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. Network of physically interacting proteins Protein complexes and the physical interactome B Complexes are the cell’s factories -spliceosome -proteasome -ribosome -replication compl. -… -cytochrome bc1 ----------------- Essential role! 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… 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…

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

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

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

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

8. Computing interaction scores Gold standard reference PPI derived from MIPS/SG complexes BB TN TP Krogan et al (2006) -combining data from ≠purifications ≠ different MS techniques -only bait-prey associations -complex ‘training’ procedure -ignored ribosomal proteins(baits) 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 Gavin et al (2006) -combining data from ≠purifications -bait-prey + prey-prey associations -unbiased statistical procedure, log-odds based

9. 0 500 1000 1500 2000 2500 3000 3500 4000 020406080100120140160180200 # False positive PP interactions # True positive PP interactions Consolidated Gavin Krogan MIPS_small_scale Core data S = 0.38 MIPS small scale 0 2000 4000 6000 010002000 Comparing the PPI networks 2708 proteins 7123 interactions 1622 proteins 9074 interactions

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

11. Protein complexes are expected to ‘share’ components Protein complexes are expected to ‘share’ components Unique components Shared components ?? Physical interaction C-1 C-2 C-1 C-2 ‘Recruitment’ time; condition ‘Recruitment’ time; condition 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.

12. Markov Cluster Algorithm (MCL) Enright et al. (2002); Van Dongen S. (2002) Hierarchic Clustering by near neighbor contact score, or neighbor pattern 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 Parsing the PPI network into densely connected regions Common approach:

13. 0 5 10 15 20 25 30 35 40 45 MIPS Consolidated MCL+overlap Gavin (all)Gavin(core+module) Mean Overlaps per complex Shared genes per overlapping complex Fraction of complexes sharing subunits with other complexes 41.4% 19.5%96.9%84.1% Degree of overlap between complexes computed using different PPI networks and different methods Degree of overlap between complexes computed using different PPI networks and different methods

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

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

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

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

18. POL II POL III POL I

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

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

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. 2 R = 0.90 1 10 100 1000 1101001000 Degree Number of proteins Average node degree = 12.530 Average complex size = 5.245 R 2 = 0.72 1 10 100 1000 1101001000 Complex size Number of complexes (a) (b)

23. 0 0.05 0.1 0.15 0.2 0.25 0.3 -0.8-0.6-0.4-0.200.20.40.60.81 Uncentered Pearson Correlation Coefficient Fraction Within complexes Between complexes Random networkes Figure S6

24. Confidence score cutoff0.380.230.150.100.05 Varying both the number of proteins and the number of interactions #proteins19212270270336254489 #interactions1203515060191332614938895 Varying only the number of interactions among the top 1921 proteins #proteins1921 #interactions1203514647177952192027816 Fig. 5 (a) (b) Precision Homogeneity

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