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

Functional metagenomic screening identifies an unexpected β-glucuronidase

The abundance of recorded protein sequence data stands in contrast to the small number of experimentally verified functional annotation. Here we screened a million-membered metagenomic library at ultrahigh throughput in microfluidic droplets for β-glucuronidase activity. We identified SN243, a genuine β-glucuronidase with little homology to previously studied enzymes of this type, as a glycoside hydrolase 3 family member. This glycoside hydrolase family contains only one recently added β-glucuronidase, showing that a functional metagenomic approach can shed light on assignments that are currently 'unpredictable' by bioinformatics. Kinetic analyses of SN243 characterized it as a promiscuous catalyst and structural analysis suggests regions of divergence from homologous glycoside hydrolase 3 members creating a wide-open active site. With a screening throughput of >10⁷ library members per day, picolitre-volume microfluidic droplets enable functional assignments that complement current enzyme database dictionaries and provide bridgeheads for the annotation of unexplored sequence space.

The evolutionary history of the HUP domain

Among the enzyme lineages that undoubtedly emerged prior to the last universal common ancestor is the so-called HUP, which includes Class I aminoacyl tRNA synthetases (AARSs) as well as enzymes mediating NAD, FAD, and CoA biosynthesis. Here, we provide a detailed analysis of HUP evolution, from emergence to structural and functional diversification. The HUP is a nucleotide binding domain that uniquely catalyzes adenylation via the release of pyrophosphate. In contrast to other ancient nucleotide binding domains with the αβα sandwich architecture, such as P-loop NTPases, the HUP's most conserved feature is not phosphate binding, but rather ribose binding by backbone interactions to the tips of β1 and/or β4. Indeed, the HUP exhibits unusual evolutionary plasticity and, while ribose binding is conserved, the location and mode of binding to the base and phosphate moieties of the nucleotide, and to the substrate(s) reacting with it, have diverged with time, foremost along the emergence of the AARSs. The HUP also beautifully demonstrates how a well-packed scaffold combined with evolvable surface elements promotes evolutionary innovation. Finally, we offer a scenario for the emergence of the HUP from a seed βαβ fragment, and suggest that despite an identical architecture, the HUP and the Rossmann represent independent emergences.

Diverse evolutionary origins of microbial [4 + 2]-cyclases in natural product biosynthesis

Natural [4 + 2]-cyclases catalyze concerted cycloaddition during biosynthesis of over 400 natural products reported. Microbial [4 + 2]-cyclases are structurally diverse with a broad range of substrates. Thus far, about 52 putative microbial [4 + 2]-cyclases of 13 different types have been characterized, with over 20 crystal structures. However, how these cyclases have evolved during natural product biosynthesis remains elusive. Structural and phylogenetic analyses suggest that these different types of [4 + 2]-cyclases might have diverse evolutionary origins, such as reductases, dehydratases, methyltransferases, oxidases, etc. Divergent evolution of enzyme function might have occurred in these different families. Understanding the independent evolutionary history of these cyclases would provide new insights into their catalysis mechanisms and the biocatalyst design.

Enzyme annotation in UniProtKB using Rhea

Motivation

To provide high quality computationally tractable enzyme annotation in UniProtKB using Rhea, a comprehensive expert-curated knowledgebase of biochemical reactions which describes reaction participants using the ChEBI (Chemical Entities of Biological Interest) ontology.

Results

We replaced existing textual descriptions of biochemical reactions in UniProtKB with their equivalents from Rhea, which is now the standard for annotation of enzymatic reactions in UniProtKB. We developed improved search and query facilities for the UniProt website, REST API and SPARQL endpoint that leverage the chemical structure data, nomenclature and classification that Rhea and ChEBI provide.

Availability and implementation

UniProtKB at https://www.uniprot.org; UniProt REST API at https://www.uniprot.org/help/api; UniProt SPARQL endpoint at https://sparql.uniprot.org/; Rhea at https://www.rhea-db.org.

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).

Challenges in the annotation of pseudoenzymes in databases: the UniProtKB approach

The universal protein knowledgebase (UniProtKB) collects and centralises functional information on proteins across a wide range of species. In addition to the functional information added to all protein entries, for enzymes, which represent 20-40% of most proteomes, UniProtKB provides additional information about Enzyme Commission classification, catalytic activity, cofactors, enzyme regulation, kinetics and pathways, all based on critical assessment of published experimental data. Computer-based analysis and structural data are used to enrich the annotation of the sequence through the identification of active sites and binding sites. While the annotation of enzymes is well-defined, the curation of pseudoenzymes in UniProtKB has highlighted some challenges: how to identify them, how to assess their lack of catalytic activity, how to annotate their lack of catalytic activity in a consistent way and how much can be inferred and propagated from experimental data obtained from other species. Through various examples, we illustrate some of these issues and discuss some of the changes we propose to enhance the annotation and discovery of pseudoenzymes. Ultimately, improving the curation of pseudoenzymes will provide the scientific community with a comprehensive resource for pseudoenzymes, which in turn will lead to a better understanding of the evolution of these molecules, the aetiology of related diseases and the development of drugs.