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1 Protein-Protein Interaction Networks MSC Seminar in Computational Biology 19.1.2006.

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Presentation on theme: "1 Protein-Protein Interaction Networks MSC Seminar in Computational Biology 19.1.2006."— Presentation transcript:

1 1 Protein-Protein Interaction Networks MSC Seminar in Computational Biology 19.1.2006

2 2 Proteins  From Greek – “proteios” – meaning “of first importance”  Involved in almost every process in the cell: Signal transduction catalysis and inhibition of interactions Ligands transportation Structural role

3 3 Protein-Protein Interaction networks  Unraveling the biochemistry of cells: Associating functions with known proteins Identifying functional modules  Different types of interactions  In yeast: ~6000 proteins having ~10 6 potential interactions, out of which 30,000 are real

4 4 Inferring PPI  Experimental Approaches: Small scale experiments Structural data Yeast two-hybrid system Affinity purification (Pull-Down Assays, phage display, ribosomal display) + Detection (Mass Spectrometry) Chemical linkers Protein Arrays  Additional experimental information Localization data mRNA co-expression

5 5 Inferring PPI  Computational Approaches: Genomic context of genes:  Fusion  Neighborhood Similar phylogenetic profiles Correlated mutations Domain analysis Structural data Cross species evidences

6 6 PPI Databases  DIP - Database of Interacting Proteins (small scale)  MIPS (yeast)  KEGG – pathways DB  BIND - protein–protein, protein–RNA, protein–DNA and protein– small-molecule interactions  PROTEOME – function, localization, interactions  IND - The Biomolecular Interaction Network Database  Yeast proteom databases: YPD, PombePD, CalPD  GRID  INTERACT - Protein-Protein Interaction database  MINT - Molecular Interactions Database  PROnet - Protein Interactions Database  PIM - Helicobacter pylori interaction maps

7 7 Comparing the DBs  High FP rate in high- throughput exp.  Disagreement between benchmark sets  Integration by probabilistic/ML approaches

8 8 PPI Servers  STRING (protein associations, naïve base)  PLEX – protein link explorer (phylogenetic profiles comparison)  Predictome – combining predictors (phylogenetic profiling, gene fusion, chromosomal proximity)

9 9 The Topology of PPI Networks  Small-world  Scale free  Recurring motifs (Barabasi et al. Nature genetics 2003)

10 10 Evidence for dynamically organized modularity in the yeast protein-protein interaction network Vidal et al. Nature, 2004  Investigating the role of hubs in the network considering temporal data  Data: Filtered yeast interactome (FYI) mRNA expression data (yeast expression compendium)

11 11 CC Distribution -- hubs; -- non-hubs; -- randomized net

12 12 Data & Party Hubs

13 13 Their Role in the Net Full NetNo Date HubsNo Party Hubs

14 14 Additional Dimension to the Net Date Hubs Party HubsHubs

15 15 Characteristic Path Length -- Random -- Hubs -- Party -- Date

16 16 Largest Component

17 17 Date Hubs Divide the Net into Homogeneous Modules

18 18 The CC is Still Varying

19 19 Summary  The partition to Date and Party hubs reveals an organized modularity in the network  Party hubs belong to specific modules  Date hubs connect different modules  Their essentiality is similar  Date hubs are involved in more genetic interactions, and thus perturbing them makes the genome more sensitive to other perturbations

20 20 Dynamic Complex Formation During the Yeast Cell Cycle Bork et al. Science 2005  Adding a temporal aspect to the network  Data: 600 periodically expressed genes assigned to the point in the cell cycle where its expression peaks. Physical interaction net of these proteins (high confidence interactions combined from Y2H, complex pull-down, MIPS DB) Constitutively expressed proteins that interact with the above were added to the net

21 21

22 22 Then net: 184 dynamic, 116 static proteins 412 proteins do not participate in any interaction (transient?)

23 23 Results  Interacting proteins are more likely to be expressed close in time

24 24 Results  Static proteins participate in interactions throughout the entire cycle

25 25 Conclusions  JIT assembly instead of JIT synthesis Simpler regulation Explains the low evolutionary conservation of transcription times of genes Regulation of specificity of cdc28p to different substrates by different cyclins

26 26 Conclusions  More dynamic (27%) than static (8%) proteins are Cdc28p targets – fine tuning by additional regulation through phosphorilation which marks them for degradation  Dynamic proteins have more (PEST) degradation signals

27 27 Conclusions  Party & Date Hubs

28 28 Summary  Regulation mechanisms: JIT assembly Fine tuning – controlling the degradation Time-dependent specificity

29 29  network analysis in a functional context  Data: DIP - The Database of Interacting Proteins (4,686, proteins; 14,493 interactions) Classification of the proteins to  Essential  toxicity-modulating  no-phenotype from previous genomic phenotyping study of S. cerevisiae (4,733 non-essential proteins; 4 DNA-damaging agents (MMS,4NQO, t-BuOOH, 2540nm UV radiation)) Global network analysis of phenotypic effects: Protein network and toxicity modulation in Saccharomyces cerevisiae Samson et al. PNAS 2004

30 30 Degree Distribution

31 31 The Mean Degree Essential Toxicity-Modulating Random Non-Essential No-Phenotype Mean Degree

32 32 Shortest-Path Length

33 33 Centrality

34 34 Clustering Coefficient  Whether two neighbors of a node interact  Ess; ToxMod > Random > nonEss;noPhe  Results are still valid when the randomization keeps the original degrees

35 35 Comparison to Metabolic Subnet.  The metabolic net is more similar to the random net

36 36 Comparison to Metabolic Subnet. Barabasi et al. Nature 2000

37 37 Summary  Toxicity modulating PPI are similar to essential proteins in aspects of high degree Small shortest path More clustered  Toxicity modulating proteins are essential under certain conditions  Highly coordinated response to damage  The Metabolic network example proves that not all cellular functions will show a similar behavior

38 38 Future Goals  Proceeding to multi-cellular organisms (fly, worm) and to human  Importing interactions between organisms (although full modules might be missing)  Experimental approaches not yet sufficient for number of genes in mammals

39 39 Thank You!

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