence and sequence similarity. We demonstrate Effusion's performance using a critical evaluation method and provide an in-depth analysis. We also dissect the design decisions we used to address challenges for predicting protein function. Finally, we propose directions in which the framework of the method can be modified for additional predictive power.

Availability and implementation

The source code for an implementation of Effusion is freely available at https://github.com/babbittlab/effusion.

Supplementary information

Supplementary data are available at Bioinformatics online.

CATH: expanding the horizons of structure-based functional annotations for genome sequences

This article provides an update of the latest data and developments within the CATH protein structure classification database (http://www.cathdb.info). The resource provides two levels of release: CATH-B, a daily snapshot of the latest structural domain boundaries and superfamily assignments, and CATH+, which adds layers of derived data, such as predicted sequence domains, functional annotations and functional clustering (known as Functional Families or FunFams). The most recent CATH+ release (version 4.2) provides a huge update in the coverage of structural data. This release increases the number of fully- classified domains by over 40% (from 308 999 to 434 857 structural domains), corresponding to an almost two- fold increase in sequence data (from 53 million to over 95 million predicted domains) organised into 6119 superfamilies. The coverage of high-resolution, protein PDB chains that contain at least one assigned CATH domain is now 90.2% (increased from 82.3% in the previous release). A number of highly requested features have also been implemented in our web pages: allowing the user to view an alignment between their query sequence and a representative FunFam structure and providing tools that make it easier to view the full structural context (multi-domain architecture) of domains and chains.

Revealing Unexplored Sequence-Function Space Using Sequence Similarity Networks

The rapidly expanding number of protein sequences found in public databases can improve our understanding of how protein functions evolve. However, our current knowledge of protein function likely represents a small fraction of the diverse repertoire that exists in nature. Integrative computational methods can facilitate the discovery of new protein functions and enzymatic reactions through the observation and investigation of the complex sequence-structure-function relationships within protein superfamilies. Here, we highlight the use of sequence similarity networks (SSNs) to identify previously unexplored sequence and function space. We exemplify this approach using the nitroreductase (NTR) superfamily. We demonstrate that SSN investigations can provide a rapid and effective means to classify groups of proteins, therefore exposing experimentally unexplored sequences that may exhibit novel functionality. Integration of such approaches with systematic experimental characterization will expand our understanding of the functional diversity of enzymes and their associated physiological roles.

Atlas of the Radical SAM Superfamily: Divergent Evolution of Function Using a "Plug and Play" Domain

The radical SAM superfamily contains over 100,000 homologous enzymes that catalyze a remarkably broad range of reactions required for life, including metabolism, nucleic acid modification, and biogenesis of cofactors. While the highly conserved SAM-binding motif responsible for formation of the key 5'-deoxyadenosyl radical intermediate is a key structural feature that simplifies identification of superfamily members, our understanding of their structure-function relationships is complicated by the modular nature of their structures, which exhibit varied and complex domain architectures. To gain new insight about these relationships, we classified the entire set of sequences into similarity-based subgroups that could be visualized using sequence similarity networks. This superfamily-wide analysis reveals important features that had not previously been appreciated from studies focused on one or a few members. Functional information mapped to the networks indicates which members have been experimentally or structurally characterized, their known reaction types, and their phylogenetic distribution. Despite the biological importance of radical SAM chemistry, the vast majority of superfamily members have never been experimentally characterized in any way, suggesting that many new reactions remain to be discovered. In addition to 20 subgroups with at least one known function, we identified additional subgroups made up entirely of sequences of unknown function. Importantly, our results indicate that even general reaction types fail to track well with our sequence similarity-based subgroupings, raising major challenges for function prediction for currently identified and new members that continue to be discovered. Interactive similarity networks and other data from this analysis are available from the Structure-Function Linkage Database.

Theoretical Studies on Catalysis Mechanisms of Serum Paraoxonase 1 and Phosphotriesterase Diisopropyl Fluorophosphatase Suggest the Alteration of Substrate Preference from Paraoxonase to DFP

The calcium-dependent β-propeller proteins mammalian serum paraoxonase 1 (PON1) and phosphotriesterase diisopropyl fluorophosphatase (DFPase) catalyze the hydrolysis of organophosphorus compounds and enhance hydrolysis of various nerve agents. In the present work, the phosphotriesterase activity development between PON1 and DFPase was investigated by using the hybrid density functional theory method B3LYP. Based on the active-site difference between PON1 and DFPase, both the wild type and the mutant (a water molecule replacing Asn270 in PON1) models were designed. The results indicated that the substitution of a water molecule for Asn270 in PON1 had little effect on the enzyme activity in kinetics, while being more efficient in thermodynamics, which is essential for DFP hydrolysis. Structure comparisons of evolutionarily related enzymes show that the mutation of Asn270 leads to the catalytic Ca²⁺ ion indirectly connecting the buried structural Ca²⁺ ion via hydrogen bonds in DFPase. It can reduce the plasticity of enzymatic structure, and possibly change the substrate preference from paraoxon to DFP, which implies an evolutionary transition from mono- to dinuclear catalytic centers. Our studies shed light on the investigation of enzyme catalysis mechanism from an evolutionary perspective.

No wisdom in the crowd: genome annotation in the era of big data - current status and future prospects

Science and engineering rely on the accumulation and dissemination of knowledge to make discoveries and create new designs. Discovery-driven genome research rests on knowledge passed on via gene annotations. In response to the deluge of sequencing big data, standard annotation practice employs automated procedures that rely on majority rules. We argue this hinders progress through the generation and propagation of errors, leading investigators into blind alleys. More subtly, this inductive process discourages the discovery of novelty, which remains essential in biological research and reflects the nature of biology itself. Annotation systems, rather than being repositories of facts, should be tools that support multiple modes of inference. By combining deduction, induction and abduction, investigators can generate hypotheses when accurate knowledge is extracted from model databases. A key stance is to depart from 'the sequence tells the structure tells the function' fallacy, placing function first. We illustrate our approach with examples of critical or unexpected pathways, using MicroScope to demonstrate how tools can be implemented following the principles we advocate. We end with a challenge to the reader.

To what extent do structural changes in catalytic metal sites affect enzyme function?

About half of known enzymatic reactions involve metals. Enzymes belonging to the same superfamily often evolve to catalyze different reactions on the same structural scaffold. The work presented here investigates how functional differentiation, within superfamilies that contain metalloenzymes, relates to structural changes at the catalytic metal site. In general, when the catalytic metal site is unchanged across the enzymes of a superfamily, the functional differentiation within the superfamily tends to be low and the mechanism conserved. Conversely, all types of structural changes in the metal binding site are observed for superfamilies with high functional differentiation. Overall, the catalytic role of the metal ions appears to be one of the most conserved features of the enzyme mechanism within metalloenzyme superfamilies. In particular, when the catalytic role of the metal ion does not involve a redox reaction (i.e. there is no exchange of electrons with the substrate), this role is almost always maintained even when the site undergoes significant structural changes. In these enzymes, functional diversification is most often associated with modifications in the surrounding protein matrix, which has changed so much that the enzyme chemistry is significantly altered. On the other hand, in more than 50% of the examples where the metal has a redox role in catalysis, changes at the metal site modify its catalytic role. Further, we find that there are no examples in our dataset where metal sites with a redox role are lost during evolution.

SYNOPSIS

In this paper we investigate how functional diversity within superfamilies of metalloenzymes relates to structural changes at the catalytic metal site. Evolution tends to strictly conserve the metal site. When changes occur, they do not modify the catalytic role of non-redox metals whereas they affect the role of redox-active metals.

Confronting the catalytic dark matter encoded by sequenced genomes

The post-genomic era has provided researchers with a deluge of protein sequences. However, a significant fraction of the proteins encoded by sequenced genomes remains without an identified function. Here, we aim at determining how many enzymes of uncertain or unknown function are still present in the Saccharomyces cerevisiae and human proteomes. Using information available in the Swiss-Prot, BRENDA and KEGG databases in combination with a Hidden Markov Model-based method, we estimate that >600 yeast and 2000 human proteins (>30% of their proteins of unknown function) are enzymes whose precise function(s) remain(s) to be determined. This illustrates the impressive scale of the ‘unknown enzyme problem’. We extensively review classical biochemical as well as more recent systematic experimental and computational approaches that can be used to support enzyme function discovery research. Finally, we discuss the possible roles of the elusive catalysts in light of recent developments in the fields of enzymology and metabolism as well as the significance of the unknown enzyme problem in the context of metabolic modeling, metabolic engineering and rare disease research.

Biocuration in the structure-function linkage database: the anatomy of a superfamily

With ever-increasing amounts of sequence data available in both the primary literature and sequence repositories, there is a bottleneck in annotating molecular function to a sequence. This article describes the biocuration process and methods used in the structure-function linkage database (SFLD) to help address some of the challenges. We discuss how the hierarchy within the SFLD allows us to infer detailed functional properties for functionally diverse enzyme superfamilies in which all members are homologous, conserve an aspect of their chemical function and have associated conserved structural features that enable the chemistry. Also presented is the Enzyme Structure-Function Ontology (ESFO), which has been designed to capture the relationships between enzyme sequence, structure and function that underlie the SFLD and is used to guide the biocuration processes within the SFLD.

Database URL:http://sfld.rbvi.ucsf.edu/

Evolutionary and molecular foundations of multiple contemporary functions of the nitroreductase superfamily

Insight regarding how diverse enzymatic functions and reactions have evolved from ancestral scaffolds is fundamental to understanding chemical and evolutionary biology, and for the exploitation of enzymes for biotechnology. We undertook an extensive computational analysis using a unique and comprehensive combination of tools that include large-scale phylogenetic reconstruction to determine the sequence, structural, and functional relationships of the functionally diverse flavin mononucleotide-dependent nitroreductase (NTR) superfamily (>24,000 sequences from all domains of life, 54 structures, and >10 enzymatic functions). Our results suggest an evolutionary model in which contemporary subgroups of the superfamily have diverged in a radial manner from a minimal flavin-binding scaffold. We identified the structural design principle for this divergence: Insertions at key positions in the minimal scaffold that, combined with the fixation of key residues, have led to functional specialization. These results will aid future efforts to delineate the emergence of functional diversity in enzyme superfamilies, provide clues for functional inference for superfamily members of unknown function, and facilitate rational redesign of the NTR scaffold.

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.

Community detection in sequence similarity networks based on attribute clustering

Networks are powerful tools for the presentation and analysis of interactions in multi-component systems. A commonly studied mesoscopic feature of networks is their community structure, which arises from grouping together similar nodes into one community and dissimilar nodes into separate communities. Here, the community structure of protein sequence similarity networks is determined with a new method: Attribute Clustering Dependent Communities (ACDC). Sequence similarity has hitherto typically been quantified by the alignment score or its expectation value. However, pair alignments with the same score or expectation value cannot thus be differentiated. To overcome this deficiency, the method constructs, for pair alignments, an extended alignment metric, the link attribute vector, which includes the score and other alignment characteristics. Rescaling components of the attribute vectors qualitatively identifies a systematic variation of sequence similarity within protein superfamilies. The problem of community detection is then mapped to clustering the link attribute vectors, selection of an optimal subset of links and community structure refinement based on the partition density of the network. ACDC-predicted communities are found to be in good agreement with gold standard sequence databases for which the "ground truth" community structures (or families) are known. ACDC is therefore a community detection method for sequence similarity networks based entirely on pair similarity information. A serial implementation of ACDC is available from https://cmb.ornl.gov/resources/developments.

Evolution of biosynthetic diversity

Since the emergence of the last common ancestor from which all extant life evolved, the metabolite repertoire of cells has increased and diversified. Not only has the metabolite cosmos expanded, but the ways in which the same metabolites are made have diversified. Enzymes catalyzing the same reaction have evolved independently from different protein folds; the same protein fold can produce enzymes recognizing different substrates, and enzymes performing different chemistries. Genes encoding useful enzymes can be transferred between organisms and even between the major domains of life. Organisms that live in metabolite-rich environments sometimes lose the pathways that produce those same metabolites. Fusion of different protein domains results in enzymes with novel properties. This review will consider the major evolutionary mechanisms that generate biosynthetic diversity: gene duplication (and gene loss), horizontal and endosymbiotic gene transfer, and gene fusion. It will also discuss mechanisms that lead to convergence as well as divergence. To illustrate these mechanisms, one of the original metabolisms present in the last universal common ancestor will be employed: polyamine metabolism, which is essential for the growth and cell proliferation of archaea and eukaryotes, and many bacteria.

Evaluating Functional Annotations of Enzymes Using the Gene Ontology

The Gene Ontology (GO) (Ashburner et al., Nat Genet 25(1):25-29, 2000) is a powerful tool in the informatics arsenal of methods for evaluating annotations in a protein dataset. From identifying the nearest well annotated homologue of a protein of interest to predicting where misannotation has occurred to knowing how confident you can be in the annotations assigned to those proteins is critical. In this chapter we explore what makes an enzyme unique and how we can use GO to infer aspects of protein function based on sequence similarity. These can range from identification of misannotation or other errors in a predicted function to accurate function prediction for an enzyme of entirely unknown function. Although GO annotation applies to any gene products, we focus here a describing our approach for hierarchical classification of enzymes in the Structure-Function Linkage Database (SFLD) (Akiva et al., Nucleic Acids Res 42(Database issue):D521-530, 2014) as a guide for informed utilisation of annotation transfer based on GO terms.

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.

Evolution of a G protein-coupled receptor response by mutations in regulatory network interactions

All cellular functions depend on the concerted action of multiple proteins organized in complex networks. To understand how selection acts on protein networks, we used the yeast mating receptor Ste2, a pheromone-activated G protein-coupled receptor, as a model system. In Saccharomyces cerevisiae, Ste2 is a hub in a network of interactions controlling both signal transduction and signal suppression. Through laboratory evolution, we obtained 21 mutant receptors sensitive to the pheromone of a related yeast species and investigated the molecular mechanisms behind this newfound sensitivity. While some mutants show enhanced binding affinity to the foreign pheromone, others only display weakened interactions with the network's negative regulators. Importantly, the latter changes have a limited impact on overall pathway regulation, despite their considerable effect on sensitivity. Our results demonstrate that a new receptor–ligand pair can evolve through network-altering mutations independently of receptor–ligand binding, and suggest a potential role for such mutations in disease.

Co-evolution of a new receptor-ligand pair will affect the downstream signal transduction network. Here, the authors use experimental evolution of yeast mating receptor Ste2 to show the effect of enhanced binding affinity and weakened interactions with the network's negative regulators on protein evolution.

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.

Sequence-Structure-Function Classification of a Catalytically Diverse Oxidoreductase Superfamily in Mycobacteria

The deazaflavin cofactor F420 enhances the persistence of mycobacteria during hypoxia, oxidative stress, and antibiotic treatment. However, the identities and functions of the mycobacterial enzymes that utilize F420 under these conditions have yet to be resolved. In this work, we used sequence similarity networks to analyze the distribution of the largest F420-dependent protein family in mycobacteria. We show that these enzymes are part of a larger split β-barrel enzyme superfamily (flavin/deazaflavin oxidoreductases, FDORs) that include previously characterized pyridoxamine/pyridoxine-5'-phosphate oxidases and heme oxygenases. We show that these proteins variously utilize F420, flavin mononucleotide, flavin adenine dinucleotide, and heme cofactors. Functional annotation using phylogenetic, structural, and spectroscopic methods revealed their involvement in heme degradation, biliverdin reduction, fatty acid modification, and quinone reduction. Four novel crystal structures show that plasticity in substrate binding pockets and modifications to cofactor binding motifs enabled FDORs to carry out a variety of functions. This systematic classification and analysis provides a framework for further functional analysis of the roles of FDORs in mycobacterial pathogenesis and persistence.

Pseudoproteases: mechanisms and function

Catalytically inactive enzymes (also known as pseudoproteases, protease homologues or paralogues, non-peptidase homologues, non-enzymes and pseudoenzymes) have traditionally been hypothesized to act as regulators of their active homologues. However, those that have been characterized demonstrate that inactive enzymes have an extensive and expanding role in biological processes, including regulation, inhibition and immune modulation. With the emergence of each new genome, more inactive enzymes are being identified, and their abundance and potential as therapeutic targets has been realized. In the light of the growing interest in this emerging field the present review focuses on the classification, structure, function and mechanism of inactive enzymes. Examples of how inactivity is defined, how this is reflected in the structure, functions of inactive enzymes in biological processes and their mode of action are discussed.

Distinct Metal Isoforms Underlie Promiscuous Activity Profiles of Metalloenzymes

Within a superfamily, functionally diverged metalloenzymes often favor different metals as cofactors for catalysis. One hypothesis is that incorporation of alternative metals expands the catalytic repertoire of metalloenzymes and provides evolutionary springboards toward new catalytic functions. However, there is little experimental evidence that incorporation of alternative metals changes the activity profile of metalloenzymes. Here, we systematically investigate how metals alter the activity profiles of five functionally diverged enzymes of the metallo-β-lactamase (MBL) superfamily. Each enzyme was reconstituted in vitro with six different metals, Cd(2+), Co(2+), Fe(2+), Mn(2+), Ni(2+), and Zn(2+), and assayed against eight catalytically distinct hydrolytic reactions (representing native functions of MBL enzymes). We reveal that each enzyme metal isoform has a significantly different activity level for native and promiscuous reactions. Moreover, metal preferences for native versus promiscuous activities are not correlated and, in some cases, are mutually exclusive; only particular metal isoforms disclose cryptic promiscuous activities but often at the expense of the native activity. For example, the L1 B3 β-lactamase displays a 1000-fold catalytic preference for Zn(2+) over Ni(2+) for its native activity but exhibits promiscuous thioester, phosphodiester, phosphotriester, and lactonase activity only with Ni(2+). Furthermore, we find that the five MBL enzymes exist as an ensemble of various metal isoforms in vivo, and this heterogeneity results in an expanded activity profile compared to a single metal isoform. Our study suggests that promiscuous activities of metalloenzymes can stem from an ensemble of metal isoforms in the cell, which could facilitate the functional divergence of metalloenzymes.

The Classification and Evolution of Enzyme Function

Enzymes are the proteins responsible for the catalysis of life. Enzymes sharing a common ancestor as defined by sequence and structure similarity are grouped into families and superfamilies. The molecular function of enzymes is defined as their ability to catalyze biochemical reactions; it is manually classified by the Enzyme Commission and robust approaches to quantitatively compare catalytic reactions are just beginning to appear. Here, we present an overview of studies at the interface of the evolution and function of enzymes.

Active biopolymers in green non-conventional media: a sustainable tool for developing clean chemical processes

By understanding structure–function relationships of active biopolymers (e.g. enzymes and nucleic acids) in green non-conventional media, sustainable chemical processes may be developed.

Introduction to the thematic minireview series on enzyme evolution

In this thematic minireview series, the JBC presents five provocative articles on Enzyme Evolution. The reviews discuss stimulating concepts that include the emergence of primordial catalysts at temperatures that were considerably warmer than present day ones and the impact of the cooling environment on the evolution of catalytic fitness and the preservation of catalysis-promoting conformational dynamics. They also discuss the use of Urzymes or invariant modules in enzyme superfamilies as paradigms for understanding the evolution of catalytic efficiency and specificity, the use of bioinformatics approaches to understand the roles of substrate ambiguity and catalytic promiscuity as drivers of evolution, and the challenges associated with assigning catalytic function as the number of superfamily members grows rapidly.