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Towards uncovering dynamics of protein interaction networks. Teresa Przytycka NIH / NLM / NCBI. Investigating protein-protein interaction networks. Image by Gary Bader (Memorial Sloan-Kettering Cancer Center). Functional Modules and Functional Groups.

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investigating protein protein interaction networks
Investigating protein-protein interaction networks

Image by Gary Bader (Memorial Sloan-Kettering Cancer Center).

DIMACS, May 2006

functional modules and functional groups
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

DIMACS, May 2006

challenge
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

DIMACS, May 2006

computational detection of protein complexes
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.

DIMACS, May 2006

protein interactions are not static
Protein interactions are not static
  • Two levels of interaction dynamics:
  • Interactions depending on phase in the cell cycle
  • Signaling

DIMACS, May 2006

signaling pathways
Signaling pathways

EGF signaling pathway from Science’s

STKE webpage

DIMACS, May 2006

previous work on detection of signaling pathways via path finding algorithms
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.

DIMACS, May 2006

slide9

Figure from Scott et al.

  • Best path
  • Sum of “good” paths

This picture is missing proteins complexes

DIMACS, May 2006

pheromone signaling pathway

Activation of the pathway is initiated by the binding of

extracellular pheromone to the receptor

Pheromone signaling pathway

which in turn catalyzes theexchange 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

DIMACS, May 2006

overlaps between functional groups
Overlaps between Functional Groups

For an illustrationfunctional groups= maximal cliques

DIMACS, May 2006

overlaps between functional groups12
Overlaps between Functional Groups

For an illustration functional groups = maximal cliques

DIMACS, May 2006

overlaps between functional groups13
Overlaps between Functional Groups

For an illustrationfunctional groups = maximal cliques

DIMACS, May 2006

overlaps between functional groups14
Overlaps between Functional Groups

For an illustrationfunctional groups = maximal cliques

DIMACS, May 2006

overlaps between functional groups15
Overlaps between Functional Groups

For an illustrationfunctional groups= maximal cliques

DIMACS, May 2006

overlaps between functional groups16
Overlaps between Functional Groups

For an illustrationfunctional groups= maximal cliques

DIMACS, May 2006

overlaps between functional groups17
Overlaps between Functional Groups

For an illustrationfunctional groups= maximal cliques

DIMACS, May 2006

overlaps between functional groups18
Overlaps between Functional Groups

For an illustration functional groups= maximal cliques

DIMACS, May 2006

overlaps between functional groups19
Overlaps between Functional Groups

For an illustrationfunctional groups= maximal cliques

DIMACS, May 2006

overlaps between functional groups20
Overlaps between Functional Groups

For an illustrationfunctional groups= maximal cliques

DIMACS, May 2006

first line of attack
First line of attack

Overlap graph:

Nodes= functional groups

Edges= overlaps between them

DIMACS, May 2006

first line of attack22
First line of attack

Overlap graph:

Nodes= functional groups

Edges= overlaps between them

DIMACS, May 2006

first line of attack23
First line of attack

Overlap graph:

Nodes= functional groups

Edges= overlaps between them

DIMACS, May 2006

first line of attack24
First line of attack

Overlap graph:

Nodes= functional groups

Edges= overlaps between them

DIMACS, May 2006

first line of attack25
First line of attack

Overlap graph:

Nodes= functional groups

Edges= overlaps between them

DIMACS, May 2006

first line of attack26
First line of attack

Overlap graph:

Nodes= functional groups

Edges= overlaps between them

Misleading !

DIMACS, May 2006

clique tree
Clique tree
  • Each tree node is a clique
  • For every protein, the cliques
  • that contain this protein form a connected subtree

DIMACS, May 2006

key properties of a clique tree
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?

DIMACS, May 2006

slide29

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 isnot chordal

hole

DIMACS, May 2006

slide30
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.

DIMACS, May 2006

slide31

Add special “OR” edges

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)

DIMACS, May 2006

slide32

Original Graph, G

Graph modification

Modified Graph, G*

Is the

modified

graph

chordal?

5

6

1. Add edges between nodes

with identical set of neighbors

2. Eliminate squares (4-cycles)

(if any) by adding a (restricted)

set of “fill in” edges connecting

nodes with similar set of

neighbors

5

6

7

S

T

O

P

7

No

1

1

10

8

8

10

3

2

3

9

2

9

4

4

Yes

1. Compute perfect elimination

order (PEO)

2. Use PEO to find maximal

cliques and compute

clique tree

Tree of Complexes

Maximal Clique Tree of G*

5, 6, 8

(1, 2), 8, 9

6, 10

(1, 2, 5, 8

(1,2),(3,4)

5, 7, 8

5

Protein

Maximal clique

1 2 (5v8)

v

v

1

8

2

Fill-in edge

representing a functional group by a boolean expression

A B

V

A

(A B C) v (D E)

B

B

A

A (B v C)

V

V

V

A

A

C

B

V

D

E

C

B

Representing a functional group by a Boolean expression

A v B

DIMACS, May 2006

example

FUS3

STE11

STE 5

STE7

KSS1

STE5 STE11 STE7 (FUS v KSS1)

v

v

v

Example

STE11

STE7

STE11

FUS3

STE 5

STE7

or

FUS3

KSS1

DIMACS, May 2006

slide36

F

A

B

C

D

E

activation

G

= MKK1 v MKK2

= SPH1

= SPA2

= STE11

= STE5

= STE7

= FUS3 = HSCB2

= KSS1 = BUD6

= DIG1 DIG2 = MPT5

receptor

G-protein

a

g

STE11

b

FUNCTIONAL GROUPS

STE7

A = HSCB2 BUD6 STE11

C = (SPH1 v SPA2) (STE11 v STE7)

E = STE5 (STE11 v STE7) (FUS3 v KSS1)

G = (FUS3 v KSS1) MPT5

B = BUD6 (SPH1 v SPA2) STE11

D = SPH1 (STE11 v STE7) FUS3

F = (FUS3 v KSS1) DIG1 DIG2

H = (MKK1 v MKK2) (SPH1 v SPA2)

STE20

STE11

FUS3

STE 5

STE7

or

FUS3

KSS1

FAR 1

DIG2

DIG1

STE12

Cdc28

H

nf b pathway

NF-κB resides in the cytosol bound to an inhibitor IκB.

NF-κB Pathway

Binding of ligand to the receptor triggers signaling cascade

In particular phosphorylation of IκB

IκB then becomes ubiquinated and destroyed by proteasomes.

This liberates NF-κB so that it is now free to move into

the nucleus where it acts as a transcription factor

DIMACS, May 2006

slide38

D

A

B

C

NIK

activation

E

= NIK

= p100

= NFkB, p105

= IKKa

= IKKb

= IKKc

= IkBa, IkBb

= IkBe

= Col-Tpl2

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

transcription complex
Transcription complex

Network from Jansen et al

DIMACS, May 2006

summary
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.

DIMACS, May 2006

applications
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.

DIMACS, May 2006

future work
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.

DIMACS, May 2006

references
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, 1:7 (26 April 2006)

DIMACS, May 2006

thanks
Thanks
  • Funding: NIH intramural program, NLM
  • Przytycka’s lab members:

Protein Complexes

Protein structure:

comparison

and classification

Orthology clustering,

Co-evolution

Analysis of protein

interaction networks

Elena Zotenko

DIMACS, May 2006

Raja Jothi