Predicting genes for orphan metabolic activities using phylogenetic profiles

Homology-based methods fail to assign genes to many metabolic activities present in sequenced organisms. To suggest genes for these orphan activities we developed a novel method that efficiently combines local structure of a metabolic network with phylogenetic profiles. We validated our method using known metabolic genes in Saccharomyces cerevisiae and Escherichia coli. We show that our method should be easily transferable to other organisms, and that it is robust to errors in incomplete metabolic networks

Influence of metabolic network structure and function on enzyme evolution

BACKGROUND: Most studies of molecular evolution are focused on individual genes and proteins. However, understanding the design principles and evolutionary properties of molecular networks requires a system-wide perspective. In the present work we connect molecular evolution on the gene level with system properties of a cellular metabolic network. In contrast to protein interaction networks, where several previous studies investigated the molecular evolution of proteins, metabolic networks have a relatively well-defined global function. The ability to consider fluxes in a metabolic network allows us to relate the functional role of each enzyme in a network to its rate of evolution. RESULTS: Our results, based on the yeast metabolic network, demonstrate that important evolutionary processes, such as the fixation of single nucleotide mutations, gene duplications, and gene deletions, are influenced by the structure and function of the network. Specifically, central and highly connected enzymes evolve more slowly than less connected enzymes. Also, enzymes carrying high metabolic fluxes under natural biological conditions experience higher evolutionary constraints. Genes encoding enzymes with high connectivity and high metabolic flux have higher chances to retain duplicates in evolution. In contrast to protein interaction networks, highly connected enzymes are no more likely to be essential compared to less connected enzymes. CONCLUSION: The presented analysis of evolutionary constraints, gene duplication, and essentiality demonstrates that the structure and function of a metabolic network shapes the evolution of its enzymes. Our results underscore the need for systems-based approaches in studies of molecular evolution

Computational prediction and experimental verification of the gene encoding the NAD � /NADP � - dependent succinate semialdehyde dehydrogenase in Escherichia coli

Although NAD �-dependent succinate semialdehyde dehydrogenase activity was first described in Escherichia coli more than 25 years ago, the responsible gene has remained elusive so far. As an experimental proof of concept for a gap-filling algorithm for metabolic networks developed earlier, we demonstrate here that the E. coli gene yneI is responsible for this activity. Our biochemical results demonstrate that the yneI-encoded succinate semialdehyde dehydrogenase can use either NAD � or NADP � to oxidize succinate semialdehyde to succinate. The gene is induced by succinate semialdehyde, and expression data indicate that yneI plays a unique physiological role in the general nitrogen metabolism of E. coli. In particular, we demonstrate using mutant growth experiments that the yneI gene has an important, but not essential, role during growth on arginine and probably has an essential function during growth on putrescine as the nitrogen source. The NADP �-dependent succinate semialdehyde dehydrogenase activity encoded by the functional homolog gabD appears to be important for nitrogen metabolism under N limitation conditions. The yneI-encoded activity, in contrast, functions primarily as a valve to prevent toxic accumulation of succinate semialdehyde. Analysis of available genome sequences demonstrated that orthologs of both yneI and gabD are broadly distributed across phylogenetic space. In spite of extensive biochemical research, metabolic network

The rate of the molecular clock and the cost of gratuitous protein synthesis

The nature of the protein molecular clock, the protein-specific rate of amino acid substitutions, is among the central questions of molecular evolution. Protein expression level is the dominant determinant of the clock rate in a number of organisms. It has been suggested that highly expressed proteins evolve slowly in all species mainly to maintain robustness to translation errors that generate toxic misfolded proteins. Here we investigate this hypothesis experimentally by comparing the growth rate of Escherichia coli expressing wild type and misfolding-prone variants of the LacZ protein. We show that the cost of toxic protein misfolding is small compared to other costs associated with protein synthesis. Complementary computational analyses demonstrate that there is also a relatively weaker, but statistically significant, selection for increasing solubility and polarity in highly expressed E. coli proteins. Although we cannot rule out the possibility that selection against misfolding toxicity significantly affects the protein clock in species other than E. coli, our results suggest that it is unlikely to be the dominant and universal factor determining the clock rate in all organisms. We find that in this bacterium other costs associated with protein synthesis are likely to play an important role. Interestingly, our experiments also suggest significant costs associated with volume effects, such as jamming of the cellular environment with unnecessary proteins

The Amino-Acid Mutational Spectrum of Human Genetic Disease

Background: Nonsynonymous mutations in the coding regions of human genes are responsible for phenotypic differences between humans and for susceptibility to genetic disease. Computational methods were recently used to predict deleterious effects of nonsynonymous human mutations and polymorphisms. Here we focus on understanding the amino-acid mutation spectrum of human genetic disease. We compare the disease spectrum to the spectra of mutual amino-acid mutation frequencies, non-disease polymorphisms in human genes, and substitutions fixed between species. Results: We find that the disease spectrum correlates well with the amino-acid mutation frequencies based on the genetic code. Normalized by the mutation frequencies, the spectrum can be rationalized in terms of chemical similarities between amino acids. The disease spectrum is almost identical for membrane and non-membrane proteins. Mutations at arginine and glycine residues are together responsible for about 30% of genetic diseases, whereas random mutations at tryptophan and cysteine have the highest probability of causing disease. Conclusions: The overall disease spectrum mainly reflects the mutability of the genetic code. We corroborate earlier results that the probability of a nonsynonymous mutation causing a genetic disease increases monotonically with an increase in the degree of evolutionary conservation of the mutation site and a decrease in the solvent-accessibility of the site; opposite trends are observed for non-disease polymorphisms. We estimate that the rate of nonsynonymous mutations with a negative impact on human health is less than one per diploid genome per generation

Ensayo rápido para la determinación de la velocidad de endurecimiento del cemento

Not availableLos ensayos acelerados de determinación de la velocidad de endurecimiento del cemento, mediante curado con vapor, son generalmente poco satisfactorios, debido a que los cementos de composición mineralógica diferente reaccionan de distinto modo, con el resultado de que la resistencia inicial de las probetas curadas por vapor no puede utilizarse como un criterio seguro de su resistencia a edades posteriores

International Summer School, ‘ From Genome to Life’

This report from the International Summer School ‘From Genome to Life’, held at the Institute d'Etudes Scientifiques de Cargèse in Corsica in July 2002, covers the talks of the invited speakers. The topics of the talks can be broadly grouped into the areas of genome annotation, comparative and evolutionary genomics, functional genomics, proteomics, structural genomics, pharmacogenomics, and organelle genomes, epigenetics and RNA

Identifying metabolic enzymes with multiple types of association evidence

BACKGROUND: Existing large-scale metabolic models of sequenced organisms commonly include enzymatic functions which can not be attributed to any gene in that organism. Existing computational strategies for identifying such missing genes rely primarily on sequence homology to known enzyme-encoding genes. RESULTS: We present a novel method for identifying genes encoding for a specific metabolic function based on a local structure of metabolic network and multiple types of functional association evidence, including clustering of genes on the chromosome, similarity of phylogenetic profiles, gene expression, protein fusion events and others. Using E. coli and S. cerevisiae metabolic networks, we illustrate predictive ability of each individual type of association evidence and show that significantly better predictions can be obtained based on the combination of all data. In this way our method is able to predict 60% of enzyme-encoding genes of E. coli metabolism within the top 10 (out of 3551) candidates for their enzymatic function, and as a top candidate within 43% of the cases. CONCLUSION: We illustrate that a combination of genome context and other functional association evidence is effective in predicting genes encoding metabolic enzymes. Our approach does not rely on direct sequence homology to known enzyme-encoding genes, and can be used in conjunction with traditional homology-based metabolic reconstruction methods. The method can also be used to target orphan metabolic activities

Vibrational energy relaxation in proteins

An overview of theories related to vibrational energy relaxation (VER) in proteins is presented. VER of a selected mode in cytochrome c is studied using two theoretical approaches. One is the equilibrium simulation approach with quantum correction factors, and the other is the reduced model approach which describes the protein as an ensemble of normal modes interacting through nonlinear coupling elements. Both methods result in estimates of the VER time (sub ps) for a CD stretching mode in the protein at room temperature. The theoretical predictions are in accord with the experimental data of Romesberg's group. A perspective on future directions for the detailed study of time scales and mechanisms for VER in proteins is presented.Comment: 12 pages, 4 figures, accepted for publication in PNA

Observation of Fragile-to-Strong Dynamic Crossover in Protein Hydration Water

At low temperatures proteins exist in a glassy state, a state which has no conformational flexibility and shows no biological functions. In a hydrated protein, at and above 220 K, this flexibility is restored and the protein is able to sample more conformational sub-states, thus becomes biologically functional. This 'dynamical' transition of protein is believed to be triggered by its strong coupling with the hydration water, which also shows a similar dynamic transition. Here we demonstrate experimentally that this sudden switch in dynamic behavior of the hydration water on lysozyme occurs precisely at 220 K and can be described as a Fragile-to-Strong dynamic crossover (FSC). At FSC, the structure of hydration water makes a transition from predominantly high-density (more fluid state) to low-density (less fluid state) forms derived from existence of the second critical point at an elevated pressure.Comment: 6 pages (Latex), 4 figures (Postscript