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Protein-protein interactions

Protein-protein interactions. Anders Riis Kristensen. Why study protein-proteins interactions?. G uilt by association Functional characterization of proteins Global phenomenon in the cell Modularity of proteins in protein complexes

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Protein-protein interactions

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  1. Protein-protein interactions Anders Riis Kristensen

  2. Why study protein-proteins interactions? • Guilt by association • Functional characterization of proteins • Global phenomenon in the cell • Modularity of proteins in protein complexes • Estimated 90 % of proteins serve as components of protein complexes, in most, if not all living organisms • Proteins interaction networks are scale-free • Few proteins many interactions, many proteins few interactions

  3. Protein interactions • Scope of study • Focused studies • FRET • AP-MS • Native mass spectrometry • Global studies • AP-MS • Y2H • SEC-PCP-SILAC

  4. Quantitative AP-MS • To differentiating specific interaction from non specific

  5. Temporal changes of the interactome

  6. AP-MRM Allows for quantitatively studying a number of proteins consistently Nature Biotechnology 29, 653-658 (2011)

  7. Problems with present techniques • Not suitable to study the human interactome • Mass spectrometry based techniques generates many samples (>30.000) • Require often tagging of proteins • Interfere with interacting proteins • Can lead to mis-localisation • Can not easily detect dynamic interactions

  8. Co migration methods J Cell Biol. 1981 Sep;90(3):568-76. Chen V, 2012, in press

  9. Co-migration methods used to study organelles Centrosome proteins Proteasome proteins Foster LJ, Cell, 125, 2006 Jakobsen L, The EMBO Journal, 30, 2011

  10. Workflow for Protein Correlation Profiling-SILAC • PCP-SILAC has traditionally been used to profile organelles using density gradients1-3. “LYS 4” “ARG 6” “LYS 0” “ARG 0” 100K MWCO 100K MWCO SEC SEC 1. Andersen, JS; Nature426, 570–574 (2003) 2. Foster, LJ; Cell125, 187–199 (2006) 3. Jakobsen L ; The EMBO Journal30, 1520–1535 (2011) Mass Spectrometry

  11. Principle of PCP-SILAC

  12. Data analysis using clustering Signalosome Prefoldin Proteasome Core Subunit Ratio M/L Proteasome Regulatory Subunit Fractions 0 5

  13. Deconvolution of the chromatograms

  14. Summary of data analysis 3421 Proteins Identified 8935 Gaussian peaks 7209 Binary Interactions 291 Protein complexes

  15. Performance of PCP-SILAC

  16. Special features of ppSILAC combined with SEC

  17. Determination of stoichiometry PSMD8

  18. Workflow for Dynamic PCP- SILAC +EGF “LYS 0” “ARG 0” “LYS 4” “ARG 6” “LYS 8” “ARG 10” 100K MWCO 100K MWCO 100K MWCO SEC SEC Mass spectrometry

  19. Workflow for Dynamic ppSILAC +EGF “LYS 0” “ARG 0” “LYS 4” “ARG 6” “LYS 8” “ARG 10” 100K MWCO 100K MWCO 100K MWCO SEC SEC Mass spectrometry

  20. Identification of dynamic interactions • Identified 351 proteins that changed interaction following 20 min. EGF stimulation. STAM Aranda B; Nuc. Acids Research38 (2001) Olsen JV; Cell127 (2006) Blagoev B; Nat. Biotechnol22 (2004) Argenzio E; Mol. Sys. Biol.7 (2011)

  21. Validation of complexes “LYS 0” “ARG 0” “LYS 4” “ARG 6” “LYS 8” “ARG 10” 100K MWCO 100K MWCO 100K MWCO Add antibody SEC SEC SEC Mass spectrometry

  22. Antibody leads to earlier elution from SEC

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