B. Hillerich

Homologue structure of the SLAC1 anion channel for closing stomata in leaves

The plant SLAC1 anion channel controls turgor pressure in the aperture-defining guard cells of plant stomata, thereby regulating the exchange of water vapour and photosynthetic gases in response to environmental signals such as drought or high levels of carbon dioxide. Here we determine the crystal structure of a bacterial homologue (Haemophilus influenzae) of SLAC1 at 1.20 Å resolution, and use structure-inspired mutagenesis to analyse the conductance properties of SLAC1 channels. SLAC1 is a symmetrical trimer composed from quasi-symmetrical subunits, each having ten transmembrane helices arranged from helical hairpin pairs to form a central five-helix transmembrane pore that is gated by an extremely conserved phenylalanine residue. Conformational features indicate a mechanism for control of gating by kinase activation, and electrostatic features of the pore coupled with electrophysiological characteristics indicate that selectivity among different anions is largely a function of the energetic cost of ion dehydration.

Discovery of new enzymes and metabolic pathways by using structure and genome context

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.

Large-Scale Determination of Sequence, Structure, and Function Relationships in Cytosolic Glutathione Transferases across the Biosphere

Global networks of the cytosolic glutathione S-transferases illuminate sequence-structure-function relationships across more than 13,000 members of this superfamily, including experimental confirmation of enzymatic activity for 82 members and new crystal structures for 27.

Homology models guide discovery of diverse enzyme specificities among dipeptide epimerases in the enolase superfamily

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.

Prediction and characterization of enzymatic activities guided by sequence similarity and genome neighborhood networks

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. DOI: http://dx.doi.org/10.7554/eLife.03275.001

Prediction of function for the polyprenyl transferase subgroup in the isoprenoid synthase superfamily.

Significance This paper reports a large-scale collaborative study of an approach for predicting the function of chain elongation prenyltransferases from genetic data. A diverse set of genes for enzymes in the isoprenoid synthase superfamily was identified for cloning, expression, X-ray structural analysis, and prediction of function by docking to homology models. Blind predictions, later verified biochemically, were accurate to within one isoprene unit for all but a few of the 74 enzymes studied, an extraordinarily high level of prediction given that the enzymes often give products whose chain lengths vary by one isoprene unit. The number of available protein sequences has increased exponentially with the advent of high-throughput genomic sequencing, creating a significant challenge for functional annotation. Here, we describe a large-scale study on assigning function to unknown members of the trans-polyprenyl transferase (E-PTS) subgroup in the isoprenoid synthase superfamily, which provides substrates for the biosynthesis of the more than 55,000 isoprenoid metabolites. Although the mechanism for determining the product chain length for these enzymes is known, there is no simple relationship between function and primary sequence, so that assigning function is challenging. We addressed this challenge through large-scale bioinformatics analysis of >5,000 putative polyprenyl transferases; experimental characterization of the chain-length specificity of 79 diverse members of this group; determination of 27 structures of 19 of these enzymes, including seven cocrystallized with substrate analogs or products; and the development and successful application of a computational approach to predict function that leverages available structural data through homology modeling and docking of possible products into the active site. The crystallographic structures and computational structural models of the enzyme–ligand complexes elucidate the structural basis of specificity. As a result of this study, the percentage of E-PTS sequences similar to functionally annotated ones (BLAST e-value ≤ 1e−70) increased from 40.6 to 68.8%, and the percentage of sequences similar to available crystal structures increased from 28.9 to 47.4%. The high accuracy of our blind prediction of newly characterized enzymes indicates the potential to predict function to the complete polyprenyl transferase subgroup of the isoprenoid synthase superfamily computationally.

Panoramic view of a superfamily of phosphatases through substrate profiling

Significance Here, we examine the activity profile of the haloalkanoic acid dehalogenase (HAD) superfamily by screening a customized library against >200 enzymes from a broad sampling of the superfamily. From this dataset, we can infer the function of nearly 35% of the superfamily. Overall, the superfamily was found to show high substrate ambiguity, with 75% of the superfamily utilizing greater than five substrates. In addition, the HAD members with the least amount of structural accessorization of the Rossmann fold were found to be the most specific, suggesting that elaboration of the core domain may have led to increased substrate range of the superfamily. Large-scale activity profiling of enzyme superfamilies provides information about cellular functions as well as the intrinsic binding capabilities of conserved folds. Herein, the functional space of the ubiquitous haloalkanoate dehalogenase superfamily (HADSF) was revealed by screening a customized substrate library against >200 enzymes from representative prokaryotic species, enabling inferred annotation of ∼35% of the HADSF. An extremely high level of substrate ambiguity was revealed, with the majority of HADSF enzymes using more than five substrates. Substrate profiling allowed assignment of function to previously unannotated enzymes with known structure, uncovered potential new pathways, and identified iso-functional orthologs from evolutionarily distant taxonomic groups. Intriguingly, the HADSF subfamily having the least structural elaboration of the Rossmann fold catalytic domain was the most specific, consistent with the concept that domain insertions drive the evolution of new functions and that the broad specificity observed in HADSF may be a relic of this process.

Experimental strategies for functional annotation and metabolism discovery: targeted screening of solute binding proteins and unbiased panning of metabolomes.

The rate at which genome sequencing data is accruing demands enhanced methods for functional annotation and metabolism discovery. Solute binding proteins (SBPs) facilitate the transport of the first reactant in a metabolic pathway, thereby constraining the regions of chemical space and the chemistries that must be considered for pathway reconstruction. We describe high-throughput protein production and differential scanning fluorimetry platforms, which enabled the screening of 158 SBPs against a 189 component library specifically tailored for this class of proteins. Like all screening efforts, this approach is limited by the practical constraints imposed by construction of the library, i.e., we can study only those metabolites that are known to exist and which can be made in sufficient quantities for experimentation. To move beyond these inherent limitations, we illustrate the promise of crystallographic- and mass spectrometric-based approaches for the unbiased use of entire metabolomes as screening libraries. Together, our approaches identified 40 new SBP ligands, generated experiment-based annotations for 2084 SBPs in 71 isofunctional clusters, and defined numerous metabolic pathways, including novel catabolic pathways for the utilization of ethanolamine as sole nitrogen source and the use of d-Ala-d-Ala as sole carbon source. These efforts begin to define an integrated strategy for realizing the full value of amassing genome sequence data.

Structural genomics for drug design against the pathogen Coxiella burnetii.

Coxiella burnetii is a highly infectious bacterium and potential agent of bioterrorism. However, it has not been studied as extensively as other biological agents, and very few of its proteins have been structurally characterized. To address this situation, we undertook a study of critical metabolic enzymes in C. burnetii that have great potential as drug targets. We used high‐throughput techniques to produce novel crystal structures of 48 of these proteins. We selected one protein, C. burnetii dihydrofolate reductase (CbDHFR), for additional work to demonstrate the value of these structures for structure‐based drug design. This enzymes structure reveals a feature in the substrate binding groove that is different between CbDHFR and human dihydrofolate reductase (hDHFR). We then identified a compound by in silico screening that exploits this binding groove difference, and demonstrated that this compound inhibits CbDHFR with at least 25‐fold greater potency than hDHFR. Since this binding groove feature is shared by many other prokaryotes, the compound identified could form the basis of a novel antibacterial agent effective against a broad spectrum of pathogenic bacteria. Proteins 2015; 83:2124–2136.

Assignment of Pterin Deaminase Activity to an Enzyme of Unknown Function Guided by Homology Modeling and Docking

Of the over 22 million protein sequences in the nonredundant TrEMBL database, fewer than 1% have experimentally confirmed functions. Structure-based methods have been used to predict enzyme activities from experimentally determined structures; however, for the vast majority of proteins, no such structures are available. Here, homology models of a functionally uncharacterized amidohydrolase from Agrobacterium radiobacter K84 (Arad3529) were computed on the basis of a remote template structure. The protein backbone of two loops near the active site was remodeled, resulting in four distinct active site conformations. Substrates of Arad3529 were predicted by docking of 57,672 high-energy intermediate (HEI) forms of 6440 metabolites against these four homology models. On the basis of docking ranks and geometries, a set of modified pterins were suggested as candidate substrates for Arad3529. The predictions were tested by enzymology experiments, and Arad3529 deaminated many pterin metabolites (substrate, k(cat)/K(m) [M(-1) s(-1)]): formylpterin, 5.2 × 10(6); pterin-6-carboxylate, 4.0 × 10(6); pterin-7-carboxylate, 3.7 × 10(6); pterin, 3.3 × 10(6); hydroxymethylpterin, 1.2 × 10(6); biopterin, 1.0 × 10(6); d-(+)-neopterin, 3.1 × 10(5); isoxanthopterin, 2.8 × 10(5); sepiapterin, 1.3 × 10(5); folate, 1.3 × 10(5), xanthopterin, 1.17 × 10(5); and 7,8-dihydrohydroxymethylpterin, 3.3 × 10(4). While pterin is a ubiquitous oxidative product of folate degradation, genomic analysis suggests that the first step of an undescribed pterin degradation pathway is catalyzed by Arad3529. Homology model-based virtual screening, especially with modeling of protein backbone flexibility, may be broadly useful for enzyme function annotation and discovering new pathways and drug targets.