1. Protein-Protein Interactions Networks  “ A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae ” P.Utez et al, Nature 2000  “ Functional organisation of the yeast proteome by systematic analysis of protein complexes ” G. Gavin et al, Nature 2002  “ Global Mapping of the Yeast Genetic Interaction Network ” Tong et al, Science 2004  “ Global analysis of protein activities using proteome chips ” Zhu, H. et al. Science 2001  “ Conserved patterns of protein interaction in multiple species ” R. Sharan et al, PNAS 2005

2. Genomics  Genomics – “ The large scale study of genomes and their functions ”  Why protein network?

3. Why protein network?  Assemblies represent more than the sum of their parts.  `complexity' may partly rely on the contextual combination of the gene products. 24,000 genes 26,000 genes 50,000 genes 19,500 genes 14,000 genes

4. Yeast as a model  Why yeast genomics? A model eukaryote organism … Saccharomyces cerevisiae

6. The best-studied organism  ~5,500 genes.  16(!) chromosomes.  13 Mb of DNA (humans have ~3,000 Mb).  We know (?) the function of >1/2 of the yeast genes.  All the essential functions are conserved from yeast to humans.conserved

7. Example: cell cycle Lee Hartwell, Nobel Prize 2001

8. 4 methodologies for high throughput research  Two hybrid systems  Analysis of protein complexes  Synthetic lethal  Protein Chips (?)

9. Two hybrid system  Aim: Identify pairs of Physical interactions.  Solution: Use the transcription mechanism of the cell

10. 3 TRANSCRIPTION DNA RNA TRANSLATION PROTEIN The central dogma

11. Transcription factors Movie – transcription (molecular model, real time) 7.2

12. Transcription – real timeTranscription – real time (viedo)

13. Eukaryotic mRNA Reporter gene

14. Two hybrid system  Isolate double plasmids using reporter or selection methods.

15. All against All

16. Focus on the baits  Baits are analyzed separately.  192 baits vs. ~6000 pray yeast strains. A component of RNA polymerase I, III, identification of three new interacting proteins

17. Two hybrid system

18.  “ A comprehensive two-hybrid analysis to explore the yeast protein interactome “ Ito T. et al, PNAS 2001.

19. Analysis of protein complexes  Aim: Identification of complexes and their sub units.  Solution: a two step method Isolation of only relevant complexes Identification of complex units.

20. Double Isolation

21. Identification of the members  Divide and conquer- Digest with protease Mass spectrometry Denaturate assembly

22. How does it work?  The deflection route of ionized molecules is used to determine the molecule ’ s mass.  The output:

23. Analysis of protein complexes  Cross results of peptide mass with protein database.  Mass spectrometry can be implied again if the data is not sufficient, this time for the peptides.

24. Analysis of protein complexes Systematic(1): 1739 bait proteins. 232 complexes with 589 baits. Systematic(2): 725 bait proteins. 3,617 interactions with 493 baits.

26. Analysis of protein complexes  About 25% false positive rate.  Covers 56/60%, 10/35% in Y2H, of known complexes.  Only 7% of the interactions were seen by Y2H assays.  But,  Can evaluate protein- Concentration. Localization. Post-translational modifications.

27. Synthetic lethality  First, few words on essentiality.  Create new strains, each strain with one gene deleted (96% coverage)  Tag each strains with a unique sequence.  Grow all the strains.  Measure the amount of each seq.  Some 18.7% (1,105) are essential.

28. Synthetic lethality  High genetic redundancy hardens the discovery of many gene functions (30%).  Only the double mutation is lethal, either of the single mutations is viable.  Why? Single biochemical pathway. Two distinct pathways for one process. …

29. The naïve approach  But how do you genomics it? …

30. All vs. All  ~5100 non essential mutants. Main tricks: 1. Haploid strains 2. Resistant markers. 3. Extra marker for the library haploid.

31. Synthetic lethality … Making it genomics  Mass analysis: Crossing the query haploid with a library (synthetic genetic array)  Tetrad analysis: Validation and finding synthetic sick

32. The genetic interaction map  8 genes against all produced a network of synthetic lethal pairs.

33. Synthetic lethality … Making it genomics  132 query genes vs. 4700  False negatives – 17-42%.  At least 4 times more dense than the PPI network.  Predicting ~100,000 interactions (?)

34. PPI Summery (2003)

35. PPI Summery S. Cerevisiae (Yeast) 4389 proteins 14319 interactions C. Elegans (Worm) 2718 proteins 3926 interactions D. Melanogaster (Fly) 7038 proteins 20720 interactions Sharan et al. PNAS 2005

36. We like Networks  Exploit graph theory methods.  Provide a general solution for data integration.

37. Network Structure and Function  Identify highly nonrandom network structural patterns that reflect function: Ideker et al: Finding co-regulated sub-graphs. Lee at el: The repeated instances of each motif are the result of evolutionary convergence. Barabasi at el: Network motifs are associated with specific cellular tasks. …

38. Baker’s yeast (Saccharomyes cerevisiae) ~15000 interactions ~5000 interacting genes Bacterial pathogen (Helicobacter pylori) ~1500 interactions ~700 interacting genes Conserved patterns of PPI in multiple species Kelley et al. PNAS 2003

39. Goals  Separating true PPI from false positives.  Assign functional roles to interactions.  Predict interactions.  Organizing the data into models of cellular signaling and regulatory machinery.  How?  Use approach based on evolutionary cross-species comparisons.

40. Interaction graph ( per species )  Vertices are the organism ’ s interacting proteins.  Edges are pair-wise interactions between proteins.  Edges are weighted using a logistic regression model:weighted A: Number of times an interaction was observed.  For Fly and worm observation In one experiment. B: Correlation coefficient of the gene expression.expression  Shown to be correlated to interaction. C: Proteins ’ small world clustering coefficient.  Sum of the neighbors logHG probs.

41. How do we find Sub-network conservation ?  Interactions within each species should approximate the desired structure: Pathway. Signal transduction. Cluster. Protein complex.  Many-to-many correspondence between the sets of proteins.

42. Network alignment graph  Each node corresponds to k sequence-similar proteins. BLAST E value < -7; considering the 10 best matches only. Cannot be split into two parts with no sequence similarity between them.  Edge represents a conserved interaction. Match -> One pair of proteins directly interacts and all other include proteins with distance <2 in the interaction maps. Gap – > All protein pairs are of distance 2 in the interaction maps. Match-Gap-> At least max{2, k −1} protein pairs directly interact.  A subgraph corresponds to a conserved sub-network.

43. q(e) q(e) – interaction similarity A probabilistic model   PS    Pe random q eq log

44. Searching for conserved sub- networks  Identifying high-scoring subgraphs of the network alignment graph. … This problem is computationally hard.  Exhaustively we find seeds - paths with 4 nodes.  Expand high scoring seeds. Greedily add/remove nodes.  Filter subgraphs with a high degree of overlap (>80%).

45. Statistical evaluation of sub- networks  Randomized data is produced: Random shuffling of each of the interaction graphs. Randomizing the sequence-similarity relationships.  Find the highest-scoring sub-networks of a given size.  P-value is computed by the distribution of the top scores.

46. ← Protein sequence similarity → ← Bacteria →← Yeast → The final product

47. 3-way Comparison S. cerevisiae 4389 proteins 14319 interactions C. elegans 2718 proteins 3926 interactions D. melanogaster 7038 proteins 20720 interactions Sharan et al. PNAS 2005

48. Multiple Network Alignment Preprocessing Interaction scores: logistic regression on #observations, expression correlation, clustering coeff. Network alignment Subnetwork search Filtering & Visualizing p-value<0.01,  80% overlap Conserved paths Conserved clusters Protein groups Conserved interactions

50. Reduced false positives  Compared these conserved clusters to known complexes in yeast - Pure cluster - contain >2 annotated proteins and >1/2 of these shared the same annotation.  94%(>83% in mono specie) pure clusters.  Did ‘‘ sticky ’’ proteins biased the clusters?  Of 39 proteins (> 50 neighbors), only 10 were included in conserved clusters. And they were annotated so.

51. Cross Validation: Function Species#Correct#PredictionsSuccess rate (%) Yeast11419858 Worm579560 Fly11518463  Outperforms sequence-based approach at 37-53%.  Guilty by association. Enrichment of GO annotation (p<0.01).GO More then half of the annotated proteins had the annotation.

52. Cross Validation: Interaction SpeciesSensitivity (%) Specificity (%) P-valueStrategy Yeast50771e-25[1] Worm43821e-13[1] Fly23845e-5[1] Yeast9991e-6[1]+[2] Worm101006e-4[1]+[2] Fly0.41000.5[1]+[2]  [1] Evidence that proteins with similar sequences interact within other species.  [2] Co-occurrence of these proteins in the same conserved cluster. cluster

53. Wet Validation: Interaction  The tests were performed by using two-hybrid assays.  Of the 65 yeast predicted interactions: 5 were self inducing. 31 tested positive.

54. Conclusions  Associate proteins that are not necessarily each other ’ s best sequence match. 177/679 conserved clusters. 31/129 conserved paths.  Inter module interaction is reinforced by inter- species observations.  40-52% >> 0.042% as a random PPI prediction.  Many PPI circuits are conserved over evolution.

55. Thanks!!!  Recoverin, a calcium-activated myristoyl switch.

56. GO – Gene Ontology  all : all ( 171472 ) GO:0008150 : biological_process ( 109503 )  GO:0007582 : physiological process ( 70981 ) GO:0008152 : metabolism ( 41395 )  GO:0009058 : biosynthesis ( 10256 ) GO:0009059 : macromolecule biosynthesis ( 6876 ) GO:0006412 : protein biosynthesis ( 4611 )  GO:0043170 : macromolecule metabolism ( 17198 ) GO:0009059 : macromolecule biosynthesis ( 6876 ) GO:0006412 : protein biosynthesis ( 4611 ) GO:0019538 : protein metabolism ( 12856 ) GO:0006412 : protein biosynthesis ( 4611 ) GO:0005575 : cellular_component ( 98453 ) GO:0003674 : molecular_function ( 108120 ) back

57. Interaction distribution

58. Expression data  Yeast - 794 conditions.  Fly - over 90 CC time points+170 profiles.  Worm - over 553 conditions. back

59. Edge weight  where 0,..., 3 are the parameters of the distribution.  Maximize the likelihood: Positive: MIPS interactions. Negative: random or false positives in the cross validation test.  Yeast - 1006 positive and negative examples.  Fly - 96 positive and negative examples.  Worm – 24 positive and 50 negative examples. back

60. 71 conserved regions: 183 significant clusters and 240 significant paths. back

61. A probabilistic model  Ms - the sub-network model.  Mn - the null model.  Ouv - the set of available observations on u-v.  Puv- fraction of (u,v) in order preserving graphs family.  T/Fuv – True/False edge (u,v). back

62. A probabilistic model  Each species ’ interaction map was randomly constructed.  Randomizing assumptions: Each interaction should be present independently with high probability. The probability depends on their total number of connections in the network.

63. Why Yeast?  “Comparative Genomics of the Eukaryotes” Rubin GM. et al. Science 2000 back

64. Analysis of protein complexes 1. Isolation: A straight forward method, using Affinity chromatography. A target protein is attached to polymer beads that are packed into a column. Cell proteins are washed through the column.Proteins the interact with the target protein adhere to the affinity matrix and are eluted later.

65. Analysis of protein complexes 2. Isolation: Co-immunoprecipitation. An antibody that recognizes the target protein is used to isolate the protein. Usually the there isn ’ t a highly specific antibody for the target protein. A chimera protein is formed, using a the target protein and an epitope tag. The common tag is a enzyme glutathione S-transferase (GST).

66. Analysis of protein complexes 2. Isolation: Isolation of complex using the Chimera Glutathione coated beads Cell extract Glutathione solution

67. MIPS  Munich Information Center for Protein Sequences (MIPS).  Hierarchy Structure.  Only manually annotated complexes from DIP.  Left with 486 proteins spanning 57 categories at level 3. back