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

Stress-responsively modulated ymdAB-clsC operon plays a role in biofilm formation and apramycin susceptibility in Escherichia coli

The YmdB protein, an inhibitor of biofilm formation and an inducer of apramycin susceptibility in Escherichia coli (E. coli), is part of a putative operon. However, transcription of this operon and its subsequent effects on biological pathways has not been fully studied. Here, we characterized the operon in terms of promoter activity, transcription and function. Promoter activity assays identified two new growth- and cold-shock-responsive upstream (PymdA) and inner (PclsC) promoters, respectively. Moreover, investigation of the operon-derived transcripts identified different polycistronic transcripts harboring multiple heterogeneous 3΄ ends. Overexpression of YmdA or ClsC proteins inhibited biofilm formation and affected apramycin susceptibility, a process dependent on the sucA gene, suggesting that the operon genes or their encoded proteins are functionally linked. Additional investigation of the effects of polycistronic transcripts on the response of E. coli cells to apramycin revealed that transcripts containing ymdA (-213 to +27) are required for apramycin susceptibility. Thus, ymdAB-clsC is a new stress-responsive operon that plays a role in inhibiting undesired biofilm forming and antibiotic-resistant bacterial populations.

Pelagibaca bermudensis promotes biofuel competence of Tetraselmis striata in a broad range of abiotic stressors: dynamics of quorum-sensing precursors and strategic improvement in lipid productivity

BACKGROUND

Amelioration of biofuel feedstock of microalgae using sustainable means through synthetic ecology is a promising strategy. The co-cultivation model (Tetraselmis striata and Pelagibaca bermudensis) was evaluated for the robust biofuel production under varying stressors as well as with the selected two-stage cultivation modes. In addition, the role of metabolic exudates including the quorum-sensing precursors was assessed.

RESULTS

The co-cultivation model innovated in this study supported the biomass production of T. striata in a saline/marine medium at a broad range of pH, salinity, and temperature/light conditions, as well as nutrient limitation with a growth promotion of 1.2-3.6-fold. Hence, this developed model could contribute to abiotic stress mitigation of T. striata. The quorum-sensing precursor dynamics of the growth promoting bacteria P. bermudensis exhibited unique pattern under varying stressors as revealed through targeted metabolomics (using liquid chromatography-mass spectrometry, LC-MS). P. bermudensis and its metabolic exudates mutually promoted the growth of T. striata, which elevated the lipid productivity. Interestingly, hydroxy alkyl quinolones independently showed growth inhibition of T. striata on elevated concentration. Among two-stage cultivation modes (low pH, elevated salinity, and nitrate limitation), specifically, nitrate limitation induced a 1.5 times higher lipid content (30-31%) than control in both axenic and co-cultivated conditions.

CONCLUSION

Pelagibaca bermudensis is established as a potential growth promoting native phycospheric bacteria for robust biomass generation of T. striata in varying environment, and two-stage cultivation using nitrate limitation strategically maximized the biofuel precursors for both axenic and co-cultivation conditions (T and T-PB, respectively). Optimum metabolic exudate of P. bermudensis which act as a growth substrate to T. striata surpasses the antagonistic effect of excessive hydroxy alkyl quinolones [HHQ, 4-hydroxy-2-alkylquinolines and PQS (pseudomonas quorum signal), 2-heptyl-3-hydroxy-4(1H)-quinolone].

In Situ Caging of Biomolecules in Graphene Hybrids for Light Modulated Bioactivity

Remote and noninvasive modulation of protein activity is essential for applications in biotechnology and medicine. Optical control has emerged as the most attractive approach owing to its high spatial and temporal resolutions; however, it is challenging to engineer light responsive proteins. In this work, a near-infrared (NIR) light-responsive graphene-silica-trypsin (GST) nanoreactor is developed for modulating the bioactivity of trypsin molecules. Biomolecules are spatially confined and protected in the rationally designed compartment architecture, which not only reduces the possible interference but also boosts the bioreaction efficiency. Upon NIR irradiation, the photothermal effect of the GST nanoreactor enables the ultrafast in situ heating for remote activation and tuning of the bioactivity. We apply the GST nanoreactor for remote and ultrafast proteolysis of proteins, which remarkably enhances the proteolysis efficiency and reduces the bioreaction time from the overnight of using free trypsin to seconds. We envision that this work not only provides a promising tool of ultrafast and remotely controllable proteolysis for in vivo proteomics in study of tissue microenvironment and other biomedical applications but also paves the way for exploring smart artificial nanoreactors in biomolecular modulation to gain insight in dynamic biological transformation.

Prediction of enzymatic pathways by integrative pathway mapping

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.

Nucleobase deaminases: a potential enzyme system for new therapies

This review presents an overview of the structure, function and mechanism of CDA deaminases and their potential as enzyme systems for development of new antimicrobial therapies.

Stable Isotope-Assisted Metabolic Profiling Reveals Growth Mode Dependent Differential Metabolism and Multiple Catabolic Pathways of l-Phenylalanine in Rubrivivax benzoatilyticus JA2

Anoxygenic phototrophic bacteria are metabolically versatile and survive under different growth modes using diverse organic compounds, yet their metabolic diversity is largely unexplored. In the present study, we employed stable-isotope-assisted metabolic profiling to unravel the l-phenylalanine catabolism in Rubrivivax benzoatilyticus JA2 under varying growth modes. Strain JA2 grows under anaerobic and aerobic conditions by utilizing l-phenylalanine as a nitrogen source. Furthermore, ring-labeled ¹³C₆-phenylalanine feeding followed by liquid chromatography-mass spectrometry exometabolite profiling revealed 60 labeled metabolic features (M + 6, M + 12, and M + 18) derived solely from l-phenylalanine, of which 11 were identified, 7 putatively identified, and 42 unidentified under anaerobic and aerobic conditions. However, labeled metabolites were significantly higher in aerobic compared to anaerobic conditions. Furthermore, detected metabolites and enzyme activities indicated multiple l-phenylalanine catabolic routes mainly Ehrlich, homogentisate-dependent melanin, benzenoid, and unidentified pathways operating under anaerobic and aerobic conditions in strain JA2. Interestingly, the study indicated l-phenylalanine-dependent and independent benzenoid biosynthesis in strain JA2 and a differential flux of l-phenylalanine to Ehrlich and benzenoid pathways under anaerobic and aerobic conditions. Additionally, unidentified labeled metabolites strongly suggest the presence of unknown phenylalanine catabolic routes in strain JA2. Overall, the study uncovered the l-phenylalanine catabolic diversity in strain JA2 and demonstrated the potential of stable isotope-assisted metabolomics in unraveling the hidden metabolic repertoire.

Dissect new mechanistic insights for geniposide efficacy on the hepatoprotection using multiomics approach

A multi-omics approach could yield in-depth mechanistic insights. Here, we performed an integrated analysis of miRNAome, proteome and metabolome, aimed to investigate the underlying mechanism of active product geniposide in ethanol-induced apoptosis. We found that integrative meta-analysis identified 28 miRNAs, 20 proteins and 7 metabolites significantly differentially expressed, respectively. Further analysis identified geniposide extensively regulated multiple metabolism pathways and the most important related pathway was citrate cycle (TCA cycle). In addition, geniposide can improve energy metabolism benefits using the Extracellular Flux Analyzer. Of particular significance, miR-144-5p exhibits a high positive correlation with oxoglutaric acid, isocitrate dehydrogenase (IDH) 1 and 2 that involved in the TCA cycle. Furthermore,we discovered that miR-144-5p regulates TCA cycle metabolism through IDH1 and IDH2. Collectively, we describe for the first time the hepatoprotective effect of geniposide decreased miR-144-5p level, capable of regulating TCA cycle by directly targeting IDH1 and IDH2 and promoting functional consequences in cells. Integrating metabolomics, miRNAomics and proteomics approach and thereby analyzing microRNAs and proteins as well as metabolites can give valuable information about the functional regulation pattern and action mechanism of natural products.

Assessment of genome annotation using gene function similarity within the gene neighborhood

BACKGROUND

Functional annotation of bacterial genomes is an obligatory and crucially important step of information processing from the genome sequences into cellular mechanisms. However, there is a lack of computational methods to evaluate the quality of functional assignments.

RESULTS

We developed a genome-scale model that assigns Bayesian probability to each gene utilizing a known property of functional similarity between neighboring genes in bacteria.

CONCLUSIONS

Our model clearly distinguished true annotation from random annotation with Bayesian annotation probability >0.95. Our model will provide a useful guide to quantitatively evaluate functional annotation methods and to detect gene sets with reliable annotations.

Functional characterization of aconitase X as a cis-3-hydroxy-L-proline dehydratase

In the aconitase superfamily, which includes the archetypical aconitase, homoaconitase, and isopropylmalate isomerase, only aconitase X is not functionally annotated. The corresponding gene (LhpI) was often located within the bacterial gene cluster involved in L-hydroxyproline metabolism. Screening of a library of (hydroxy)proline analogues revealed that this protein catalyzes the dehydration of cis-3-hydroxy-L-proline to Δ¹-pyrroline-2-carboxylate. Furthermore, electron paramagnetic resonance and site-directed mutagenic analyses suggests the presence of a mononuclear Fe(III) center, which may be coordinated with one glutamate and two cysteine residues. These properties were significantly different from those of other aconitase members, which catalyze the isomerization of α- to β-hydroxy acids, and have a [4Fe-4S] cluster-binding site composed of three cysteine residues. Bacteria with the LhpI gene could degrade cis-3-hydroxy-L-proline as the sole carbon source, and LhpI transcription was up-regulated not only by cis-3-hydroxy-L-proline, but also by several isomeric 3- and 4-hydroxyprolines.

Structure to function of an α-glucan metabolic pathway that promotes Listeria monocytogenes pathogenesis

Here we employ a 'systems structural biology' approach to functionally characterize an unconventional α-glucan metabolic pathway from the food-borne pathogen Listeria monocytogenes (Lm). Crystal structure determination coupled with basic biochemical and biophysical assays allowed for the identification of anabolic, transport, catabolic and regulatory portions of the cycloalternan pathway. These findings provide numerous insights into cycloalternan pathway function and reveal the mechanism of repressor, open reading frame, kinase (ROK) transcription regulators. Moreover, by developing a structural overview we were able to anticipate the cycloalternan pathway's role in the metabolism of partially hydrolysed starch derivatives and demonstrate its involvement in Lm pathogenesis. These findings suggest that the cycloalternan pathway plays a role in interspecies resource competition-potentially within the host gastrointestinal tract-and establish the methodological framework for characterizing bacterial systems of unknown function.

Exploring and Understanding the Biochemical Diversity of the Human Microbiota

Recent studies have illuminated a remarkable diversity and abundance of microbes living on and within the human body. While we are beginning to appreciate associations of certain bacteria and genes with particular host physiological states, considerable information is lacking about the relevant functional activities of the human microbiota. The human gut microbiome encodes tremendous potential for the biosynthesis and transformation of compounds that are important for both microbial and host physiology. Implementation of chemical knowledge and techniques will be required to improve our understanding of the biochemical diversity of the human microbiota. Such efforts include the characterization of novel microbial enzymes and pathways, isolation of microbial natural products, and development of tools to modulate biochemical functions of the gut microbiota. Ultimately, a molecular understanding of gut microbial activities will be critical for elucidating and manipulating these organisms' contributions to human health and disease.

Bioinformatics for the synthetic biology of natural products: integrating across the Design-Build-Test cycle

Covering: 2000 to 2016Progress in synthetic biology is enabled by powerful bioinformatics tools allowing the integration of the design, build and test stages of the biological engineering cycle. In this review we illustrate how this integration can be achieved, with a particular focus on natural products discovery and production. Bioinformatics tools for the DESIGN and BUILD stages include tools for the selection, synthesis, assembly and optimization of parts (enzymes and regulatory elements), devices (pathways) and systems (chassis). TEST tools include those for screening, identification and quantification of metabolites for rapid prototyping. The main advantages and limitations of these tools as well as their interoperability capabilities are highlighted.

Docking Screens for Novel Ligands Conferring New Biology

It is now plausible to dock libraries of 10 million molecules against targets over several days or weeks. When the molecules screened are commercially available, they may be rapidly tested to find new leads. Although docking retains important liabilities (it cannot calculate affinities accurately nor even reliably rank order high-scoring molecules), it can often can distinguish likely from unlikely ligands, often with hit rates above 10%. Here we summarize the improvements in libraries, target quality, and methods that have supported these advances, and the open access resources that make docking accessible. Recent docking screens for new ligands are sketched, as are the binding, crystallographic, and in vivo assays that support them. Like any technique, controls are crucial, and key experimental ones are reviewed. With such controls, docking campaigns can find ligands with new chemotypes, often revealing the new biology that may be docking's greatest impact over the next few years.

L-Hydroxyproline and d-Proline Catabolism in Sinorhizobium meliloti

Sinorhizobium meliloti forms N2-fixing root nodules on alfalfa, and as a free-living bacterium, it can grow on a very broad range of substrates, including l-proline and several related compounds, such as proline betaine, trans-4-hydroxy-l-proline (trans-4-l-Hyp), and cis-4-hydroxy-d-proline (cis-4-d-Hyp). Fourteen hyp genes are induced upon growth of S. meliloti on trans-4-l-Hyp, and of those, hypMNPQ encodes an ABC-type trans-4-l-Hyp transporter and hypRE encodes an epimerase that converts trans-4-l-Hyp to cis-4-d-Hyp in the bacterial cytoplasm. Here, we present evidence that the HypO, HypD, and HypH proteins catalyze the remaining steps in which cis-4-d-Hyp is converted to α-ketoglutarate. The HypO protein functions as a d-amino acid dehydrogenase, converting cis-4-d-Hyp to Δ(1)-pyrroline-4-hydroxy-2-carboxylate, which is deaminated by HypD to α-ketoglutarate semialdehyde and then converted to α-ketoglutarate by HypH. The crystal structure of HypD revealed it to be a member of the N-acetylneuraminate lyase subfamily of the (α/β)8 protein family and is consistent with the known enzymatic mechanism for other members of the group. It was also shown that S. meliloti can catabolize d-proline as both a carbon and a nitrogen source, that d-proline can complement l-proline auxotrophy, and that the catabolism of d-proline is dependent on the hyp cluster. Transport of d-proline involves the HypMNPQ transporter, following which d-proline is converted to Δ(1)-pyrroline-2-carboxylate (P2C) largely via HypO. The P2C is converted to l-proline through the NADPH-dependent reduction of P2C by the previously uncharacterized HypS protein. Thus, overall, we have now completed detailed genetic and/or biochemical characterization of 9 of the 14 hyp genes.

IMPORTANCE

Hydroxyproline is abundant in proteins in animal and plant tissues and serves as a carbon and a nitrogen source for bacteria in diverse environments, including the rhizosphere, compost, and the mammalian gut. While the main biochemical features of bacterial hydroxyproline catabolism were elucidated in the 1960s, the genetic and molecular details have only recently been determined. Elucidating the genetics of hydroxyproline catabolism will aid in the annotation of these genes in other genomes and metagenomic libraries. This will facilitate an improved understanding of the importance of this pathway and may assist in determining the prevalence of hydroxyproline in a particular environment.

Asymmetric reductive amination by a wild-type amine dehydrogenase from the thermophilic bacteria Petrotoga mobilis

Biocatalytic potential of a new wild-type amine dehydrogenase used in an enzyme-catalyzed synthesis of an enantiomerically pure primary amine.

De novo assembly and functional annotation of Myrciaria dubia fruit transcriptome reveals multiple metabolic pathways for L-ascorbic acid biosynthesis

Background

Myrciaria dubia is an Amazonian fruit shrub that produces numerous bioactive phytochemicals, but is best known by its high L-ascorbic acid (AsA) content in fruits. Pronounced variation in AsA content has been observed both within and among individuals, but the genetic factors responsible for this variation are largely unknown. The goals of this research, therefore, were to assemble, characterize, and annotate the fruit transcriptome of M. dubia in order to reconstruct metabolic pathways and determine if multiple pathways contribute to AsA biosynthesis.

Results

In total 24,551,882 high-quality sequence reads were de novo assembled into 70,048 unigenes (mean length = 1150 bp, N50 = 1775 bp). Assembled sequences were annotated using BLASTX against public databases such as TAIR, GR-protein, FB, MGI, RGD, ZFIN, SGN, WB, TIGR_CMR, and JCVI-CMR with 75.2 % of unigenes having annotations. Of the three core GO annotation categories, biological processes comprised 53.6 % of the total assigned annotations, whereas cellular components and molecular functions comprised 23.3 and 23.1 %, respectively. Based on the KEGG pathway assignment of the functionally annotated transcripts, five metabolic pathways for AsA biosynthesis were identified: animal-like pathway, myo-inositol pathway, L-gulose pathway, D-mannose/L-galactose pathway, and uronic acid pathway. All transcripts coding enzymes involved in the ascorbate-glutathione cycle were also identified. Finally, we used the assembly to identified 6314 genic microsatellites and 23,481 high quality SNPs.

Conclusions

This study describes the first next-generation sequencing effort and transcriptome annotation of a non-model Amazonian plant that is relevant for AsA production and other bioactive phytochemicals. Genes encoding key enzymes were successfully identified and metabolic pathways involved in biosynthesis of AsA, anthocyanins, and other metabolic pathways have been reconstructed. The identification of these genes and pathways is in agreement with the empirically observed capability of M. dubia to synthesize and accumulate AsA and other important molecules, and adds to our current knowledge of the molecular biology and biochemistry of their production in plants. By providing insights into the mechanisms underpinning these metabolic processes, these results can be used to direct efforts to genetically manipulate this organism in order to enhance the production of these bioactive phytochemicals.

The accumulation of AsA precursor and discovery of genes associated with their biosynthesis and metabolism in M. dubia is intriguing and worthy of further investigation. The sequences and pathways produced here present the genetic framework required for further studies. Quantitative transcriptomics in concert with studies of the genome, proteome, and metabolome under conditions that stimulate production and accumulation of AsA and their precursors are needed to provide a more comprehensive view of how these pathways for AsA metabolism are regulated and linked in this species.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2225-6) contains supplementary material, which is available to authorized users.

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.

Functional genomics of tomato: opportunities and challenges in post-genome NGS era

The Tomato Genome Sequencing Project represented a landmark venture in the history of sequencing projects where both Sanger's and next-generation sequencing (NGS) technologies were employed, and a highly accurate and one of the best assembled plant genomes along with a draft of the wild relative, Solanum pimpinellifolium, were released in 2012. However, the functional potential of the major portion of this newly generated resource is still undefined. The very first challenge before scientists working on tomato functional biology is to exploit this high-quality reference sequence for tapping of the wealth of genetic variants for improving agronomic traits in cultivated tomatoes. The sequence data generated recently by 150 Tomato Genome Consortium would further uncover the natural alleles present in different tomato genotypes. Therefore, we found it relevant to have a fresh outlook on tomato functional genomics in the context of application of NGS technologies in its post-genome sequencing phase. Herein, we provide an overview how NGS technologies vis-a-vis available reference sequence have assisted each other for their mutual improvement and how their combined use could further facilitate the development of other 'omics' tools, required to propel the Solanaceae research. Additionally, we highlight the challenges associated with the application of these cutting-edge technologies.

PINGU: PredIction of eNzyme catalytic residues usinG seqUence information

Identification of catalytic residues can help unveil interesting attributes of enzyme function for various therapeutic and industrial applications. Based on their biochemical roles, the number of catalytic residues and sequence lengths of enzymes vary. This article describes a prediction approach (PINGU) for such a scenario. It uses models trained using physicochemical properties and evolutionary information of 650 non-redundant enzymes (2136 catalytic residues) in a support vector machines architecture. Independent testing on 200 non-redundant enzymes (683 catalytic residues) in predefined prediction settings, i.e., with non-catalytic per catalytic residue ranging from 1 to 30, suggested that the prediction approach was highly sensitive and specific, i.e., 80% or above, over the incremental challenges. To learn more about the discriminatory power of PINGU in real scenarios, where the prediction challenge is variable and susceptible to high false positives, the best model from independent testing was used on 60 diverse enzymes. Results suggested that PINGU was able to identify most catalytic residues and non-catalytic residues properly with 80% or above accuracy, sensitivity and specificity. The effect of false positives on precision was addressed in this study by application of predicted ligand-binding residue information as a post-processing filter. An overall improvement of 20% in F-measure and 0.138 in Correlation Coefficient with 16% enhanced precision could be achieved. On account of its encouraging performance, PINGU is hoped to have eventual applications in boosting enzyme engineering and novel drug discovery.

Oxidative cyclizations in orthosomycin biosynthesis expand the known chemistry of an oxygenase superfamily

Orthosomycins are oligosaccharide antibiotics that include avilamycin, everninomicin, and hygromycin B and are hallmarked by a rigidifying interglycosidic spirocyclic ortho-δ-lactone (orthoester) linkage between at least one pair of carbohydrates. A subset of orthosomycins additionally contain a carbohydrate capped by a methylenedioxy bridge. The orthoester linkage is necessary for antibiotic activity but rarely observed in natural products. Orthoester linkage and methylenedioxy bridge biosynthesis require similar oxidative cyclizations adjacent to a sugar ring. We have identified a conserved group of nonheme iron, α-ketoglutarate-dependent oxygenases likely responsible for this chemistry. High-resolution crystal structures of the EvdO1 and EvdO2 oxygenases of everninomicin biosynthesis, the AviO1 oxygenase of avilamycin biosynthesis, and HygX of hygromycin B biosynthesis show how these enzymes accommodate large substrates, a challenge that requires a variation in metal coordination in HygX. Excitingly, the ternary complex of HygX with cosubstrate α-ketoglutarate and putative product hygromycin B identified an orientation of one glycosidic linkage of hygromycin B consistent with metal-catalyzed hydrogen atom abstraction from substrate. These structural results are complemented by gene disruption of the oxygenases evdO1 and evdMO1 from the everninomicin biosynthetic cluster, which demonstrate that functional oxygenase activity is critical for antibiotic production. Our data therefore support a role for these enzymes in the production of key features of the orthosomycin antibiotics.

Structure-based virtual screening for drug discovery: principles, applications and recent advances

Structure-based drug discovery (SBDD) is becoming an essential tool in assisting fast and cost-efficient lead discovery and optimization. The application of rational, structure-based drug design is proven to be more efficient than the traditional way of drug discovery since it aims to understand the molecular basis of a disease and utilizes the knowledge of the three-dimensional structure of the biological target in the process. In this review, we focus on the principles and applications of Virtual Screening (VS) within the context of SBDD and examine different procedures ranging from the initial stages of the process that include receptor and library pre-processing, to docking, scoring and post-processing of topscoring hits. Recent improvements in structure-based virtual screening (SBVS) efficiency through ensemble docking, induced fit and consensus docking are also discussed. The review highlights advances in the field within the framework of several success studies that have led to nM inhibition directly from VS and provides recent trends in library design as well as discusses limitations of the method. Applications of SBVS in the design of substrates for engineered proteins that enable the discovery of new metabolic and signal transduction pathways and the design of inhibitors of multifunctional proteins are also reviewed. Finally, we contribute two promising VS protocols recently developed by us that aim to increase inhibitor selectivity. In the first protocol, we describe the discovery of micromolar inhibitors through SBVS designed to inhibit the mutant H1047R PI3Kα kinase. Second, we discuss a strategy for the identification of selective binders for the RXRα nuclear receptor. In this protocol, a set of target structures is constructed for ensemble docking based on binding site shape characterization and clustering, aiming to enhance the hit rate of selective inhibitors for the desired protein target through the SBVS process.

Paracoccus denitrificans possesses two BioR homologs having a role in regulation of biotin metabolism

Recently, we determined that BioR, the GntR family of transcription factor, acts as a repressor for biotin metabolism exclusively distributed in certain species of α-proteobacteria, including the zoonotic agent Brucella melitensis and the plant pathogen Agrobacterium tumefaciens. However, the scenario is unusual in Paracoccus denitrificans, another closely related member of the same phylum α-proteobacteria featuring with denitrification. Not only does it encode two BioR homologs Pden_1431 and Pden_2922 (designated as BioR1 and BioR2, respectively), but also has six predictive BioR-recognizable sites (the two bioR homolog each has one site, whereas the two bio operons (bioBFDAGC and bioYB) each contains two tandem BioR boxes). It raised the possibility that unexpected complexity is present in BioR-mediated biotin regulation. Here we report that this is the case. The identity of the purified BioR proteins (BioR1 and BioR2) was confirmed with LC-QToF-MS. Phylogenetic analyses combined with GC percentage raised a possibility that the bioR2 gene might be acquired by horizontal gene transfer. Gel shift assays revealed that the predicted BioR-binding sites are functional for the two BioR homologs, in much similarity to the scenario seen with the BioR site of A. tumefaciens bioBFDAZ. Using the A. tumefaciens reporter system carrying a plasmid-borne LacZ fusion, we revealed that the two homologs of P. denitrificans BioR are functional repressors for biotin metabolism. As anticipated, not only does the addition of exogenous biotin stimulate efficiently the expression of bioYB operon encoding biotin transport/uptake system BioY, but also inhibits the transcription of the bioBFDAGC operon resembling the de novo biotin synthetic pathway. EMSA-based screening failed to demonstrate that the biotin-related metabolite is involved in BioR-DNA interplay, which is consistent with our former observation with Brucella BioR. Our finding defined a complex regulatory network for biotin metabolism in P. denitrificans by two BioR proteins.

Inactivation of Mandelate Racemase by 3-Hydroxypyruvate Reveals a Potential Mechanistic Link between Enzyme Superfamilies

Mandelate racemase (MR), a member of the enolase superfamily, catalyzes the Mg(2+)-dependent interconversion of the enantiomers of mandelate. Several α-keto acids are modest competitive inhibitors of MR [e.g., mesoxalate (Ki = 1.8 ± 0.3 mM) and 3-fluoropyruvate (Ki = 1.3 ± 0.1 mM)], but, surprisingly, 3-hydroxypyruvate (3-HP) is an irreversible, time-dependent inhibitor (kinact/KI = 83 ± 8 M(-1) s(-1)). Protection from inactivation by the competitive inhibitor benzohydroxamate, trypsinolysis and electrospray ionization tandem mass spectrometry analyses, and X-ray crystallographic studies reveal that 3-HP undergoes Schiff-base formation with Lys 166 at the active site, followed by formation of an aldehyde/enol(ate) adduct. Such a reaction is unprecedented in the enolase superfamily and may be a relic of an activity possessed by a promiscuous progenitor enzyme. The ability of MR to form and deprotonate a Schiff-base intermediate furnishes a previously unrecognized mechanistic link to other α/β-barrel enzymes utilizing Schiff-base chemistry and is in accord with the sequence- and structure-based hypothesis that members of the metal-dependent enolase superfamily and the Schiff-base-forming N-acetylneuraminate lyase superfamily and aldolases share a common ancestor.

Enzyme Function Initiative-Enzyme Similarity Tool (EFI-EST): A web tool for generating protein sequence similarity networks

The Enzyme Function Initiative, an NIH/NIGMS-supported Large-Scale Collaborative Project (EFI; U54GM093342; http://enzymefunction.org/), is focused on devising and disseminating bioinformatics and computational tools as well as experimental strategies for the prediction and assignment of functions (in vitro activities and in vivo physiological/metabolic roles) to uncharacterized enzymes discovered in genome projects. Protein sequence similarity networks (SSNs) are visually powerful tools for analyzing sequence relationships in protein families (H.J. Atkinson, J.H. Morris, T.E. Ferrin, and P.C. Babbitt, PLoS One 2009, 4, e4345). However, the members of the biological/biomedical community have not had access to the capability to generate SSNs for their "favorite" protein families. In this article we announce the EFI-EST (Enzyme Function Initiative-Enzyme Similarity Tool) web tool (http://efi.igb.illinois.edu/efi-est/) that is available without cost for the automated generation of SSNs by the community. The tool can create SSNs for the "closest neighbors" of a user-supplied protein sequence from the UniProt database (Option A) or of members of any user-supplied Pfam and/or InterPro family (Option B). We provide an introduction to SSNs, a description of EFI-EST, and a demonstration of the use of EFI-EST to explore sequence-function space in the OMP decarboxylase superfamily (PF00215). This article is designed as a tutorial that will allow members of the community to use the EFI-EST web tool for exploring sequence/function space in protein families.

DomSign: a top-down annotation pipeline to enlarge enzyme space in the protein universe

Background

Computational predictions of catalytic function are vital for in-depth understanding of enzymes. Because several novel approaches performing better than the common BLAST tool are rarely applied in research, we hypothesized that there is a large gap between the number of known annotated enzymes and the actual number in the protein universe, which significantly limits our ability to extract additional biologically relevant functional information from the available sequencing data. To reliably expand the enzyme space, we developed DomSign, a highly accurate domain signature–based enzyme functional prediction tool to assign Enzyme Commission (EC) digits.

Results

DomSign is a top-down prediction engine that yields results comparable, or superior, to those from many benchmark EC number prediction tools, including BLASTP, when a homolog with an identity >30% is not available in the database. Performance tests showed that DomSign is a highly reliable enzyme EC number annotation tool. After multiple tests, the accuracy is thought to be greater than 90%. Thus, DomSign can be applied to large-scale datasets, with the goal of expanding the enzyme space with high fidelity. Using DomSign, we successfully increased the percentage of EC-tagged enzymes from 12% to 30% in UniProt-TrEMBL. In the Kyoto Encyclopedia of Genes and Genomes bacterial database, the percentage of EC-tagged enzymes for each bacterial genome could be increased from 26.0% to 33.2% on average. Metagenomic mining was also efficient, as exemplified by the application of DomSign to the Human Microbiome Project dataset, recovering nearly one million new EC-labeled enzymes.

Conclusions

Our results offer preliminarily confirmation of the existence of the hypothesized huge number of “hidden enzymes” in the protein universe, the identification of which could substantially further our understanding of the metabolisms of diverse organisms and also facilitate bioengineering by providing a richer enzyme resource. Furthermore, our results highlight the necessity of using more advanced computational tools than BLAST in protein database annotations to extract additional biologically relevant functional information from the available biological sequences.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0499-y) contains supplementary material, which is available to authorized users.

Microbial synthesis of plant oxylipins from γ-linolenic acid through designed biotransformation pathways

Secondary metabolites of plants are often difficult to synthesize in high yields because of the large complexity of the biosynthetic pathways and challenges encountered in the functional expression of the required biosynthetic enzymes in microbial cells. In this study, the biosynthesis of plant oxylipins--a family of oxygenated unsaturated carboxylic acids--was explored to enable a high-yield production through a designed microbial synthetic system harboring a set of microbial enzymes (i.e., fatty acid double-bond hydratases, alcohol dehydrogenases, Baeyer-Villiger monooxygenases, and esterases) to produce a variety of unsaturated carboxylic acids from γ-linolenic acid. The whole cell system of the recombinant Escherichia coli efficiently produced (6Z,9Z)-12-hydroxydodeca-6,9-dienoic acid (7), (Z)-9-hydroxynon-6-enoic acid (15), (Z)-dec-4-enedioic acid (17), and (6Z,9Z)-13-hydroxyoctadeca-6,9-dienoic acid (2). This study demonstrated that various secondary metabolites of plants can be produced by implementing artificial biosynthetic pathways into whole-cell biocatalysis.

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.

Bioinformatic analysis of a PLP-dependent enzyme superfamily suitable for biocatalytic applications

In this review we analyse structure/sequence-function relationships for the superfamily of PLP-dependent enzymes with special emphasis on class III transaminases. Amine transaminases are highly important for applications in biocatalysis in the synthesis of chiral amines. In addition, other enzyme activities such as racemases or decarboxylases are also discussed. The substrate scope and the ability to accept chemically different types of substrates are shown to be reflected in conserved patterns of amino acids around the active site. These findings are condensed in a sequence-function matrix, which facilitates annotation and identification of biocatalytically relevant enzymes and protein engineering thereof.

Covalent docking predicts substrates for haloalkanoate dehalogenase superfamily phosphatases

Enzyme function prediction remains an important open problem. Though structure-based modeling, such as metabolite docking, can identify substrates of some enzymes, it is ill-suited to reactions that progress through a covalent intermediate. Here we investigated the ability of covalent docking to identify substrates that pass through such a covalent intermediate, focusing particularly on the haloalkanoate dehalogenase superfamily. In retrospective assessments, covalent docking recapitulated substrate binding modes of known cocrystal structures and identified experimental substrates from a set of putative phosphorylated metabolites. In comparison, noncovalent docking of high-energy intermediates yielded nonproductive poses. In prospective predictions against seven enzymes, a substrate was identified for five. For one of those cases, a covalent docking prediction, confirmed by empirical screening, and combined with genomic context analysis, suggested the identity of the enzyme that catalyzes the orphan phosphatase reaction in the riboflavin biosynthetic pathway of Bacteroides.

Development of bio-based fine chemical production through synthetic bioengineering

Fine chemicals that are physiologically active, such as pharmaceuticals, cosmetics, nutritional supplements, flavoring agents as well as additives for foods, feed, and fertilizer are produced by enzymatically or through microbial fermentation. The identification of enzymes that catalyze the target reaction makes possible the enzymatic synthesis of the desired fine chemical. The genes encoding these enzymes are then introduced into suitable microbial hosts that are cultured with inexpensive, naturally abundant carbon sources, and other nutrients. Metabolic engineering create efficient microbial cell factories for producing chemicals at higher yields. Molecular genetic techniques are then used to optimize metabolic pathways of genetically and metabolically well-characterized hosts. Synthetic bioengineering represents a novel approach to employ a combination of computer simulation and metabolic analysis to design artificial metabolic pathways suitable for mass production of target chemicals in host strains. In the present review, we summarize recent studies on bio-based fine chemical production and assess the potential of synthetic bioengineering for further improving their productivity.

A distinct MaoC-like enoyl-CoA hydratase architecture mediates cholesterol catabolism in Mycobacterium tuberculosis

The Mycobacterium tuberculosis (Mtb) igr operon plays an essential role in Mtb cholesterol metabolism, which is critical for pathogenesis during the latent stage of Mtb infection. Here we report the first structure of a heterotetrameric MaoC-like enoyl-CoA hydratase, ChsH1-ChsH2, which is encoded by two adjacent genes from the igr operon. We demonstrate that ChsH1-ChsH2 catalyzes the hydration of a steroid enoyl-CoA, 3-oxo-4,17-pregnadiene-20-carboxyl-CoA, in the modified β-oxidation pathway for cholesterol side chain degradation. The ligand-bound and apoenzyme structures of ChsH1-ChsH2^N reveal an unusual, modified hot-dog fold with a severely truncated central α-helix that creates an expanded binding site to accommodate the bulkier steroid ring system. The structures show quaternary structure shifts that accommodate the four rings of the steroid substrate and offer an explanation for why the unusual heterotetrameric assembly is utilized for hydration of this steroid. The unique αβ heterodimer architecture utilized by ChsH1-ChsH2 to bind its distinctive substrate highlights an opportunity for the development of new antimycobacterial drugs that target a pathway specific to Mtb.

An atypical orphan carbohydrate-NRPS genomic island encodes a novel lytic transglycosylase

Microbial genome sequencing platforms have produced a deluge of orphan biosynthetic pathways suspected of biosynthesizing new small molecules with pharmacological relevance. Genome synteny analysis provides an assessment of genomic island content, which is enriched in natural product gene clusters. Here we identified an atypical orphan carbohydrate-nonribosomal peptide synthetase genomic island in Photorhabdus luminescens using genome synteny analysis. Heterologous expression of the pathway led to the characterization of five oligosaccharide metabolites with lysozyme inhibitory activities. The oligosaccharides harbor a 1,6-anhydro-β-D-N-acetyl-glucosamine moiety, a rare structural feature for natural products. Gene deletion analysis and biochemical reconstruction of oligosaccharide production led to the discovery that a hypothetical protein in the pathway is a lytic transglycosylase responsible for bicyclic sugar formation. The example presented here supports the notion that targeting select genomic islands with reduced reliance on known protein homologies could enhance the discovery of new metabolic chemistry and biology.

Predicting the functions and specificity of triterpenoid synthases: a mechanism-based multi-intermediate docking approach

Terpenoid synthases construct the carbon skeletons of tens of thousands of natural products. To predict functions and specificity of triterpenoid synthases, a mechanism-based, multi-intermediate docking approach is proposed. In addition to enzyme function prediction, other potential applications of the current approach, such as enzyme mechanistic studies and enzyme redesign by mutagenesis, are discussed.

The rapid growth in the number of protein sequences presents challenges for enzyme function assignment. Computational methods, such as bioinformatics, homology modeling and docking, are becoming increasingly important for predicting of enzyme functions from protein sequences. Terpenoids are one of largest classes of natural products, and many drugs (e.g. taxol) consist of terpenoids or terpenoid derivatives. Understanding the biosynthesis of the terpenoids is of great interest. Terpenoid synthases catalyze the key cyclization steps of the biosynthesis of terpenoids via carbocation rearrangements, generating numerous multiple-ring carbon skeletons. Triterpenoid synthases, as an important class of terpenoid synthases, catalyze the cyclization of either squalene or oxido-squalene into cyclized products such as sterols (e.g. lanosterol). In this work, we propose a computational approach that can be used to predict product specificity of the triterpenoid synthases. Our approach provides insight into the ‘design principles’ of these fascinating enzymes, and may become a practical approach for function prediction and enzyme engineering.

Integration of untargeted metabolomics with transcriptomics reveals active metabolic pathways

While recent advances in metabolomic measurement technologies have been dramatic, extracting biological insight from complex metabolite profiles remains a challenge. We present an analytical strategy that uses data obtained from high resolution liquid chromatography-mass spectrometry and a bioinformatics toolset for detecting actively changing metabolic pathways upon external perturbation. We begin with untargeted metabolite profiling to nominate altered metabolites and identify pathway candidates, followed by validation of those pathways with transcriptomics. Using the model organisms Rhodospirillum rubrum and Bacillus subtilis, our results reveal metabolic pathways that are interconnected with methionine salvage. The rubrum-type methionine salvage pathway is interconnected with the active methyl cycle in which re-methylation, a key reaction for recycling methionine from homocysteine, is unexpectedly suppressed; instead, homocysteine is catabolized by the transsulfuration pathway. Notably, the non-mevalonate pathway is repressed, whereas the rubrum-type methionine salvage pathway contributes to isoprenoid biosynthesis upon 5'-methylthioadenosine feeding. In this process, glutathione functions as a coenzyme in vivo when 1-methylthio-d-xylulose 5-phosphate (MTXu 5-P) methylsulfurylase catalyzes dethiomethylation of MTXu 5-P. These results clearly show that our analytical approach enables unexpected metabolic pathways to be uncovered.

A computational approach to enzyme design: predicting ω-aminotransferase catalytic activity using docking and MM-GBSA scoring

Enzyme design is an important area of ongoing research with a broad range of applications in protein therapeutics, biocatalysis, bioengineering, and other biomedical areas; however, significant challenges exist in the design of enzymes to catalyze specific reactions of interest. Here, we develop a computational protocol using an approach that combines molecular dynamics, docking, and MM-GBSA scoring to predict the catalytic activity of enzyme variants. Our primary focuses are to understand the molecular basis of substrate recognition and binding in an S-stereoselective ω-aminotransferase (ω-AT), which naturally catalyzes the transamination of pyruvate into alanine, and to predict mutations that enhance the catalytic efficiency of the enzyme. The conversion of (R)-ethyl 5-methyl-3-oxooctanoate to (3S,5R)-ethyl 3-amino-5-methyloctanoate in the context of several ω-AT mutants was evaluated using the computational protocol developed in this work. We correctly identify the mutations that yield the greatest improvements in enzyme activity (20-60-fold improvement over wild type) and confirm that the computationally predicted structure of a highly active mutant reproduces key structural aspects of the variant, including side chain conformational changes, as determined by X-ray crystallography. Overall, the protocol developed here yields encouraging results and suggests that computational approaches can aid in the redesign of enzymes with improved catalytic efficiency.

Plant-derived compatible solutes proline betaine and betonicine confer enhanced osmotic and temperature stress tolerance to Bacillus subtilis

L-Proline is a widely used compatible solute and is employed by Bacillus subtilis, through both synthesis and uptake, as an osmostress protectant. Here, we assessed the stress-protective potential of the plant-derived L-proline derivatives N-methyl-L-proline, L-proline betaine (stachydrine), trans-4-L-hydroxproline and trans-4-hydroxy-L-proline betaine (betonicine) for cells challenged by high salinity or extremes in growth temperature. l-Proline betaine and betonicine conferred salt stress protection, but trans-4-L-hydroxyproline and N-methyl-L-proline was unable to do so. Except for L-proline, none of these compounds served as a nutrient for B. subtilis. L-Proline betaine was a considerably better osmostress protectant than betonicine, and its import strongly reduced the l-proline pool produced by B. subtilis under osmotic stress conditions, whereas a supply of betonicine affected the L-proline pool only modestly. Both compounds downregulated the transcription of the osmotically inducible opuA operon, albeit to different extents. Mutant studies revealed that L-proline betaine was taken up via the ATP-binding cassette transporters OpuA and OpuC, and the betaine-choline-carnitine-transporter-type carrier OpuD; betonicine was imported only through OpuA and OpuC. L-Proline betaine and betonicine also served as temperature stress protectants. A striking difference between these chemically closely related compounds was observed: L-proline betaine was an excellent cold stress protectant, but did not provide heat stress protection, whereas the reverse was true for betonicine. Both compounds were primarily imported in temperature-challenged cells via the high-capacity OpuA transporter. We developed an in silico model for the OpuAC-betonicine complex based on the crystal structure of the OpuAC solute receptor complexed with L-proline betaine.

The evolution of enzyme function in the isomerases

The advent of computational approaches to measure functional similarity between enzymes adds a new dimension to existing evolutionary studies based on sequence and structure. This paper reviews research efforts aiming to understand the evolution of enzyme function in superfamilies, presenting a novel strategy to provide an overview of the evolution of enzymes belonging to an individual EC class, using the isomerases as an exemplar.

Leveraging structure for enzyme function prediction: methods, opportunities, and challenges

The rapid growth of the number of protein sequences that can be inferred from sequenced genomes presents challenges for function assignment, because only a small fraction (currently <1%) has been experimentally characterized. Bioinformatics tools are commonly used to predict functions of uncharacterized proteins. Recently, there has been significant progress in using protein structures as an additional source of information to infer aspects of enzyme function, which is the focus of this review. Successful application of these approaches has led to the identification of novel metabolites, enzyme activities, and biochemical pathways. We discuss opportunities to elucidate systematically protein domains of unknown function, orphan enzyme activities, dead-end metabolites, and pathways in secondary metabolism.

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

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.

DOI:http://dx.doi.org/10.7554/eLife.03275.002