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From High Throughput Pull-Downs To Protein Complexes: Building a Model of the Physical

From High Throughput Pull-Downs To Protein Complexes: Building a Model of the Physical Interactome of Yeast. Shoshana Wodak Hospital for Sick Children Shoshana@sickkids.ca Depts. Biochemistry & Medical Genetics and Microbiology University of Toronto. Swiss-Prot Fortaleza 2006. B.

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From High Throughput Pull-Downs To Protein Complexes: Building a Model of the Physical

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  1. From High Throughput Pull-Downs To Protein Complexes: Building a Model of the Physical Interactome of Yeast Shoshana Wodak Hospital for Sick Children Shoshana@sickkids.ca Depts. Biochemistry & Medical Genetics and Microbiology University of Toronto Swiss-Prot Fortaleza 2006

  2. B Protein complexes and the physical interactome Complexes are the cell’s factories -spliceosome -proteasome -ribosome -replication compl. -… -cytochrome bc1 ----------------- Essential role! Our knowledge about them is limited… -can be rather dynamic entities, with variable life times -their formation is likely regulated at various levels, transcriptional level, post transcriptional modification, degradation… Network of physically interacting proteins

  3. 1st step: mapping the physical interactome Mapping binary interactions: two hybrid screens split ubiquitin screens(membrane proteins) Characterizing complexes: over expression & single affinity purification +MS Tandem affinity purification +MS Most extensive studies done in the yeast S. cerevisiae Many low throughput studies MIPS/CYGD & SGD databases Now containing ~215-230 hand-curated protein complexes for S. cerevisiae Several HTP studies 2YHB: Ito, et al. (2002) Uetz et al. (2002) AP&TAP: Ho, et al. (2002) Gavin et al. (2002) Gavin et al. (2006) Krogan et a. (2006)

  4. 20 25 47 Overlap ≤ 5% 5% < overlap ≤ 50% 50% < overlap ≤ 90% 399 90% < overlap Similarities and differences between the two 2006 studies (Gavin/ Krogan) Krogan et al. (5333) Gavin et al. (1993) Krogan et al. (2338) Gavin et al. (2671) # Baits # Preys Ho et al. (573) Ho et al. (1389) Gavin complexes (491) Krogan complexes (547) ≠ ??? Collin et al. (2006) Gavin/Krogan

  5. High throughput study of Korgan et al. (2006)

  6. MALDI/MS LC/MS (I) (II) Deriving the PPI Network (III) Identifying Functional Modules (IV) (V) Validation and Analysis

  7. bait 0.05 prey bait 0.90 prey bait 0.90 prey bait 0.32 prey bait 0.54 prey prey prey bait prey prey prey bait prey prey prey prey prey Matrix Spokes wi PPI graph MS analysis Representing interactions Interaction score calculation

  8. Gavin et al (2006) -combining data from ≠purifications -bait-prey + prey-prey associations -unbiased statistical procedure, log-odds based B Krogan et al (2006) -combining data from ≠purifications ≠ different MS techniques -only bait-prey associations -complex ‘training’ procedure -ignored ribosomal proteins(baits) B Collin et al (2006) [Consolidated network] -combined data from Gavin and Krogan -bait-prey + prey-prey associations -new Protein Enrichment (PE) score: augmented version of Gavin’s scores + ‘training’ -> Confidence scores Computing interaction scores Gold standard reference PPI derived from MIPS/SG complexes TN TP

  9. 1622 proteins 9074 interactions 6000 4000 2000 0 0 1000 2000 2708 proteins 7123 interactions Comparing the PPI networks 4000 Consolidated Gavin S = 0.38 3500 Krogan MIPS_small_scale 3000 2500 # True positive PP interactions 2000 1500 1000 Core data MIPS small scale 500 0 0 20 40 60 80 100 120 140 160 180 200 # False positive PP interactions

  10. MALDI/MS LC/MS (I) (II) Deriving the PPI Network (III) Identifying Functional Modules (IV) (V) Validation and Analysis

  11. Protein complexes are expected to ‘share’ components Unique components C-2 C-1 Physical interaction ?? Shared components C-2 This information is however currently not available from the purification data. The pulled down complexes represent temporal and spatial averages of the in-vivo distribution. ‘Recruitment’ time; condition C-1

  12. Parsing the PPI network into densely connected regions Common approach: Hierarchic Clustering Markov Cluster Algorithm (MCL) by near neighbor contact score, or neighbor pattern Enright et al. (2002); Van Dongen S. (2002) Simulates random walks within graphs by computing higher moments of contact Matrix = Measures similarity in path lengths 1,2,3,4 between nodes in the graph

  13. Overlaps per complex Shared genes per overlapping complex Degree of overlap between complexes computed using different PPI networks and different methods 45 40 35 30 25 Mean 20 15 10 5 0 MIPS Gavin (all) Gavin(core+module) Consolidated MCL+overlap Fraction of complexes sharing subunits with other complexes

  14. MALDI/MS LC/MS (I) (II) Deriving the PPI Network (III) Identifying Functional Modules (IV) (V) Validation and Analysis

  15. 10 32 53 47 87 291 177 341 Overlap ≤ 5% 5% < overlap ≤ 50% 50% < overlap ≤ 90% 90% < overlap 42 20 50 77 35 209 99 71 Overlap with MIPS complexes Gavin (491) Krogan (547) Gavin_MCL (203) Consolidated_MCL (400) Cellular localization Go annotations Overlap with MIPS complexes Consolidated MCL

  16. 20 25 13 47 30 34 Overlap ≤ 5% 111 5% < overlap ≤ 50% 50% < overlap ≤ 90% 98 140 399 58 90% < overlap 40 (a) Gavin/Krogan Gavin_MCL/Krogan Gavin_PE/Krogan_PE (491) (547) (203) (547) (321) (640) (b)

  17. a d c b 20S Proteasome Mitochondrial Ribosome 19/22S Regulator Ribosomal Large Subunit Ribosomal Small Subunit RNA Pol. I, II, III RSC Exosome Mediator SAGA TFIIIC SRP Exocyst COP I Golgi Transport SNF1 H+ Transporting ATPase, Vacuolar GenePro Vlasblom et al. (2006) APC MRP RNase

  18. POL II POL I POL III

  19. SAGA-like complex TFIID ADA complex SAGA complex Fig. 8c

  20. Diffraction Pattern MALDI/MS LC/MS Phase calculation Model refinement Protein 3D structure (I) (II) Deriving the PPI Network (III) Identifying Functional Modules (IV) (V) Validation and Analysis

  21. Acknowledgements Shuye Pu (HSC, Toronto) James Vlasblom (HSC, Toronto) Chris Orsi (HSC, Toronto) Mark Superina (HSC, Toronto) Gina Liu (HSC, Toronto) CCB Systems Support team (HSC, Toronto) Nicolas Simonis (ULB Belgium) Jacques van Helden (ULB, Belgium) Sylvain Brohée (ULB, Belgium) Nevan Krogan (B&B Toronto/ HHMI,UCSF) Jack Greenblatt (B&B,Toronto) Sean Collins (HHMI,UCSF) Jonathan Weissman (HHMI,UCSF) Andrew Emili (B&B, Toronto) John Parkinson (HSC, Toronto) Haiyuan Yu (MBB, Yale U.) Mark Gerstein (MBB,Yale U.)

  22. R 2 = 0.90 2 R = 0.72 (b) (a) 1000 1000 100 100 Number of complexes Number of proteins 10 10 1 1 1 10 100 1000 1 10 100 1000 Degree Complex size Average complex size = 5.245 Average node degree = 12.530

  23. 0.3 Within complexes 0.25 Between complexes Random networkes 0.2 Fraction 0.15 0.1 0.05 0 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 Uncentered Pearson Correlation Coefficient Figure S6

  24. (a) Precision Homogeneity (b) Fig. 5

  25. ORF tag Bank of ORF's fused with a tag y TAP Y Expression in yeast and lysis + Other cellular proteins Tandem affinity purification C B Y 1D SDS PAGE A D E B A = YPR184w = YKL085w = YPR160w = YLR258w = YER133w = YER054c Identification of components by Mass Spec D C Y E

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