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Proteomics

Proteomics . For any given species, the space of possible biomolecules and their organization into pathways and processes is large but finite. .

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Proteomics

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  1. Proteomics For any given species, the space of possible biomolecules and their organization into pathways and processes is large but finite.

  2. In theory, therefore, the biological systems operating in a species can be described comprehensively if a sufficient density of observations on all the elements that constitute the system can be obtained.

  3. Proteomics • Initial goal was to rapidly identify all the proteins expressed by a cell or tissue – a goal that has yet to be achieved for any species! • There are more molecular genetic ways to study proteins and more biochemical ways

  4. How to organize information? • Gene Ontology • Biological process • Frequently from biochemical analyses • In silico analysis • Molecular function • Biochemical analysis • Cellular component • Biochemical analysis • GFP or other tagging • Interactions MS Two-hybrid Other methods

  5. Transposon tagging to identify ORFS Why include a URA3 gene? Why have a lacZ lacking a promoter? Why would you want to cut out all the intervening DNA?

  6. High throughput tests of function

  7. Yeast deletion strains

  8. Microscopic localization

  9. microscopy

  10. Process for protein isolation

  11. 2D gel electrophoresis

  12. Antibody arrays Good for low-abundance proteins Problem is antibody specificity

  13. Array-based protein interaction detection

  14. Protein microarrays

  15. Caveats • The technology of proteomics is not as mature as genomics, owing to the lack of amplification schemes akin to PCR. Only proteins from a natural source can be analyzed • The complexities of the proteome arise because most proteins seem to be processed and modified in complex ways and can be the products of differential splicing; • in addition; protein abundance spans a range estimated to be 5 to 6 orders of magnitude in yeast and 10 orders of magnitude in humans.

  16. MS analysis

  17. MS analysis

  18. Two-hybrid analysis

  19. Interaction maps - Grid

  20. Goals- Aebersold • Convergence between discovery science and hypothesis-driven science • Systems biology approaches will detect connections between broad cellular functions and pathways that were neigher apparent nor predictable. • Ability to collect data already outstrips our ability to validate, integrate, and interpret it.

  21. challenges • Complexity – some proteins have >1000 variants • Need for a general technology for targeted manipulation of gene expression • Limited throughput of todays proteomic platforms • Lack of general technique for absolute quantitation of proteins

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