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

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Presentation transcript:

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

Reporter Gene Bait Protein Binding Domain Prey Protein Activation Domain 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 The Two-Hybrid System

Reporter Gene Bait Protein Binding Domain Prey Protein Activation Domain 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.

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

ProNet Process Flowchart Seq Bait Construction 2 Bait Verification 3 Two-Hybrid Screen 4 Identification and Confirmation of Interactions

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. B-Cell mRNA SourcesCardiac Myocyte mRNA Sources Murine primary resting B cellsMurine primary cardiac myocytes Murine primary B cells stimulated with a ligand cocktail of anti-IgM, anti- CD40, and IL-4 for 4 hr Nk-TAg murine cardiac myocyte cell line WEHI-231 murine B-cell line

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.

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 (P sites) (Pro regions) (N-term) (linker) (SH2) (C-term) PPPSH2Pro Phosphorylation sites Proline-rich SH3 binding Src homology 2 domain BLNK

Project Status-One Year Later May 2002May 2003 Bait Proteins Approved Screens Initiated Bait Fragments in Preparation 119- Bait Proteins with Released Data 16 (26)55 (90) Total Interactions170473

B Cell Receptor Pathway

PakNck Pix/Cool Git z Endophilin2 BAN-P PDE4B3 Ruk1 - PP2C gamma

Project Status-One Year Later May 2002May 2003 Bait Proteins Approved Screens Initiated Bait Fragments in Preparation 119- Bait Proteins with Released Data 16 (26)55 (90) Total Interactions170473

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?

Interaction examples:1

Interaction examples: 2

CD19 Actin Cytoskeleton PDK1 PI3K  (p110) Btk WISH CAP Dbl CamK II NdkB cdc42 CD22 Fyn SOS2

CD19 Btk Actin Cytoskeleton SOS2 WISH CAP Dbl CamK II NdkB cdc42 PDK1 CD22 Fyn PI3K  (p110) Sam68 Protein 4.1G AIP Cbl-b

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

Project Status-One Year Later May 2002May 2003 Bait Proteins Approved Screens Initiated Bait Fragments in Preparation 119- Bait Proteins with Released Data 16 (26)55 (90) Total Interactions170473

Bait Status

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

Project Status-The Year Ahead May 2003May 2004 Bait Proteins Approved 163? Bait Proteins with Released Data 55? Bait Proteins in Screening 36? Bait Proteins with No Interactions 72? Total Interactions473?

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