Enzyme Function Initiative Research Articles

Functional Diversity and Structural Analysis of SAM‐dependent Aminobutanoyl Transferases

Aminobutanoyl transferases (ABT) catalyze the transfer of 2‐aminobutanoate from S‐adenosyl‐L‐methionine (SAM) in the production of a variety of natural products. Nicotianamine (NA), a metal‐chelator found in plants, is a classic example and is biosynthesized by nicotianamine synthase through the condensation of three aminobutanoyl moieties from three successive SAM molecules with the first moiety formed into an azetidine ring. Although NA is common in plants, fungal, archaeal and bacterial species have also been shown to produce NA‐like molecules using ABTs. Examples have been characterized from the bacterium S. aureus and the archaeal species M. thermautotrophicus. Structural and functional data remains limited, but existing data suggests a diversity of products using one or more SAM moieties and/or additional amino acid substrates. We used Enzyme Function Initiative (EFI) tools to create sequence similarity and genome neighborhood networks to explore the diversity of ABT‐containing operons and to guide our predictions for the functional roles of distinct ABTs. We also used multi‐sequence alignments of our EFI dataset and structural alignments of the two existing ABT crystal structures to identify structural motifs that may be important to function. We have identified five functional ABT classes producing distinct product types. Example hypotheses from our analysis include the identification of consecutive asparagines deep in the active site pocket that appear to predict processivity and the presence of a serine/alanine substitution that correlates with stereoselectivity. To begin testing hypotheses developed from our bioinformatic analysis, we have selected four ABTs for heterologous expression and purification. We have successfully purified a carboxyazetidine‐forming ABT from P. aeruginosa and have begun crystallization trials. Purification of additional ABTs and the development of an HPLC‐based activity assay is ongoing. Through the characterization of ABTs selected from our bioinformatic analysis we expect to establish rules for predicting processivity, carboxyazetidine ring formation and the use of amino acid substrates by uncharacterized ABTs and to expand the limited available structural data using X‐ray crystallography.

Enzyme Function Prediction, Discovery, and Characterization in Undergraduate Biochemistry Teaching and Research Labs

The Structural Genomics Initiative was an effort by consortiums to solve as many unique protein structures as possible; the Protein Data Bank contains a number of enzymes whose structures have been solved, but for which no enzymatic activity has been determined. The Enzyme Function Initiative is an effort to determine as many unique enzyme functions as possible. There are a number of putative NUDIX Hydrolase superfamily members whose structures have been solved, but for which no enzymatic activity has been determined. We have catalogued the structurally-determined enzymes within the NUDIX Hydrolase superfamily using BLAST, Dali, SCOP, and PDB. We then began to characterize these enzymes in the biochemistry teaching laboratory and are finishing their characterization in the research lab. In the biochemistry lab course, the students have expressed his-tagged Nudix Hydrolases (PDB entries 2AZW, 2PQV, 3QSJ, and 3R03) from plasmids obtained from DNASU, purified the enzymes using nickel affinity chromatography, and then did enzyme assays to determine the substrates. From the assays, the students discovered Nudix Hydrolase activities for these enzymes, which we have finished characterizing in the research lab.

IurV, Encoded by ORF VCA0231, Is Involved in the Regulation of Iron Uptake Genes in Vibrio cholerae.

The pathogen Vibrio cholerae has multiple iron acquisition systems which allow bacteria to exploit a variety of iron sources across the different environments on which it thrives. The expression of such iron uptake systems is highly regulated, mainly by the master iron homeostasis regulator Fur but also by other mechanisms. Recently, we documented that the expression of many of the iron-responsive genes is also modulated by riboflavin. Among them, the open reading frame VCA0231, repressed both by riboflavin and iron, encodes a putative transcriptional regulator of the AraC/XylS family. Nonetheless, the genes or functions affected by this factor are unknown. In the present study, a series of in silico analyses was performed in order to identify the putative functions associated with the product of VCA0231. The STRING database predicted many iron uptake genes as functional partners for the product of VCA0231. In addition, a genomic neighborhood analysis with the Enzyme Function Initiative tools detected many Pfam families involved in iron homeostasis genetically associated with VCA0231. Moreover, a phylogenetic tree showed that other AraC/XylS members known to regulate siderophore utilization in bacteria clustered together and the product of VCA0231 localized in this cluster. This suggested that the product of VCA0231, here named IurV, is involved in the regulation of iron uptake processes. RNAseq was performed to determine the transcriptional effects of a deletion in VCA0231. A total of 52 genes were overexpressed and 21 genes were downregulated in response to the iurV deletion. Among these, several iron uptake genes and other iron homeostasis-related genes were found. Six gene ontology (GO) functional terms were enriched in the upregulated genes, of which five were related to iron metabolism. The regulatory pattern observed in the transcriptomics of a subset of genes was independently confirmed by quantitative real time PCR analysis. The results indicate that IurV is a novel regulator of the AraC/XylS family involved in the repression of iron uptake genes. Whether this effect is direct or indirect remains to be determined.

Conserved genomic neighborhood is a strong but no perfect indicator for a direct interaction of microbial gene products

BackgroundThe order of genes in bacterial genomes is not random; for example, the products of genes belonging to an operon work together in the same pathway. The cotranslational assembly of protein complexes is deemed to conserve genomic neighborhoods even stronger than a common function. This is why a conserved genomic neighborhood can be utilized to predict, whether gene products form protein complexes.ResultsWe were interested to assess the performance of a neighborhood-based classifier that analyzes a large number of genomes. Thus, we determined for the genes encoding the subunits of 494 experimentally verified hetero-dimers their local genomic context. In order to generate phylogenetically comprehensive genomic neighborhoods, we utilized the tools offered by the Enzyme Function Initiative. For each subunit, a sequence similarity network was generated and the corresponding genome neighborhood network was analyzed to deduce the most frequent gene product. This was predicted as interaction partner, if its abundance exceeded a threshold, which was the frequency giving rise to the maximal Matthews correlation coefficient. For the threshold of 16%, the true positive rate was 45%, the false positive rate 0.06%, and the precision 55%. For approximately 20% of the subunits, the interaction partner was not found in a neighborhood of ± 10 genes.ConclusionsOur phylogenetically comprehensive analysis confirmed that complex formation is a strong evolutionary factor that conserves genome neighborhoods. On the other hand, for 55% of the cases analyzed here, classification failed. Either, the interaction partner was not present in a ± 10 gene window or was not the most frequent gene product.

The EFI Web Resource for Genomic Enzymology Tools: Leveraging Protein, Genome, and Metagenome Databases to Discover Novel Enzymes and Metabolic Pathways.

The assignment of functions to uncharacterized proteins discovered in genome projects requires easily accessible tools and computational resources for large-scale, user-friendly leveraging of the protein, genome, and metagenome databases by experimentalists. This article describes the web resource developed by the Enzyme Function Initiative (EFI; accessed at https://efi.igb.illinois.edu/ ) that provides "genomic enzymology" tools ("web tools") for (1) generating sequence similarity networks (SSNs) for protein families (EFI-EST); (2) analyzing and visualizing genome context of the proteins in clusters in SSNs (in genome neighborhood networks, GNNs, and genome neighborhood diagrams, GNDs) (EFI-GNT); and (3) prioritizing uncharacterized SSN clusters for functional assignment based on metagenome abundance (chemically guided functional profiling, CGFP) (EFI-CGFP). The SSNs generated by EFI-EST are used as the input for EFI-GNT and EFI-CGFP, enabling easy transfer of information among the tools. The networks are visualized and analyzed using Cytoscape, a widely used desktop application; GNDs and CGFP heatmaps summarizing metagenome abundance are viewed within the tools. We provide a detailed example of the integrated use of the tools with an analysis of glycyl radical enzyme superfamily (IPR004184) found in the human gut microbiome. This analysis demonstrates that (1) SwissProt annotations are not always correct, (2) large-scale genome context analyses allow the prediction of novel metabolic pathways, and (3) metagenome abundance can be used to identify/prioritize uncharacterized proteins for functional investigation.

Computational Studies of the NUDIX Hydrolase Superfamily

The Protein Structure Initiative (PSI) was an effort by consortiums to solve as many unique protein structures as possible; the Protein Data Bank contains a number of enzymes whose structures have been solved, but for which no enzymatic activity has been determined. The Enzyme Function Initiative (EFI) is an effort to determine as many unique enzyme functions as possible. There are a number of putative NUDIX Hydrolase superfamily members for which structures have been solved, but for which no enzymatic activity has been determined. We have catalogued the structurally‐determined enzymes within the NUDIX Hydrolase superfamily using the PDB, BLAST, Dali, and SCOP. We are now in the process of characterizing these enzymes in the biochemistry teaching laboratory.Computational programs, such as ProMOL and UCSF Chimera, aid in annotating uncharacterized enzymes. ProMOL works by aligning the relative spatial arrangement of the catalytic residues of a reference enzyme with query searches. Chimera is a molecular visualization program that provides various tools for the structural analysis of proteins, including comparative modeling and conformational analysis. We are using these programs to design catalytic motifs for the NUDIX hydrolase superfamily and subfamilies within, which then can be used to uncover family members whose structures have been solved. Likewise, we have used the docking program PyRx, which utilizes AutoDock Vina, to predict substrate specificity, and have discovered that due to its “static” nature, it does not satisfactorily predict substrate specificity. Thus, next we will use a “dynamic” docking program to reevaluate substrate specificity prediction. Through this process, we have discovered the strengths and limitations of these programs.Bioinformatics tools, including databases and computational programs, are good starting points for determining protein function; however, the only way to definitively determine protein function is through experimentally determining activity. Our ultimate goal is to discover and characterize new enzymes from human pathogens as potential novel antibiotic targets.Support or Funding InformationNSF IUSE grant (PAC, JLM, and SOH). RIT College of Science FEAD grant (SOH and JLM).This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

AGeNNT: annotation of enzyme families by means of refined neighborhood networks

BackgroundLarge enzyme families may contain functionally diverse members that give rise to clusters in a sequence similarity network (SSN). In prokaryotes, the genome neighborhood of a gene-product is indicative of its function and thus, a genome neighborhood network (GNN) deduced for an SSN provides strong clues to the specific function of enzymes constituting the different clusters. The Enzyme Function Initiative (http://enzymefunction.org/) offers services that compute SSNs and GNNs.ResultsWe have implemented AGeNNT that utilizes these services, albeit with datasets purged with respect to unspecific protein functions and overrepresented species. AGeNNT generates refined GNNs (rGNNs) that consist of cluster-nodes representing the sequences under study and Pfam-nodes representing enzyme functions encoded in the respective neighborhoods. For cluster-nodes, AGeNNT summarizes the phylogenetic relationships of the contributing species and a statistic indicates how unique nodes and GNs are within this rGNN. Pfam-nodes are annotated with additional features like GO terms describing protein function. For edges, the coverage is given, which is the relative number of neighborhoods containing the considered enzyme function (Pfam-node). AGeNNT is available at https://github.com/kandlinf/agennt.ConclusionsAn rGNN is easier to interpret than a conventional GNN, which commonly contains proteins without enzymatic function and overly specific neighborhoods due to phylogenetic bias. The implemented filter routines and the statistic allow the user to identify those neighborhoods that are most indicative of a specific metabolic capacity. Thus, AGeNNT facilitates to distinguish and annotate functionally different members of enzyme families.

Tools and strategies for discovering novel enzymes and metabolic pathways

Summary The number of entries in the sequence databases continues to increase exponentially – the UniProt database is increasing with a doubling time of ∼4 years (2% increase/month). Approximately 50% of the entries have uncertain, unknown, or incorrect function annotations because these are made by automated methods based on sequence homology. If the potential in complete genome sequences is to be realized, strategies and tools must be developed to facilitate experimental assignment of functions to uncharacterized proteins discovered in genome projects. The Enzyme Function Initiative (EFI; previously supported by U54GM093342 from the National Institutes of Health, now supported by P01GM118303) developed web tools for visualizing and analyzing (1) sequence and function space in protein families (EFI-EST) and (2) genome neighbourhoods in microbial and fungal genomes (EFI-GNT) to assist the design of experimental strategies for discovering the in vitro activities and in vivo metabolic functions of uncharacterized enzymes. The EFI developed an experimental platform for large-scale production of the solute binding proteins (SBPs) for ABC, TRAP, and TCT transport systems and their screening with a physical ligand library to identify the identities of the ligands for these transport systems. Because the genes that encode transport systems are often co-located with the genes that encode the catabolic pathways for the transported solutes, the identity of the SBP ligand together with the EFI-EST and EFI-GNT web tools can be used to discover new enzyme functions and new metabolic pathways. This approach is demonstrated with the characterization of a novel pathway for ethanolamine catabolism.

ATP-binding Cassette (ABC) Transport System Solute-binding Protein-guided Identification of Novel d-Altritol and Galactitol Catabolic Pathways in Agrobacterium tumefaciens C58

Innovations in the discovery of the functions of uncharacterized proteins/enzymes have become increasingly important as advances in sequencing technology flood protein databases with an exponentially growing number of open reading frames. This study documents one such innovation developed by the Enzyme Function Initiative (EFI; U54GM093342), the use of solute-binding proteins for transport systems to identify novel metabolic pathways. In a previous study, this strategy was applied to the tripartite ATP-independent periplasmic transporters. Here, we apply this strategy to the ATP-binding cassette transporters and report the discovery of novel catabolic pathways for d-altritol and galactitol in Agrobacterium tumefaciens C58. These efforts resulted in the description of three novel enzymatic reactions as follows: 1) oxidation of d-altritol to d-tagatose via a dehydrogenase in Pfam family PF00107, a previously unknown reaction; 2) phosphorylation of d-tagatose to d-tagatose 6-phosphate via a kinase in Pfam family PF00294, a previously orphan EC number; and 3) epimerization of d-tagatose 6-phosphate C-4 to d-fructose 6-phosphate via a member of Pfam family PF08013, another previously unknown reaction. The epimerization reaction catalyzed by a member of PF08013 is especially noteworthy, because the functions of members of PF08013 have been unknown. These discoveries were assisted by the following two synergistic bioinformatics web tools made available by the Enzyme Function Initiative: the EFI-Enzyme Similarity Tool and the EFI-Genome Neighborhood Tool.

A Novel Pathway for Bacterial Ethanolamine Metabolism

Ethanolamine, a major component of the cell membranes of bacteria and animals in the form of phosphatidylethanolamine, can be a source of carbon and/or nitrogen for many bacterial species. The canonical model for bacterial ethanolamine utilization (eut) requires the adenosylcobalamin-dependent enzyme ethanolamine ammonia-lyase, which catalyzes the conversion of ethanolamine to ammonia and acetaldehyde for cellular nitrogen and carbon, respectively. Here, we report discovery and characterization of a novel pathway for bacterial ethanolamine metabolism in organisms that lack canonical eut genes. We use molecular docking, enzymology, microbiology, and mass spectrometry to delineate a four-enzyme pathway for ethanolamine catabolism that terminates in glycine, a source of cellular nitrogen. Initially, a gamma-glutamyl synthetase catalyzes the ATP-dependent condensation of ethanolamine with L-glutamate to form γ-glutamyl-ethanolamide. Oxidation of the hydroxyl group of γ-glutamyl-ethanolamide by an alcohol dehydrogenase and subsequent oxidation of the resulting aldehyde by an aldehyde dehydrogenase leads to formation of γ-glutamyl glycine. Finally, hydrolysis of the γ-glutamyl glycine peptide bond by an amidohydrolase yields L-glutamate and glycine, which is assimilated as a nitrogen source. Using the Enzyme Function Initiative Enzyme Similarity Tool (EFI-EST) and Genome Neighborhood Tool (EFI-GNT), we show that a diverse set of organisms ranging from the Actinobacteria to the Proteobacteria possess the genetic capability for non-eut ethanolamine metabolism, with multiple apparent instances of non-orthologous gene displacement (NOD) between different phyla. This research was supported by US National Institutes of Health grant U54GM093342.

Sequence Similarity Networks for the Protein Universe

As of November 2014, over 86 million protein sequences had been deposited in the TrEMBL database, of which only 0.5 million had experimental support for an enzymatic function. Currently, protein databases depend heavily on homology-based predictions of enzyme function, yet it is estimated that only 50% of current predicted functions in UniprotKB are correct. The process would benefit greatly from added expertise, while still maintaining a balance of careful curation and throughput. For sequence comparison and visualization, the sequence similarity network (SSN) is a computationally efficient alternative to the standard dendrogram. Making SSNs easily accessible to the non-bioinformatician allows enzymologists, microbiologists, and chemists to observe the sequence identity landscape for a protein family of interest and select more informed identity boundaries for appropriate transfer of function via homology. This talk describes the efforts of the Enzyme Function Initiative to provide precomputed SSNs for each unique protein family described in the Pfam database, covering ~80% of the protein universe. The project combines state-of-the-art computational resources with a rigorous workflow that accommodates updates three times per year. Networks generated at the minimum E-value threshold (E-5) are available instantaneously (compared to 5.5 hr average for de novo generation), and generation times for networks with a user-defined E-value threshold are reduced 10-fold. This talk also addresses how universal precomputation of Pfam SSNs facilitates integration of sequence similarity information with additional EFI tools for expedited enzyme function discovery. Supported by NIH grant U54 GM093342.

Transforming biomedical and structural data into information and knowledge

The LabDB laboratory information management system (LIMS) tracks, organizes and analyzes data from chemical and solution management, protein production, crystallization, diffraction, structure solution, and in vitro biochemical and biophysical experiments. The system is comprised of multiple modules specialized for different tasks, such as the Xtaldb system for crystallization or the hkldb module of the HKL-3000 suite for diffraction data collection and structure solution. The biochemical/biophysical experiments tracked by LabDB include spectrophotometric binding and kinetics, thermal shift binding, isothermal titration calorimetry (ITC) and protein quantitation. These tools associate functional and structural experiments, for example, for selecting likely substrates for co-crystallization and soaking experiments. Whenever possible, the system harvests data with no or minimal user intervention from laboratory hardware. Devices that may connect to or import data into LabDB include crystal observation (Rigaku Minstrel HT and Formulatrix Rock Imager), liquid handling (Formulatrix Rock Maker and Emerald Opti-Matrix Maker), chromatography (GE Healthcare AKTA), quantitation (Caliper LabChip GX II and Bio-Rad Gel Doc EZ), RT-PCR (Applied Biosystems 7900HT and Bio-Rad C1000/CFX96) and ITC (MicroCal iTC-200) systems. LabDB is used by two high-throughput PSI:Biology centers (MCSG and NYSGRC) as well as other major NIH consortia (the Center for Structural Genomics of Infectious Diseases and the Enzyme Function Initiative), and track millions of experiments on tens of thousands of targets.[1] The system also provides extensive data mining and analysis tools for translating raw experimental data into information and knowledge. We present examples of analyses generated by the system useful in designing new experiments.

Discovery of novel enzyme activities and metabolic pathways (227.3)

The Enzyme Function Initiative (EFI) is a Large‐Scale Collaborative Project (U54GM093342) that is devising strategies and tools to facilitate the discovery of the in vitro activities and in vivo metabolic functions of enzymes discovered in genome projects. This talk will provide examples of how 1) bioinformatic and computational tools can be used to predict novel enzymatic functions and pathways; and 2) enzymology, microbiology, and metabolomics can be used to verify those functions and place them in metabolic context. Supported by NIH U54GM093342.

Filling In The Gaps

The protein community is awash in data. As of October, the UniProt database of protein sequences had more than 44 million entries. UniProt serves as a clearinghouse for sequences. Researchers who identify new sequences can add them and annotate them by including any functional information they’ve determined or inferred. Others can annotate sequences as well. The functions of a small fraction of the millions of entries have been experimentally determined. But most proteins are annotated only on the basis of their similarity to other proteins or aren’t annotated at all. To make matters worse, many of those tentative annotations are wrong. And the problem only grows as more genomes are sequenced and more proteins of unknown function are identified. What can be done to ferret out the protein functions buried in this tidal wave of data? That’s where the Enzyme Function Initiative (EFI) comes in. This consortium of approximately 80 ...

The Enzyme Function Initiative

The Enzyme Function Initiative (EFI) was recently established to address the challenge of assigning reliable functions to enzymes discovered in bacterial genome projects; in this Current Topic, we review the structure and operations of the EFI. The EFI includes the Superfamily/Genome, Protein, Structure, Computation, and Data/Dissemination Cores that provide the infrastructure for reliably predicting the in vitro functions of unknown enzymes. The initial targets for functional assignment are selected from five functionally diverse superfamilies (amidohydrolase, enolase, glutathione transferase, haloalkanoic acid dehalogenase, and isoprenoid synthase), with five superfamily specific Bridging Projects experimentally testing the predicted in vitro enzymatic activities. The EFI also includes the Microbiology Core that evaluates the in vivo context of in vitro enzymatic functions and confirms the functional predictions of the EFI. The deliverables of the EFI to the scientific community include (1) development of a large-scale, multidisciplinary sequence/structure-based strategy for functional assignment of unknown enzymes discovered in genome projects (target selection, protein production, structure determination, computation, experimental enzymology, microbiology, and structure-based annotation), (2) dissemination of the strategy to the community via publications, collaborations, workshops, and symposia, (3) computational and bioinformatic tools for using the strategy, (4) provision of experimental protocols and/or reagents for enzyme production and characterization, and (5) dissemination of data via the EFI's Website, http://enzymefunction.org. The realization of multidisciplinary strategies for functional assignment will begin to define the full metabolic diversity that exists in nature and will impact basic biochemical and evolutionary understanding, as well as a wide range of applications of central importance to industrial, medicinal, and pharmaceutical efforts.