Towards uncovering dynamics of protein interaction networks

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Towards uncovering dynamics of protein
interaction networks

• Teresa Przytycka
• NIH / NLM / NCBI

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Investigating protein-protein interaction
networks
Image by Gary Bader (Memorial Sloan-Kettering
Cancer Center).
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Functional Modules and Functional Groups

• Functional Module Group of genes or their
  products in a metabolic or signaling pathway,
  which are related by one or more genetic or
  cellular interactions and whose members have more
  relations among themselves than with members of
  other modules (Tornow et al. 2003)
• Functional Group protein complex (alternatively
  a group of pairwise interacting proteins) or a
  set of alternative variants of such a complex.
• Functional group is part of functional module

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Challenge

• Within a subnetwork (functional module) assummed
  to contain molecules involved in a dynamic
  process (like signaling pathway) , identify
  functional groups and partial order of their
  formation

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Computational Detection of Protein Complexes

• Spirin Mirny 2003,
• Rives Galitski 2003
• Bader et al. 2003
• Bu et al. 2003
• a large number of other methods
• Common theme
• Identifying densely connected subgraphs.

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Protein interactions are not static

• Two levels of interaction dynamics
• Interactions depending on phase in the cell
  cycle
• Signaling

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Signaling pathways
EGF signaling pathway from Sciences STKE webpage
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Previous work on detection of Signaling Pathways
via Path Finding Algorithms

• Steffen et al. 2002 Scott et al. 2005
• IDEA
• The signal travels from a receptor protein to a
  transcription factor (we may know from which
  receptor to which transcription factor).
• Enumerate simple paths (up to same length, say 8,
  from receptor(s) to transcription factor(s)
• Nodes that belong to many paths are more likely
  to be true elements of signaling pathway.

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• Figure from Scott et al.
• Best path
• Sum of good paths

This picture is missing proteins complexes
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Pheromone signaling pathway
Activation of the pathway is initiated by the
binding of extracellular pheromone to the
receptor
which in turn catalyzes the exchange of GDP for
GTP on its its cognate G protein alpha subunit
Ga.
G b is freed to activate the downstream MAPK
cascade
receptor
a
g
STE11
b
STE7
STE20
STE11
FUS3
STE 5
STE7
or
FUS3
KSS1
DIG2
DIG1
STE12
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Overlaps between Functional Groups
For an illustration functional groups maximal
cliques
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Overlaps between Functional Groups
For an illustration functional groups maximal
cliques
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Overlaps between Functional Groups
For an illustration functional groups maximal
cliques
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Overlaps between Functional Groups
For an illustration functional groups maximal
cliques
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Overlaps between Functional Groups
For an illustration functional groups maximal
cliques
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Overlaps between Functional Groups
For an illustration functional groups maximal
cliques
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Overlaps between Functional Groups
For an illustration functional groups maximal
cliques
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Overlaps between Functional Groups
For an illustration functional groups maximal
cliques
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Overlaps between Functional Groups
For an illustration functional groups maximal
cliques
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Overlaps between Functional Groups
For an illustration functional groups maximal
cliques
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First line of attack
Overlap graph Nodes functional groups Edges
overlaps between them
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First line of attack
Overlap graph Nodes functional groups Edges
overlaps between them
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First line of attack
Overlap graph Nodes functional groups Edges
overlaps between them
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First line of attack
Overlap graph Nodes functional groups Edges
overlaps between them
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First line of attack
Overlap graph Nodes functional groups Edges
overlaps between them
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First line of attack
Overlap graph Nodes functional groups Edges
overlaps between them
Misleading !
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Clique tree

• Each tree node is a clique

• For every protein, the cliques
• that contain this protein form a connected
  subtree

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Key properties of a clique tree
We can trace each protein as it enters/ leaves
each complex (functional group)
Can such a tree always be constructed?
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Clique trees can be constructed only for chordal
graphs
Chord an edge connecting two non-consecutive
nodes of a cycle
Chordal graph every cycle of length at least
four has a chord.
With these two edges the graph is not chordal
hole
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• Is protein interaction network chordal?
• Not really
• Consider smaller subnetworks like functional
  modules
• Is such subnetwork chordal?
• Not necessarily but if it is not it is typically
  chordal or close to it!
• Furthermore, the places where they violates
  chordality tend to be of interest.

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Pheromone pathway from high throughput data
assembled by Spirin et al. 2004
Square 1 MKK1, MKK2 are experimentally
confirmed to be redundant
I
Square 2 STE11 and STE7 missing interaction
Square 3 FUS3 and KSS1 similar roles
(replaceable but not redundant)
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(No Transcript)
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Representing a functional group by a Boolean
expression
A v B
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Not all graphs can be represented by Boolean
expression
P4
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Example
STE11
STE7
STE11
FUS3
STE 5
STE7
or
FUS3
KSS1
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H
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NF-?B Pathway
NF-?B resides in the cytosol bound to an
inhibitor I?B.
Binding of ligand to the receptor triggers
signaling cascade In particular phosphorylation
of I?B
I?B then becomes ubiquinated and destroyed by
proteasomes. This liberates NF-?B so that it is
now free to move into the nucleus where it acts
as a transcription factor
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repressors
activating complex
Based on network assembled by Bouwmeester, et
al. (all paths of length at most 2 from NIK to
NF-kB are included)
FUNCTIONAL GROUPS
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Transcription complex
Network from Jansen et al
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Summary

• We proposed a new method delineating functional
  groups and representing their overlaps
• Each functional group is represented as a Boolean
  expression
• If functional groups represent dynamically
  changing protein associations, the method can
  suggest a possible order of these dynamic
  changes
• For static functional groups it provides compact
  tree representation of overlaps between such
  groups
• Can be used for predicting protein-protein
  interactions and putative associations and
  pathways
• To achieve our goal we used existing results from
  chordal graph theory and cograph theory but we
  also contributed new graph-theoretical results.

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Applications

• Testing for consistency
• Generating hypothesis
• OR edges alternative/possible missing
  interactions. It is interesting to identify them
  and test which (if any) of the two possibilities
  holds
• Question Can we learn to distinguish or
  resulting from missing interaction and or
  indicating a variant of a complex.

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Future work

• So far we used methods developed by other groups
  to delineate functional modules and analyzed
  them. We are working on a new method which would
  work best with our technique.
• No dense graph requirement
• Our modules will include paths analogous to Scott
  et al.
• Considering possible ways of dealing with long
  cycles.
• Since fill-in process is not necessarily unique
  consider methods of exposing simultaneously
  possible variants.
• Add other information, e.g., co-expression in
  conjunction with our tree of complexes.

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References

• Proceedings of the First RECOMB Satellite Meeting
  on Systems Biology.
• Decomposition of overlapping protein complexes A
  graph theoretical method for analyzing static and
  dynamic protein associations Elena Zotenko,
  Katia S Guimaraes, Raja Jothi, Teresa M
  PrzytyckaAlgorithms for Molecular Biology 2006,
  17 (26 April 2006)

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Thanks

• Funding NIH intramural program, NLM
• Przytyckas lab members

Protein Complexes Protein structure comparison
and classification
Orthology clustering, Co-evolution
Analysis of protein interaction networks
Elena Zotenko
Raja Jothi