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Protein-Protein Interaction Networks

MSC Seminar in Computational Biology

19.1.2006

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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

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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 ~106

potential interactions, out of which 30,000

are real

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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

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Inferring PPI Computational Approaches:

Genomic context of genes: Fusion Neighborhood

Similar phylogenetic profiles Correlated mutations Domain analysis Structural data Cross species evidences

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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

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Comparing the DBs High FP rate in high-

throughput exp. Disagreement

between benchmark sets

Integration by probabilistic/ML approaches

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PPI Servers STRING (protein associations, naïve base) PLEX – protein link explorer (phylogenetic

profiles comparison) Predictome – combining predictors

(phylogenetic profiling, gene fusion, chromosomal proximity)

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The Topology of PPI Networks

Small-world Scale free Recurring motifs (Barabasi et al. Nature genetics 2003)

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Evidence for dynamically organized modularity in the yeast protein-protein interaction networkVidal 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)

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CC Distribution

-- hubs; -- non-hubs; -- randomized net

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Data & Party Hubs

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Their Role in the Net

Full Net No Date Hubs No Party Hubs

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Additional Dimension to the Net

Date Hubs

Party HubsHubs

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Characteristic Path Length

-- Random-- Hubs-- Party-- Date

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Largest Component

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Date Hubs Divide the Net into Homogeneous Modules

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The CC is Still Varying

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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

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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

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• Then net: 184 dynamic, 116 static proteins

• 412 proteins do not participate in any interaction (transient?)

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Results Interacting proteins are more likely to be

expressed close in time

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Results Static proteins participate in interactions

throughout the entire cycle

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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

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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

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Conclusions Party & Date Hubs

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Summary Regulation mechanisms:

JIT assembly Fine tuning – controlling the degradation Time-dependent specificity

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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 cerevisiaeSamson et al. PNAS 2004

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Degree Distribution

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The Mean Degree

Essential Toxicity-Modulating Random Non-EssentialNo-Phenotype

Mea

n D

egre

e

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Shortest-Path Length

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Centrality

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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

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Comparison to Metabolic Subnet.

The metabolic net is more similar to the random net

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Comparison to Metabolic Subnet.

Barabasi et al. Nature 2000

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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

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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

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Thank You!