Search

Over the last 25 years, biology has entered the genomic era and is becoming a science of 'big data'. Most interpretations of genomic analyses rely on accurate functional annotations of the proteins encoded by more than 500 000 genomes sequenced to date. By different estimates, only half the predicted sequenced proteins carry an accurate functional annotation, and this percentage varies drastically between different organismal lineages. Such a large gap in knowledge hampers all aspects of biological enterprise and, thereby, is standing in the way of genomic biology reaching its full potential. A brainstorming meeting to address this issue funded by the National Science Foundation was held during 3-4 February 2022. Bringing together data scientists, biocurators, computational biologists and experimentalists within the same venue allowed for a comprehensive assessment of the current state of functional annotations of protein families. Further, major issues that were obstructing the field were identified and discussed, which ultimately allowed for the proposal of solutions on how to move forward.

The functions of most proteins are yet to be determined. The function of an enzyme is often defined by its interacting partners, including its substrate and product, and its role in larger metabolic networks. Here, we describe a computational method that predicts the functions of orphan enzymes by organizing them into a linear metabolic pathway. Given candidate enzyme and metabolite pathway members, this aim is achieved by finding those pathways that satisfy structural and network restraints implied by varied input information, including that from virtual screening, chemoinformatics, genomic context analysis, and ligand -binding experiments. We demonstrate this integrative pathway mapping method by predicting the L-gulonate catabolic pathway in Haemophilus influenzae Rd KW20. The prediction was subsequently validated experimentally by enzymology, crystallography, and metabolomics. Integrative pathway mapping by satisfaction of structural and network restraints is extensible to molecular networks in general and thus formally bridges the gap between structural biology and systems biology.

The functions of most proteins are yet to be determined. The function of an enzyme is often defined by its interacting partners, including its substrate and product, and its role in larger metabolic networks. Here, we describe a computational method that predicts the functions of orphan enzymes by organizing them into a linear metabolic pathway. Given candidate enzyme and metabolite pathway members, this aim is achieved by finding those pathways that satisfy structural and network restraints implied by varied input information, including that from virtual screening, chemoinformatics, genomic context analysis, and ligand -binding experiments. We demonstrate this integrative pathway mapping method by predicting the L-gulonate catabolic pathway in Haemophilus influenzae Rd KW20. The prediction was subsequently validated experimentally by enzymology, crystallography, and metabolomics. Integrative pathway mapping by satisfaction of structural and network restraints is extensible to molecular networks in general and thus formally bridges the gap between structural biology and systems biology.

Using a large-scale "genomic enzymology" approach, we (i) assigned novel ATP-dependent four-carbon acid sugar kinase functions to members of the DUF1537 protein family (domain of unknown function; Pfam families PF07005 and PF17042) and (ii) discovered novel catabolic pathways for d-threonate, l-threonate, and d-erythronate. The experimentally determined ligand specificities of several solute binding proteins (SBPs) for TRAP (tripartite ATP-independent permease) transporters for four-carbon acids, including d-erythronate and l-erythronate, were used to constrain the substrates for the catabolic pathways that degrade the SBP ligands to intermediates in central carbon metabolism. Sequence similarity networks and genome neighborhood networks were used to identify the enzyme components of the pathways. Conserved genome neighborhoods encoded SBPs as well as permease components of the TRAP transporters, members of the DUF1537 family, and a member of the 4-hydroxy-l-threonine 4-phosphate dehydrogenase (PdxA) oxidative decarboxylase, class II aldolase, or ribulose 1,5-bisphosphate carboxylase/oxygenase, large subunit (RuBisCO) superfamily. Because the characterized substrates of members of the PdxA, class II aldolase, and RuBisCO superfamilies are phosphorylated, we postulated that the members of the DUF1537 family are novel ATP-dependent kinases that participate in catabolic pathways for four-carbon acid sugars. We determined that (i) the DUF1537/PdxA pair participates in a pathway for the conversion of d-threonate to dihydroxyacetone phosphate and CO2 and (ii) the DUF1537/class II aldolase pair participates in pathways for the conversion of d-erythronate and l-threonate (epimers at carbon-3) to dihydroxyacetone phosphate and CO2 The physiological importance of these pathways was demonstrated in vivo by phenotypic and genetic analyses.

Metabolic pathways in eubacteria and archaea often are encoded by operons and/or gene clusters (genome neighborhoods) that provide important clues for assignment of both enzyme functions and metabolic pathways. We describe a bioinformatic approach (genome neighborhood network; GNN) that enables large scale prediction of the in vitro enzymatic activities and in vivo physiological functions (metabolic pathways) of uncharacterized enzymes in protein families. We demonstrate the utility of the GNN approach by predicting in vitro activities and in vivo functions in the proline racemase superfamily (PRS; InterPro IPR008794). The predictions were verified by measuring in vitro activities for 51 proteins in 12 families in the PRS that represent ∼85% of the sequences; in vitro activities of pathway enzymes, carbon/nitrogen source phenotypes, and/or transcriptomic studies confirmed the predicted pathways. The synergistic use of sequence similarity networks3 and GNNs will facilitate the discovery of the components of novel, uncharacterized metabolic pathways in sequenced genomes.

Assigning valid functions to proteins identified in genome projects is challenging: overprediction and database annotation errors are the principal concerns. We and others are developing computation-guided strategies for functional discovery with metabolite docking to experimentally derived or homology-based three-dimensional structures. Bacterial metabolic pathways often are encoded by genome neighbourhoods (gene clusters and/or operons), which can provide important clues for functional assignment. We recently demonstrated the synergy of docking and pathway context by predicting the intermediates in the glycolytic pathway in Escherichia coli. Metabolite docking to multiple binding proteins and enzymes in the same pathway increases the reliability of in silico predictions of substrate specificities because the pathway intermediates are structurally similar. Here we report that structure-guided approaches for predicting the substrate specificities of several enzymes encoded by a bacterial gene cluster allowed the correct prediction of the in vitro activity of a structurally characterized enzyme of unknown function (PDB 2PMQ), 2-epimerization of trans-4-hydroxy-L-proline betaine (tHyp-B) and cis-4-hydroxy-D-proline betaine (cHyp-B), and also the correct identification of the catabolic pathway in which Hyp-B 2-epimerase participates. The substrate-liganded pose predicted by virtual library screening (docking) was confirmed experimentally. The enzymatic activities in the predicted pathway were confirmed by in vitro assays and genetic analyses; the intermediates were identified by metabolomics; and repression of the genes encoding the pathway by high salt concentrations was established by transcriptomics, confirming the osmolyte role of tHyp-B. This study establishes the utility of structure-guided functional predictions to enable the discovery of new metabolic pathways.

To study the substrate specificity of enzymes, we use the amidohydrolase and enolase superfamilies as model systems; members of these superfamilies share a common TIM barrel fold and catalyze a wide range of chemical reactions. Here, we describe a collaboration between the Enzyme Specificity Consortium (ENSPEC) and the New York SGX Research Center for Structural Genomics (NYSGXRC) that aims to maximize the structural coverage of the amidohydrolase and enolase superfamilies. Using sequence- and structure-based protein comparisons, we first selected 535 target proteins from a variety of genomes for high-throughput structure determination by X-ray crystallography; 63 of these targets were not previously annotated as superfamily members. To date, 20 unique amidohydrolase and 41 unique enolase structures have been determined, increasing the fraction of sequences in the two superfamilies that can be modeled based on at least 30% sequence identity from 45% to 73%. We present case studies of proteins related to uronate isomerase (an amidohydrolase superfamily member) and mandelate racemase (an enolase superfamily member), to illustrate how this structure-focused approach can be used to generate hypotheses about sequence–structure–function relationships.

Superfamily and family analyses provide an effective tool for the functional classification of proteins, but must be automated for use on large datasets. We describe a 'gold standard' set of enzyme superfamilies, clustered according to specific sequence, structure, and functional criteria, for use in the validation of family and superfamily clustering methods. The gold standard set represents four fold classes and differing clustering difficulties, and includes five superfamilies, 91 families, 4,887 sequences and 282 structures.

The functional annotation of proteins identified in genome sequencing projects is based on similarities to homologs in the databases. As a result of the possible strategies for divergent evolution, homologous enzymes frequently do not catalyze the same reaction, and we conclude that assignment of function from sequence information alone should be viewed with some skepticism.

Mechanistic diversity in the RuBisCO superfamily was studied by observing methionine salvage pathway in Geobacillus kaustophilus. It was found that the Geobacillus kaustophilus uses the same structural technique as RuBisCO for stabilizing the enolate anion intermediate, that is coordination to an essential [Mg.sup.2+], while proton abstraction is catalyzed by a different general base.

Members of the mechanistically diverse enolase superfamily catalyze reactions that are initiated by abstraction of the alpha-proton of a carboxylate anion to generate an enolate anion intermediate that is stabilized by coordination to a [Mg.sup.2+] ion. The catalytic groups, ligands for an essential [Mg.sup.2+] and acid/base catalysts, are located in the (beta/alpha)8-barrel domain of the bidomain proteins.

Structural and biochemical evidence suggest that the 3-keto-L-gulonate 6- phosphate decarboxylase (KGPDC) reaction proceeds via a Mg(super 2+)-stabilized 1,2-cis-enediolate intermediate. The catalytic roles of conserved residues in the active site of KGPDC is defined and demonstrated that even though KGPDC and orotidine 5'-monophosphate decarboxylase (OMPDC) share a common active site architecture, each enzyme uses this architecture to catalyze reactions with unrelated substrate specificity and unrelated reactions mechanisms.

The study highlights the opportunistic nature of divergent evolution in conscripting the active site of a progenitor to catalyze a mechanistically distinct reaction. In the report about the structure of the Mg(super 2+)-requiring 3-keto-L-gulonate 6-phosphate decarboxylase (KGPDC), a mechanism is suggested in which the 2-OH group and 3-keto group, the substrate are liganded to the Mg(super 2+).

The crotonase superfamily catalyzes different overall reactions. Bacterial (3S)-Methylglutaconyl-CoA Hydratase (MGCHS) provides an example of opportunistic evolution.

Research has been conducted on 3-keto-L-gulonate 6-phosphate decarboxylase which is encoded by Escherichia coli genome ulaD gene. The structural studies of this decarboxylase are reported.

Research describes the physiologic and genetic basis of L-ascorbate fermentation by Escherichia coli K-12 operon. The operons ula (yif-sga) and yia-sgb code for all the enzymes in the catabolic pathway of L-ascorbate utilization.

Three-dimensional structure analysis of two L-Ala-D/L-Glu epimerases from Escherichica coli and Bacillus subtilis shows significant size differences between them but similar active site architecture.

Two proteins, YcjG and YkfB, from Escherichia coli and Bacillus subtilis, respectively, function as L-Ala-D/L-Glu epimerases and they metabolize murein peptide containing L-Ala-d-Glu. The epimerases catalyze a 1,1-proton transfer.

Research has been conducted on the D-glucarate dehydratase from Escherichia coli (GlucD) that catalyses the D-glucarate and L-idarate dehydration. The structure of the GlucD has been investigated and the results demonstrate that His 339 and Asn 341 dyads are proximal to the position of the 4-OH leaving group.

The review describes the diverse nature and functions of specificity and mechanistically-diverse superfamilies and functionally distinct suprafamilies of enzymes that play a role in divergent evolution of enzyme mechanisms of action.

The two-domain fold found for o-succinylbenzoate synthase (OSBS) of Escherichia coli is like those found for other members of the enolase superfamily. There is a mixed alpha/beta capping domain from parts at the polypeptide N- and C-termini and a larger (beta/alpha)(sub.7)beta barrel domain. Most interactions of OSB and the protein are indirect through water molecules or hydrophobic interactions. Findings could show something about the structural strategy for evolution of new catalytic activities in homologues of OSBS and about an explanation for the OSBS from Amycolaptosis to catalyze the racemization of N-acylamino acids.