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The cytosolic glutathione transferase (cytGST) superfamily comprises more than 13,000 nonredundant sequences found throughout the biosphere. Their key roles in metabolism and defense against oxidative damage have led to thousands of studies over several decades. Despite this attention, little is known about the physiological reactions they catalyze and most of the substrates used to assay cytGSTs are synthetic compounds. A deeper understanding of relationships across the superfamily could provide new clues about their functions. To establish a foundation for expanded classification of cytGSTs, we generated similarity-based subgroupings for the entire superfamily. Using the resulting sequence similarity networks, we chose targets that broadly covered unknown functions and report here experimental results confirming GST-like activity for 82 of them, along with 37 new 3D structures determined for 27 targets. These new data, along with experimentally known GST reactions and structures reported in the literature, were painted onto the networks to generate a global view of their sequence-structure-function relationships. The results show how proteins of both known and unknown function relate to each other across the entire superfamily and reveal that the great majority of cytGSTs have not been experimentally characterized or annotated by canonical class. A mapping of taxonomic classes across the superfamily indicates that many taxa are represented in each subgroup and highlights challenges for classification of superfamily sequences into functionally relevant classes. Experimental determination of disulfide bond reductase activity in many diverse subgroups illustrate a theme common for many reaction types. Finally, sequence comparison between an enzyme that catalyzes a reductive dechlorination reaction relevant to bioremediation efforts with some of its closest homologs reveals differences among them likely to be associated with evolution of this unusual reaction. Interactive versions of the networks, associated with functional and other types of information, can be downloaded from the Structure-Function Linkage Database (SFLD; http://sfld.rbvi.ucsf.edu).

Pathway docking ( in silico docking of metabolites to several enzymes and binding proteins in a metabolic pathway) enables the discovery of a catabolic pathway for the osmolyte trans -4-hydroxy- l -proline betaine. Structural key to predicting enzyme function Overprediction and database annotation errors in genome-sequencing projects have caused much confusion because of the difficulty of assigning valid functions to the proteins identified. These authors use structure-guided approaches for predicting the substrate specificities of several enzymes encoded by a bacterial gene cluster to correctly predict the in vitro activity of an enzyme of unknown function and identify the catabolic pathway in which it participates in cells. The substrate-liganded pose predicted by virtual library screening was confirmed experimentally, enzyme 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. This study establishes the utility of structure-guided functional predictions for the discovery of new metabolic pathways. Assigning valid functions to proteins identified in genome projects is challenging: overprediction and database annotation errors are the principal concerns 1 . We and others 2 are developing computation-guided strategies for functional discovery with ‘metabolite docking’ to experimentally derived 3 or homology-based 4 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 5 . 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.

Some cyclodipeptides are unusual in that their cyclic scaffold is created from activated, amino-acid–loaded tRNA substrates. Structural and biochemical evidence now demonstrates that the enzymes that perform this reaction are homologous to tRNA synthetases and use a covalently bound intermediate. The Mycobacterium tuberculosis enzyme Rv2275 catalyzes the formation of cyclo( L -Tyr- L -Tyr) using two molecules of Tyr-tRNA Tyr as substrates. The three-dimensional (3D) structure of Rv2275 was determined to 2.0-Å resolution, revealing that Rv2275 is structurally related to the class Ic aminoacyl-tRNA synthetase family of enzymes. Mutagenesis and radioactive labeling suggests a covalent intermediate in which L -tyrosine is transferred from Tyr-tRNA Tyr to an active site serine (Ser88) by transesterification with Glu233 serving as a critical base, catalyzing dipeptide bond formation.

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. DNA molecules are polymers in which four nucleotides—guanine, adenine, thymine, and cytosine—are arranged along a sugar backbone. The sequence of these four nucleotides along the DNA strand determines the genetic code of the organism, and can be deciphered using various genome sequencing techniques. Microbial genomes are particularly easy to sequence as they contain fewer than several million nucleotides, compared with the 3 billion or so nucleotides that are present in the human genome. Reading a genome sequence is straight forward, but predicting the physiological functions of the proteins encoded by the genes in the sequence can be challenging. In a process called genome annotation, the function of protein is predicted by comparing the relevant gene to the genes of proteins with known functions. However, microbial genomes and proteins are hugely diverse and over 50% of the microbial genomes that have been sequenced have not yet been related to any physiological function. With thousands of microbial genomes waiting to be deciphered, large scale approaches are needed. Zhao et al. take advantage of a particular characteristic of microbial genomes. DNA sequences that code for two proteins required for the same task tend to be closer to each other in the genome than two sequences that code for unrelated functions. Operons are an extreme example; an operon is a unit of DNA that contains several genes that are expressed as proteins at the same time. Zhao et al. have developed a bioinformatic method called the genome neighbourhood network approach to work out the function of proteins based on their position relative to other proteins in the genome. When applied to the proline racemase superfamily (PRS), which contains enzymes with similar sequences that can catalyze three distinct chemical reactions, the new approach was able to assign a function to the majority of proteins in a public database of PRS enzymes, and also revealed new members of the PRS family. Experiments confirmed that the proteins behaved as predicted. The next challenge is to develop the genome neighbourhood network approach so that it can be applied to more complex systems.

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 rapid advance in genome sequencing presents substantial challenges for protein functional assignment, with half or more of new protein sequences inferred from these genomes having uncertain assignments. The assignment of enzyme function in functionally diverse superfamilies represents a particular challenge, which we address through a combination of computational predictions, enzymology, and structural biology. Here we describe the results of a focused investigation of a group of enzymes in the enolase superfamily that are involved in epimerizing dipeptides. The first members of this group to be functionally characterized were Ala-Glu epimerases in Eschericiha coli and Bacillus subtilis, based on the operon context and enzymological studies; these enzymes are presumed to be involved in peptidoglycan recycling. We have subsequently studied more than 65 related enzymes by computational methods, including homology modeling and metabolite docking, which suggested that many would have divergent specificities;, i.e., they are likely to have different (unknown) biological roles. In addition to the Ala-Phe epimerase specificity reported previously, we describe the prediction and experimental verification of: (i) a new group of presumed Ala-Glu epimerases; (ii) several enzymes with specificity for hydrophobic dipeptides, including one from Cytophaga hutchinsonii that epimerizes D-Ala-D-Ala; and (iii) a small group of enzymes that epimerize cationic dipeptides. Crystal structures for certain of these enzymes further elucidate the structural basis of the specificities. The results highlight the potential of computational methods to guide experimental characterization of enzymes in an automated, large-scale fashion.

Through the use of genetic, enzymatic, metabolomic, and structural analyses, we have discovered the catabolic pathway for proline betaine, an osmoprotectant, in Paracoccus denitrificans and Rhodobacter sphaeroides . Genetic and enzymatic analyses showed that several of the key enzymes of the hydroxyproline betaine degradation pathway also function in proline betaine degradation. Metabolomic analyses detected each of the metabolic intermediates of the pathway. The proline betaine catabolic pathway was repressed by osmotic stress and cold stress, and a regulatory transcription factor was identified. We also report crystal structure complexes of the P. denitrificans HpbD hydroxyproline betaine epimerase/proline betaine racemase with l -proline betaine and cis -hydroxyproline betaine. IMPORTANCE At least half of the extant protein annotations are incorrect, and the errors propagate as the number of genome sequences increases exponentially. A large-scale, multidisciplinary sequence- and structure-based strategy for functional assignment of bacterial enzymes of unknown function has demonstrated the pathway for catabolism of the osmoprotectant proline betaine. At least half of the extant protein annotations are incorrect, and the errors propagate as the number of genome sequences increases exponentially. A large-scale, multidisciplinary sequence- and structure-based strategy for functional assignment of bacterial enzymes of unknown function has demonstrated the pathway for catabolism of the osmoprotectant proline betaine.

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.

Colocation of the genes encoding ABC, TRAP, and TCT transport systems and catabolic pathways for the transported ligand provides a strategy for discovering novel microbial enzymes and pathways. We screened solute-binding proteins (SBPs) for ABC transport systems and identified three that bind d-apiose, a branched pentose in the cell walls of higher plants. Guided by sequence similarity networks (SSNs) and genome neighborhood networks (GNNs), the identities of the SBPs enabled the discovery of four catabolic pathways for d-apiose with eleven previously unknown reactions. The new enzymes include d-apionate oxidoisomerase, which catalyzes hydroxymethyl group migration, as well as 3-oxo-isoapionate-4-phosphate decarboxylase and 3-oxo-isoapionate-4-phosphate transcarboxylase/hydrolase, which are RuBisCO-like proteins (RLPs). The web tools for generating SSNs and GNNs are publicly accessible (http://efi.igb.illinois.edu/efi-est/), so similar ‘genomic enzymology’ strategies for discovering novel pathways can be used by the community.

Isoniazid is a key drug used in the treatment of tuberculosis. Isoniazid is a pro-drug, which, after activation by the katG -encoded catalase peroxidase, reacts nonenzymatically with NAD + and NADP + to generate several isonicotinoyl adducts of these pyridine nucleotides. One of these, the acyclic 4 S isomer of isoniazid-NAD, targets the inhA -encoded enoyl-ACP reductase, an enzyme essential for mycolic acid biosynthesis in Mycobacterium tuberculosis . Here we show that the acyclic 4 R isomer of isoniazid-NADP inhibits the M. tuberculosis dihydrofolate reductase (DHFR), an enzyme essential for nucleic acid synthesis. This biologically relevant form of the isoniazid adduct is a subnanomolar bisubstrate inhibitor of M. tuberculosis DHFR. Expression of M. tuberculosis DHFR in Mycobacterium smegmatis mc 2 155 protects cells against growth inhibition by isoniazid by sequestering the drug. Thus, M. tuberculosis DHFR is the first new target for isoniazid identified in the last decade.