ProteinProtein Interaction Networks

1
Protein-Protein Interaction Networks

• MSC Seminar in Computational Biology
• 19.1.2006

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
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

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
Inferring PPI

• Computational Approaches
• Genomic context of genes
• Fusion
• Neighborhood
• Similar phylogenetic profiles
• Correlated mutations
• Domain analysis
• Structural data
• Cross species evidences

6
PPI Databases

• DIP - Database of Interacting Proteins (small
  scale)
• MIPS (yeast)
• KEGG pathways DB
• BIND - proteinprotein, proteinRNA, proteinDNA
  and proteinsmall-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
Comparing the DBs

• High FP rate in high- throughput exp.
• Disagreement between benchmark sets
• Integration by probabilistic/ML approaches

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
The Topology of PPI Networks

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

10
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)

11
CC Distribution
-- hubs -- non-hubs -- randomized net
12
Data Party Hubs
13
Their Role in the Net
Full Net
No Date Hubs
No Party Hubs
14
Additional Dimension to the Net
Date Hubs
Party Hubs
Hubs
15
Characteristic Path Length
-- Random -- Hubs -- Party -- Date
16
Largest Component
17
Date Hubs Divide the Net into Homogeneous Modules
18
The CC is Still Varying
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
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
(No Transcript)
22

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

23
Results

• Interacting proteins are more likely to be
  expressed close in time

24
Results

• Static proteins participate in interactions
  throughout the entire cycle

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
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
Conclusions

• Party Date Hubs

28
Summary

• Regulation mechanisms
• JIT assembly
• Fine tuning controlling the degradation
• Time-dependent specificity

29
Global network analysis of phenotypic effects
Protein network and toxicity modulation in
Saccharomyces cerevisiaeSamson et al. PNAS 2004

• 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))

30
Degree Distribution
31
The Mean Degree
Essential Toxicity-Modulating Random
Non-Essential No-Phenotype
Mean Degree
32
Shortest-Path Length
33
Centrality
34
Clustering Coefficient

• Whether two neighbors of a node interact
• Ess ToxMod gt Random gt nonEssnoPhe
• Results are still valid when the randomization
  keeps the original degrees

35
Comparison to Metabolic Subnet.

• ? The metabolic net is more similar to the random
  net

36
Comparison to Metabolic Subnet. Barabasi et al.
Nature 2000
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
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
Thank You!