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Functional and Evolutionary Attributes through Analysis of Metabolism

Functional and Evolutionary Attributes through Analysis of Metabolism. Sophia Tsoka European Bioinformatics Institute Cambridge UK. Outline. Metabolic reconstruction in a variety of species Analysis of sequence to function in metabolism Evolution of metabolic enzymes and pathways

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Functional and Evolutionary Attributes through Analysis of Metabolism

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  1. Functional and Evolutionary Attributes through Analysis of Metabolism Sophia Tsoka European Bioinformatics Institute Cambridge UK

  2. Outline • Metabolic reconstruction in a variety of species • Analysis of sequence to function in metabolism • Evolution of metabolic enzymes and pathways • Gene fusion analysis in enzymatic sets

  3. Enzyme Dataset Characterisation • Experimental information for the entire known metabolic complement of E. coli • Representation of metabolic pathways includes • reactions • compounds • stoichiometry • isozymes • … etc • Powerful query capabilities using lisp, java

  4. Metabolic Reconstruction - 1 For genome characterisation Methanococcus jannaschii • 40% of genome archaeal-specific • 1792 proteins • 130 pathways (22 complete) • 297 reactions identified • 609 reaction frames • 461 enzymatic reaction frames • 510 compound framesc Tsoka, Simon, Ouzounis, Archaea, 1(4), 223, 2004

  5. Metabolic Reconstruction - 2 For drug target discovery Plasmodium falciparum • 5366 proteins • 122 pathways • 697 reactions • 861 enzymatic reaction • 525 compounds • 216 chokepoint reactions as drug targets Yeh, Hanekamp, Tsoka, Karp, Altman, Genome Research, 14, 917, 2004

  6. Molecular and Functional Diversity of Metabolic Pathways • Associate enzyme sequence and function for E. coli small-molecule metabolism set • Sequence: enzyme families • Function: reaction types and pathways • How many enzyme families changed in function? • How easily is each new function invented? • Provide insight to: • function prediction based on sequence homology • evolution of biochemical pathways Tsoka & Ouzounis, Genome Research, 11, 1503-1510, 2001

  7. SwissProt+TrEMBL Evolution of Metabolic Networks Taxonomic groups • non-redundant set of all known protein sequences E. coli enzyme dataset • 548 enzymes • 208 monomers • 348 complexes • 132 pathways Archaea - (21K) Bacteria - (139K) Eukarya • Protista (17K) • Fungi (24K) • ViridiPlantae (72K) • Metazoa (187K) Viruses - (122K) Peregrin-Alvarez, Tsoka, Ouzounis, Genome Research, 13, 422-427, 2003

  8. How Conserved is Small-Molecule Metabolism across Taxonomic Groups? Are ‘Other’ Proteins More/Less Conserved than Metabolic Enzymes? Bacterial enzymes are less-species specific … … and more phylogenetically diverse, compared to control samples Peregrin-Alvarez, Tsoka, Ouzounis, Genome Research, 13, 422-427, 2003

  9. Computational Prediction of Protein Interactions Sequence-based prediction of protein interactions • phylogenetic profiles (Pellegrini et al. PNAS, 96, 4286, 1999) • gene order – (Dandekar et al. Trends Bioch. Sci. 324, 1998) • gene clusters – (Overbeek et al. PNAS, 96, 2896, 1999) • gene fusion – (Enright et al. Nature, 402, 86, 1999, Marcotte et al. Science, 285, 751, 1999) Is Fusion Frequency in E. coli Enzymes Higher than in Other Classes of Proteins? A Gene Fusion Event Tsoka & Ouzounis, Nature Genetics, 26, 141-142, 2000

  10. Conclusions • Analysis of sequence to function in metabolic networks • Evolution of metabolic enzymes and pathways • Genome constraints as a means to detect protein interactions • Analysis of entire genomes has the potential to discover • the organisational principles of cells and • the mechanisms of their evolution

  11. Acknowledgements • Medical Research Council • Computational Genomics Group, EBI Christos Ouzounis Jose Manuel Peregrin-Alvarez Leon Goldovsky • Russ Altman and Iwei Yeh • Peter Karp .. and his group

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