Simultaneous cross-evaluation of heterogeneous E. coli datasets via mechanistic simulation.

The extensive heterogeneity of biological data poses challenges to analysis and interpretation. Construction of a large-scale mechanistic model of Escherichia coli enabled us to integrate and cross-evaluate a massive, heterogeneous dataset based on measurements reported by various groups over decades. We identified inconsistencies with functional consequences across the data, including that the total output of the ribosomes and RNA polymerases described by data are not sufficient for a cell to reproduce measured doubling times, that measured metabolic parameters are neither fully compatible with each other nor with overall growth, and that essential proteins are absent during the cell cycle-and the cell is robust to this absence. Finally, considering these data as a whole leads to successful predictions of new experimental outcomes, in this case protein half-lives.

The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases.

The MetaCyc database (MetaCyc.org) is a freely accessible comprehensive database describing metabolic pathways and enzymes from all domains of life. The majority of MetaCyc pathways are small-molecule metabolic pathways that have been experimentally determined. MetaCyc contains more than 2400 pathways derived from >46,000 publications, and is the largest curated collection of metabolic pathways. BioCyc (BioCyc.org) is a collection of 5700 organism-specific Pathway/Genome Databases (PGDBs), each containing the full genome and predicted metabolic network of one organism, including metabolites, enzymes, reactions, metabolic pathways, predicted operons, transport systems, and pathway-hole fillers. The BioCyc website offers a variety of tools for querying and analyzing PGDBs, including Omics Viewers and tools for comparative analysis. This article provides an update of new developments in MetaCyc and BioCyc during the last two years, including addition of Gibbs free energy values for compounds and reactions; redesign of the primary gene/protein page; addition of a tool for creating diagrams containing multiple linked pathways; several new search capabilities, including searching for genes based on sequence patterns, searching for databases based on an organism's phenotypes, and a cross-organism search; and a metabolite identifier translation service.

An Enlarged Profile of Uremic Solutes.

Better knowledge of the uremic solutes that accumulate when the kidneys fail could lead to improved renal replacement therapy. This study employed the largest widely available metabolomic platform to identify such solutes. Plasma and plasma ultrafiltrate from 6 maintenance hemodialysis (HD) patients and 6 normal controls were first compared using a platform combining gas and liquid chromatography with mass spectrometry. Further studies compared plasma from 6 HD patients who had undergone total colectomy and 9 with intact colons. We identified 120 solutes as uremic including 48 that had not been previously reported to accumulate in renal failure. Combination of the 48 newly identified solutes with those identified in previous reports yielded an extended list of more than 270 uremic solutes. Among the solutes identified as uremic in the current study, 9 were shown to be colon-derived, including 6 not previously identified as such. Literature search revealed that many uremic phenyl and indole solutes, including most of those shown to be colon-derived, come from plant foods. Some of these compounds can be absorbed directly from plant foods and others are produced by colon microbial metabolism of plant polyphenols that escape digestion in the small intestine. A limitation of the metabolomic method was that it underestimated the elevation in concentration of uremic solutes which were measured using more quantitative assays.

A genome-scale metabolic flux model of Escherichia coli K-12 derived from the EcoCyc database.

BACKGROUND: Constraint-based models of Escherichia coli metabolic flux have played a key role in computational studies of cellular metabolism at the genome scale. We sought to develop a next-generation constraint-based E. coli model that achieved improved phenotypic prediction accuracy while being frequently updated and easy to use. We also sought to compare model predictions with experimental data to highlight open questions in E. coli biology. RESULTS: We present EcoCyc-18.0-GEM, a genome-scale model of the E. coli K-12 MG1655 metabolic network. The model is automatically generated from the current state of EcoCyc using the MetaFlux software, enabling the release of multiple model updates per year. EcoCyc-18.0-GEM encompasses 1445 genes, 2286 unique metabolic reactions, and 1453 unique metabolites. We demonstrate a three-part validation of the model that breaks new ground in breadth and accuracy: (i) Comparison of simulated growth in aerobic and anaerobic glucose culture with experimental results from chemostat culture and simulation results from the E. coli modeling literature. (ii) Essentiality prediction for the 1445 genes represented in the model, in which EcoCyc-18.0-GEM achieves an improved accuracy of 95.2% in predicting the growth phenotype of experimental gene knockouts. (iii) Nutrient utilization predictions under 431 different media conditions, for which the model achieves an overall accuracy of 80.7%. The model's derivation from EcoCyc enables query and visualization via the EcoCyc website, facilitating model reuse and validation by inspection. We present an extensive investigation of disagreements between EcoCyc-18.0-GEM predictions and experimental data to highlight areas of interest to E. coli modelers and experimentalists, including 70 incorrect predictions of gene essentiality on glucose, 80 incorrect predictions of gene essentiality on glycerol, and 83 incorrect predictions of nutrient utilization. CONCLUSION: Significant advantages can be derived from the combination of model organism databases and flux balance modeling represented by MetaFlux. Interpretation of the EcoCyc database as a flux balance model results in a highly accurate metabolic model and provides a rigorous consistency check for information stored in the database.

A framework for application of metabolic modeling in yeast to predict the effects of nsSNV in human orthologs.

BACKGROUND: We have previously suggested a method for proteome wide analysis of variation at functional residues wherein we identified the set of all human genes with nonsynonymous single nucleotide variation (nsSNV) in the active site residue of the corresponding proteins. 34 of these proteins were shown to have a 1:1:1 enzyme:pathway:reaction relationship, making these proteins ideal candidates for laboratory validation through creation and observation of specific yeast active site knock-outs and downstream targeted metabolomics experiments. Here we present the next step in the workflow toward using yeast metabolic modeling to predict human metabolic behavior resulting from nsSNV. RESULTS: For the previously identified candidate proteins, we used the reciprocal best BLAST hits method followed by manual alignment and pathway comparison to identify 6 human proteins with yeast orthologs which were suitable for flux balance analysis (FBA). 5 of these proteins are known to be associated with diseases, including ribose 5-phosphate isomerase deficiency, myopathy with lactic acidosis and sideroblastic anaemia, anemia due to disorders of glutathione metabolism, and two porphyrias, and we suspect the sixth enzyme to have disease associations which are not yet classified or understood based on the work described herein. CONCLUSIONS: Preliminary findings using the Yeast 7.0 FBA model show lack of growth for only one enzyme, but augmentation of the Yeast 7.0 biomass function to better simulate knockout of certain genes suggested physiological relevance of variations in three additional proteins. Thus, we suggest the following four proteins for laboratory validation: delta-aminolevulinic acid dehydratase, ferrochelatase, ribose-5 phosphate isomerase and mitochondrial tyrosyl-tRNA synthetase. This study indicates that the predictive ability of this method will improve as more advanced, comprehensive models are developed. Moreover, these findings will be useful in the development of simple downstream biochemical or mass-spectrometric assays to corroborate these predictions and detect presence of certain known nsSNVs with deleterious outcomes. Results may also be useful in predicting as yet unknown outcomes of active site nsSNVs for enzymes that are not yet well classified or annotated.

The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases.

The MetaCyc database (MetaCyc.org) is a comprehensive and freely accessible database describing metabolic pathways and enzymes from all domains of life. MetaCyc pathways are experimentally determined, mostly small-molecule metabolic pathways and are curated from the primary scientific literature. MetaCyc contains >2100 pathways derived from >37,000 publications, and is the largest curated collection of metabolic pathways currently available. BioCyc (BioCyc.org) is a collection of >3000 organism-specific Pathway/Genome Databases (PGDBs), each containing the full genome and predicted metabolic network of one organism, including metabolites, enzymes, reactions, metabolic pathways, predicted operons, transport systems and pathway-hole fillers. Additions to BioCyc over the past 2 years include YeastCyc, a PGDB for Saccharomyces cerevisiae, and 891 new genomes from the Human Microbiome Project. The BioCyc Web site offers a variety of tools for querying and analysis of PGDBs, including Omics Viewers and tools for comparative analysis. New developments include atom mappings in reactions, a new representation of glycan degradation pathways, improved compound structure display, better coverage of enzyme kinetic data, enhancements of the Web Groups functionality, improvements to the Omics viewers, a new representation of the Enzyme Commission system and, for the desktop version of the software, the ability to save display states.

Protein proton-proton dynamics from amide proton spin flip rates.

Residue-specific amide proton spin-flip rates K were measured for peptide-free and peptide-bound calmodulin. K approximates the sum of NOE build-up rates between the amide proton and all other protons. This work outlines the theory of multi-proton relaxation, cross relaxation and cross correlation, and how to approximate it with a simple model based on a variable number of equidistant protons. This model is used to extract the sums of K-rates from the experimental data. Error in K is estimated using bootstrap methodology. We define a parameter Q as the ratio of experimental K-rates to theoretical K-rates, where the theoretical K-rates are computed from atomic coordinates. Q is 1 in the case of no local motion, but decreases to values as low as 0.5 with increasing domination of sidechain protons of the same residue to the amide proton flips. This establishes Q as a monotonous measure of local dynamics of the proton network surrounding the amide protons. The method is applied to the study of proton dynamics in Ca(2+)-saturated calmodulin, both free in solution and bound to smMLCK peptide. The mean Q is 0.81 +/- 0.02 for free calmodulin and 0.88 +/- 0.02 for peptide-bound calmodulin. This novel methodology thus reveals the presence of significant interproton disorder in this protein, while the increase in Q indicates rigidification of the proton network upon peptide binding, confirming the known high entropic cost of this process.

Eta(z)/kappa: a transverse relaxation optimized spectroscopy NMR experiment measuring longitudinal relaxation interference.

NMR spin relaxation experiments provide a powerful tool for the measurement of global and local biomolecular rotational dynamics at subnanosecond time scales. Technical limitations restrict most spin relaxation studies to biomolecules weighing less than 10 kDa, considerably smaller than the average protein molecular weight of 30 kDa. In particular, experiments measuring eta(z), the longitudinal (1)H(N)-(15)N dipole-dipole (DD)/(15)N chemical shift anisotropy (CSA) cross-correlated relaxation rate, are among those least suitable for use with larger biosystems. This is unfortunate because these experiments yield valuable insight into the variability of the (15)N CSA tensor over the polypeptide backbone, and this knowledge is critical to the correct interpretation of most (15)N-NMR backbone relaxation experiments, including R(2) and R(1). In order to remedy this situation, we present a new (1)H(N)-(15)N transverse relaxation optimized spectroscopy experiment measuring eta(z) suitable for applications with larger proteins (up to at least 30 kDa). The presented experiment also yields kappa, the site-specific rate of longitudinal (1)H(N)-(1)H(') DD cross relaxation. We describe the eta(z)/kappa experiment's performance in protonated human ubiquitin at 30.0 degrees C and in protonated calcium-saturated calmodulin/peptide complex at 20.0 degrees C, and demonstrate preliminary experimental results for a deuterated E. coli DnaK ATPase domain construct at 34 degrees C.

Quantifying Lipari-Szabo modelfree parameters from 13CO NMR relaxation experiments.

It is proposed to obtain effective Lipari-Szabo order parameters and local correlation times for relaxation vectors of protein (13)CO nuclei by carrying out a (13)CO-R(1) auto relaxation experiment, a transverse (13)CO CSA/13CO-13Calpha CSA/dipolar cross correlation and a transverse (13)CO CSA/(13)CO-(15)N CSA/dipolar cross correlation experiment. Given the global rotational correlation time from (15)N relaxation experiments, a new program COMFORD (CO-Modelfree Fitting Of Relaxation Data) is presented to fit the (13)CO data to an effective order parameter S2CO, an effective local correlation time and the orientation of the CSA tensor with respect to the molecular frame. It is shown that the effective S2CO is least sensitive to rotational fluctuations about an imaginary Calpha-Calpha axis and most sensitive to rotational fluctuations about an imaginary axis parallel to the NH bond direction. As such, the Calpha-Calpha information is fully complementary to the (15)N relaxation order parameter, which is least sensitive to fluctuations about the NH axis and most sensitive to fluctuations about the Calpha-Calpha axis. The new paradigm is applied on data of Ca(2+) saturated Calmodulin, and on available literature data for Ubiquitin. Our data indicate that the S2CO order parameters rapport on slower, and sometimes different, motions than the (15)N relaxation order parameters. The CO local correlation times correlate well with the calmodulin's secondary structure.