Identification of Protein-Protein Interactions by the Yeast 2-Hybrid System

1. Identification of Protein-Protein Interactions by the Yeast 2-Hybrid System Alliance for Cell Signaling Myriad Genetics Inc.

2. The Two-Hybrid System • Two hybrid proteins are generated with transcription factor domains • Both fusions are expressed in a yeast cell that carries a reporter gene whose expression is under the control of binding sites for the DNA-binding domain Activation Domain Prey Protein Bait Protein Binding Domain Reporter Gene

3. The Two-Hybrid System • Interaction of bait and prey proteins localizes the activation domain to the reporter gene, thus activating transcription. • Since the reporter gene typically codes for a survival factor, yeast colonies will grow only when an interaction occurs. Activation Domain Prey Protein Reporter mRNA Bait Protein Reporter mRNA Reporter mRNA Reporter mRNA Binding Domain Reporter mRNA Reporter Gene

4. Myriad’s ProNet Two-Hybrid Process • Industrial-scale application of the yeast two-hybrid system • Roboticized bait creation process • Custom activation domain libraries • Efficient mating strategy • Automated for quality and throughput • Positive sample tracking • Robots for media preparation, liquid handling, colony picking • Statistical quality control

5. 1 3 2 4 ProNet Process Flowchart 1 Bait Construction 40 0 2 Bait Verification 41 42 1 3 Two-Hybrid Screen 4 2 44 43 4 Identification and Confirmation of Interactions 3 5 6 45 23 22 27 7 8 9 46 48 47 24 25 26 28 10 49 29 30 12 11 50 34 33 31 32 13 51 56 Seq 35 36 14 15 16 52 53 57 37 17 Seq 54 55 38 39 18 19 20 60 58 59 21 61 62 64 63 Seq

6. AfCS Cell Type-Specific Libraries • Screening of an activation domain library that is representative of a specific cell type may permit the identification of cell type–specific protein-protein interactions. • Since a number of proteins that play a central role in B-cell signaling pathways may be expressed at low levels in resting cells, mRNA was isolated from B cells stimulated with a cocktail of ligands selected to activate the major B-cell signaling pathways.

7. Bait Selection • A number of criteria were taken into consideration in the selection of bait protein candidates: • Proteins known to be expressed in B cells and/or cardiac myocytes. • Proteins known or suspected to be involved in signaling pathways in B cells and/or cardiac myocytes. • Proteins believed important in GPCR or BCR signaling leading to PIP3 generation. These were selected first in an attempt to define interactions within a focused network of signaling molecules. • Preference was also given to proteins that had a higher likelihood of success in the yeast two-hybrid assay • proteins with well-defined modular domains exhibiting secondary structure suggestive of involvement in protein-protein interactions.

8. BLNK Phosphorylation sites Proline-rich SH3 binding Src homology 2 domain P P P Pro Pro SH2 110 116 134 146 350 436 1-100 (P sites) 150-350 (linker) 1-240 (N-term) 348-457 (SH2) 100-200 (Pro regions) 210-457 (C-term) Bait Design • Fragments of proteins representing folded domains are often more effective than the full-length protein in identifying physiologically relevant interactions • If the domain structure of a given bait protein was already established, the specific baits were designed to represent one or more folded domains. • For cases in which domain structure was not available, a variety of secondary structure prediction algorithms were used to predict domains and thus direct bait design. • Baits were designed to cover the entire protein, with several overlapping fragments, as not all baits will work effectively.

9. Project Status-One Year Later

10. B Cell Receptor Pathway

11. B Cell Receptor Pathway

12. Nck 14-3-3z Endophilin2 Pak BAN-P PDE4B3 - Pix/Cool Ruk1 Git2 PP2C gamma B Cell Receptor Pathway

13. Project Status-One Year Later

17. What can we learn from the larger data set? • Last year, 170 interactions derived from just 16 bait proteins • Limited interconnectivity • We now have 473 interactions from 55 bait proteins • How can the average biologist analyze this data set? • With difficulty! • All 473 interactions uploaded to Access database table • Query: which prey IDs are correlated with more than one bait ID?

18. Interaction examples:1

19. Interaction examples: 2

21. CamK II SOS2 CD19 CD22 Btk Fyn Dbl CAP cdc42 WISH NdkB PI3Kg (p110) Actin Cytoskeleton PDK1

22. Cbl-b 20894430 AIP 13542677 4633514 8567325 19070197 6755399 Sam68 26326968 6671538 3064262 Protein 4.1G CamK II SOS2 CD19 CD22 Btk Fyn Dbl CAP cdc42 WISH NdkB PI3Kg (p110) Actin Cytoskeleton PDK1

23. Caveats! • Y2H data requires validation by secondary assay • Protein complementation • Pull-downs • Didn’t observe source library in data analysis • Didn’t analyze all bait and prey coordinates to map sites of interaction • Cant assume multi-protein complexes since proteins may be competing for same site of interaction • This level of analysis requires more sophisticated computational approach

24. Project Status-One Year Later

25. Bait Status

26. Bait Status

27. Pattern Among Negative Baits? • Of 72 negative baits, 45 are either membrane proteins (receptors, cell-surface antigens, channels) or are membrane associated (G proteins, GEFs, RGS etc.) • May be overcome by generous and creative bait design but these proteins will always give lower “return” in Y2H assay • Also several proteins that could be expected to do better (cytosolic protein kinases and some adaptors) • Further baits advisable • Future baits sets should also include some of novel preys identified in primary screen

28. Project Status-The Year Ahead

29. Acknowledgements Myriad Terrece Pearman Brandi Williams Karen Heichman Paul Bartel AfCS Data analysis and display Gil Sambrano Bob Sinkovits Joshua Li RNA preparation Keng-Mean Lin Robert Hsueh Zhen Yan Joella Grossoehme Read Pierce Jason Polasek Jody Girouard