1. Mapping Protein-Protein Interactions MEDG 505 (Genome Analysis) 13 January 2005 Morin: -Overview -IP-MS -Data integration Student presentations: -Y2H interactions -RNA vs Protein expression analysis Discussion: -Lessons -Application

2. Central Dogma DNARNAProteinFunction Humans: - ~25,000 genes - 25-40% with functional annotations General Goal: Annotation of proteome -Identify disease related proteins -Identify therapeutic targets How identify protein functions?

3. Protein Function General purpose of proteins is to interaction with other molecules -Enzyme/substrate -Protein/protein Cellular processes governed by complex networks of interacting proteins -Determination of protein-protein interactions infers functional hypotheses

4. Protein Annotation -verifies biological role -translation to humans problematic -differences in biology cloud interpretation -can verify biological role -binary interactions -often protein fragments -high false positives -extensively employed -comprehensive and HTP -mRNAs infer proteome -identifies expression changes -silent to PTMs -cause and effect difficult to infer -interactions difficult to predict Large Scale Methods for annotation of protein function: -Genetic -Mutational analysis in model organisms -Yeast 2-hybrid -Genomic -mRNA profiling -Biochemical -MS analysis of purified protein complexes -identifies interactions directly -yields higher order interactions -identifies PTMs -binding affinity can be employed -technically challenging Lesson: All methods need to be employed to fully annotate proteome.

5. IP-MS Immunoprecipitation - Mass Spectrometry

6. Immunoprecipitate Interaction Partners

7. Protein identification Excise bands LC-MS/MS fragmentation Gel separation

8. Tagged Protein Structure ORFFLAGlox C-tagged construct N-tagged construct FLAG lox ORF lox CMV

9. Properties of Immunoprecipitated Protein Complexes Types of interacting proteins Background binding to bait/matrix/MS (filter?) Proteins from throughout lifespan Processing/transport/degradation proteins (filter?) Weak affinity (less reproducible?) Strong affinity Primary interactors Secondary interactors High data volume Experimental design and analysis should be designed for expectations Methodology for evaluation 1-Experimental validation 2-Bioinformatic evaluation 3-Experimental reproducibility -transfection/IP protocols

10. Method Characterization Characterization Project 1- 49 Baits, from diverse protein families -tag both N and C termini 2- IP-MS, repeat 4+ times 3- 190 preys -hit: -observed 2+ times -frequency less than 5% 4- Analyze

11. N- & C-Tag Hit Overlap Lessons: 1)5 Hits per Bait. 2)N-tags interfere less than C-tags. 3)Both tags needed to get good representation. Sample 33 Baits

12. Prey Reproducibility Sample 42 Baits 190 Preys Note: ~50% of C-tags have 1.0 rate. Lesson: Improve immunoprecipitation conditions. Question: How many trials to see a prey 2 times?

13. Note: If hit = 3+ times then probability = 0.125 Planning Trial Size HH HT TH TT H H T T H T H T H T H T H H T T H T H T T H T H Rate = 0.5 2 trials3 trials p: prey observation frequency n: number of trials k: number of observations required (2) Binomial distribution equation Lessons: Identifies suspect data Improving reproducibility rate reduces number of trials needed. Lessons: Identifies suspect data Improving reproducibility rate reduces number of trials needed.

14. False Negative Rate Lesson: 1 or 2 trials provides highly incomplete dataset.

15. Predicted False Positive Rate PathMap (global) observation frequency False positive frequency Method -determine prey frequency in database -Assume background proteins have a uniform random distribution -Assume background does not change with time or experimental conditions -Compare prey frequency to predicted observation rate p: prey observation frequency n: number of trials k: number of observations required (2) E falsepositive : expected number of false positives cutoff: frequency cutoff Numhits(p): number of hits at each prey observation frequency 5% < 0.05 “safe” region

16. Estimated Experimental False Positive Rate Random Sampling Method -randomly reassign bait labels for each IP for all 49 baits -repeat -obtain 3, 4, and 5 trial sets, 49 baits each, with preys randomly assigned to a bait (5% database frequency) -assume random distribution (no relation between baits) Results -false positive rate 2-3X greater than calculated. -non-uniform distribution Reasons -not independent experiments -non-random -baits are related -cross-contamination -equipment contamination

17. Managing False Positives 1-Control subtraction -empty vector immunoprecipitation -irrelevant protein immunoprecipitation 2-Reproducibility -2+ times -3-4 biological replicates 3-Database frequency -observation frequency cutoff 4- Prioritization -annotation 5-Validation -reciprocal immunoprecipitation -co-expression

18. Interaction Network Example

19. Human Pathway Pilot Project Contract design: -20 baits, chosen by customer (17 actually provided) -N & C FLAG tags, constructed by MDSP. -Report all observed interactions. Additional design parameters: -Expressed and immunoprecipitated 4 times each. -Report all interactions classified as hits. TNF  pathway -Proinflammatory cytokine expressed mainly by activated monocytes and macrophages -Highly studied -Pathway members provide ready availability of baits. -Understanding incomplete, providing opportunity for discovery -Disease involvement -Tumor progression and killing -Diabetes -Infection -Inflammation -Pharmaceutical potential -Find protein targets that perform isolated TNF  functions without side-effects.

20. with Preys TNFα Pathway: Inflammation/Cancer - 17 Baits - Both N & C tags - 4 Immunoprecipitations

21. TNF  Pathway Project Summary Potential antibody targets

22. Integrating Proteomic and Genomic Information

23. Genes Regulating Cell Growth and Division Systematic identification of pathways that couple cell growth and division in yeast Science 297: 395-400, 2002. Paul Jorgensen Joy L. Nishikawa Bobby-Joe Breitkreutz Mike Tyers Program in Molecular Biology and Cancer Samuel Lunenfeld Research Institute Mount Sinai Hospital Toronto, Ontario, Canada

24. Genetic Screen for Yeast Size Mutants lge Wild type size profile whi Cell volume (fL) 10356085110 4812 strains (~2 yrs) sfp1

25. GALSFP1WT GAL genes (10) Nucleotide biosynthesis (12) tRNA synthetases (6) ribosome biogenesis (21) RNA Polymerases I and III (10) nucleolus (29) Translation initiation and elongation (17) Ribosomal protein genes (136) SFP1 5 3 1.5 -1.5 -3 -5 scale SFP1 regulated genes

26. Yeast Interaction Map Ho et al. Nature 10:180-3, 2002.  FLAG IP > LC-MS/MS -725 bait attempts -493 baits > 1578 preys -646 unannotated preys

27. Protein interactions Overlap of Genetic, Expression & Interaction Data Common mRNA regulation Genetic interaction Nucleolar Network

28. Gene Regulation in Breast Cancer 98 breast tumors x 25000 genes 430 231 2460 van’t Veer et al. (2002) Nature 415, 530-6. “genes that are overexpressed in tumors with a poor prognosis profile are potential targets for the rational development of new cancer drugs” Proteins in the functional pathway of disease associated genes may identify additional or better therapeutic targets.

29. Overlap of PathMap and Breast Cancer Genes van’t Veer et al. (2002) Nature 415, 530-6.

30. Protein Networks in Prognosis Reporters up regulated down regulated only + 55 35 4 16 enzyme Interaction network provides context

31. Integrated Genomic/Proteomic Breast Cancer Project van’t Veer et al. (2002) Nature 415, 530-6. Profile gene expression changes during tumor progression Assemble experimental gene set -genes with expression changes -genes suspect for breast cancer progression Perform IP-MS to determine interacting proteins Analyze for regulatory networks and critical pathways