Towards uncovering dynamics of protein interaction networks Teresa Przytycka NIH / NLM / NCBI.

Towards uncovering dynamics Towards uncovering dynamics of protein interaction networksof protein interaction networks

Teresa PrzytyckaTeresa PrzytyckaNIH / NLM / NCBINIH / NLM / NCBI

DIMACS, May 2006DIMACS, May 2006 22

Investigating Investigating protein-protein interaction networksprotein-protein interaction networks

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


Functional Modules and Functional Modules and Functional GroupsFunctional 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


ChallengeChallenge

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


Computational Detection of Protein ComplexesComputational Detection of Protein Complexes

Spirin & Mirny 2003, Rives & Galitski 2003Bader et al. 2003 Bu et al. 2003… a large number of other methods

Common theme :

Identifying densely connected subgraphs.


Protein interactions are not staticProtein interactions are not static

Two levels of interaction dynamics:

• Interactions depending on phase in the cell cycle

• Signaling


Signaling pathwaysSignaling pathways

EGF signaling pathway from Science’sSTKE webpage


Previous work on detection of Signaling Previous work on detection of Signaling Pathways via Path Finding AlgorithmsPathways 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.


Figure from Scott et al.

a) Best pathb) Sum of “good”

paths

This picture is missing proteins complexes


Pheromone signaling pathwayPheromone signaling pathway

rece

ptor

ST

E 5

STE11

STE7

FUS3

STE11

STE7

FUS3

DIG1DIG2

STE12

KSS1

or

STE20

Activation of the pathway is initiated by the binding of extracellular pheromone to the receptor

which in turn catalyzes the exchange of GDP for GTPon its its cognate G protein alpha subunit G. G is freed to activate the downstream MAPK cascade


Overlaps between Functional Overlaps between Functional GroupsGroups

For an illustration functional groups = maximal cliques


First line of attackFirst line of attack

Overlap graph:Nodes= functional groups Edges= overlaps between them



Overlap graph:Nodes= functional groups

Edges= overlaps between them



Overlap graph:Nodes= functional groupsEdges= overlaps between them




Misleading !


Clique treeClique tree • Each tree node is a clique

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


Key properties of a clique treeKey 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?


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

Clique trees can be constructed only for Clique trees can be constructed only for chordal graphschordal graphs


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.


I

Pheromone pathway from high throughput

data; assembled by

Spirin et al. 2004

Square 1:MKK1, MKK2 are experimentally confirmed to be redundant

Square 2:STE11 and STE7 – missing interaction

Square 3:FUS3 and KSS1 – similar roles (replaceable but not redundant)

Add special “OR” edges

10

1

2 3

4

56

7

8

9

Original Graph, G

Is themodified

graphchordal?

STOP

1. Compute perfect elimination order (PEO)2. Use PEO to find maximal cliques and compute clique tree

Yes

No

Tree of Complexes

1. Add edges between nodeswith 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

Graph modification Modified Graph, G*

1

2 3

4

6

8

9

5

7

10

Maximal cliqueProtein Fill-in edge

Maximal Clique Tree of G*

6, 105, 6, 8

5, 7, 8

(1, 2, 5, 8

(1, 2), 8, 9

(1,2),(3,4)

1 2 (5v8)

vv

1

5

2

8


Representing a functional group Representing a functional group by a Boolean expression by a Boolean expression

A BA B

VA B

A v BA

C

BA (B v C)

V

B

DA

C

E (A B C) v (D E)

V V V


Not all graphs can be represented Not all graphs can be represented by Boolean expressionby Boolean expression

P4

Cographs = graphs which can be represented by Boolean expressions


ExampleExample

ST

E 5

STE11

STE7

FUS3

STE11

STE7

FUS3

KSS1

or

STE11

STE7

FUS3

KSS1

STE 5

STE5 STE11 STE7 (FUS v KSS1)vv v

H

B = BUD6 (SPH1 v SPA2) STE11D = SPH1 (STE11 v STE7) FUS3F = (FUS3 v KSS1) DIG1 DIG2H = (MKK1 v MKK2) (SPH1 v SPA2)

activation

B DC E

F

G

A

= FUS3 = HSCB2= KSS1 = BUD6= DIG1 DIG2 = MPT5

= STE11= STE5= STE7

= MKK1 v MKK2= SPH1= SPA2

FUNCTIONAL GROUPS

A = HSCB2 BUD6 STE11C = (SPH1 v SPA2) (STE11 v STE7)E = STE5 (STE11 v STE7) (FUS3 v KSS1)G = (FUS3 v KSS1) MPT5

rece

pto

r

ST

E 5

STE11STE7FUS3

STE11

STE7

FUS3

DIG1DIG2STE12

G-protein

KSS1or

STE20

FAR 1Cdc28


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

Binding of ligand to the receptor triggers signaling cascadeIn 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

FUNCTIONAL GROUPS

Based on network assembled by: Bouwmeester, et al.: (all paths of length at most 2 from NIK to NF-B are included)

activating complex

= IKKa= IKKb= IKKc

= NIK= p100= NFkB, p105

= IkBa, IkBb= IkBe= Col-Tpl2

NIKactivation

B CA

E

D

repressors


Transcription complexTranscription complex

Network from Jansen et al


SummarySummary 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.


ApplicationsApplications

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.


Future workFuture 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.


ReferencesReferences

Proceedings of the First RECOMB Satellite Proceedings of the First RECOMB Satellite Meeting on Systems Biology. 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 Elena Zotenko, Katia S Guimaraes, Raja Jothi, Teresa M PrzytyckaJothi, Teresa M PrzytyckaAlgorithms for Molecular BiologyAlgorithms for Molecular Biology 2006, 2006, 11:7 :7 (26 April 2006)(26 April 2006)


ThanksThanksFunding: NIH intramural program, NLMPrzytycka’s lab members:

Elena Zotenko

Raja Jothi

Analysis of protein interaction networks

Orthology clustering,Co-evolution

Protein ComplexesProtein structure:

comparison and classification

Towards uncovering dynamics of protein interaction networks Teresa Przytycka NIH / NLM / NCBI.

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