Sequence variations, protein interactions, and diseases€¦ · Sequence variations, protein interactions, and diseases Teresa Przytycka NIH / NLM / NCBI October 7, 2009 Inferring

Sequence variations, protein

interactions, and diseases

Teresa Przytycka

NIH / NLM / NCBI

October 7, 2009

Inferring protein interaction

from sequence co-evolution

• Protein interaction and sequence co-

evolution

Interacting proteins are expected to co-

evolve to ensure proper binding

BSOSC Review, November 2008 4

Ph

ylo

gen

eti

c

Tre

es

Protein A Protein B

Ort

ho

log

s

Organism 1

Organism 2

Organism 3

Organism n

Mirrortree Method:Protein A Protein B

Inferring protein interaction from

the co-evolution principle

[Goh et al 2000, Pazos and Valencia 2001]

5

Evolutionary vector

1

2

3

.

.

.

1 2 3 ….. n

Distance Matrix

BSOSC Review, November 2008 6Compute correlation

Ph

ylo

gen

eti

c

Tre

es

Protein A Protein B

Ort

ho

log

s

Organism 1

Organism 2

Organism 3

Organism n

Mirrortree Method:Protein A Protein B

Inferring protein interaction from

the co-evolution principle

Simple idea but lot’s of questions…

• How to separate co-evolution due to

common speciation history form co-

evolution due to function?

• Is the co- evolution signal distributed

uniformly over the sequence? Between

binding site only?

• Predicting Interaction specificity SOSC Review, November 2008 7

Kann et. al. Proteins 2007

Kann et. al. JMB 2009

Jothi et. al. JMB 2007

Jothi et. al. Bioinformatics 2005

Challenges

• How to separate co-evolution due to

common speciation history form co-

evolution due to function?

• Is the co- evolution signal distributed

uniformly over the sequence? Between

binding site only?

• Predicting Interaction specificity BSOSC Review, November 2008 8


Kann et. al. JMB 2009


Jothi et. al. Bioinformatics 2005

Do binding sites co-evolve more tightly?

BSOSC Review, November 2008 9Kann et. al. JMB 2009

Binding sites are important but not the

only contributor of the co- evolutionary

signal

BSOSC Review, November 2008 11Kann et. al. JMB 2009


Predicting interacting domains


Given interacting multi-domain proteins

domains that are in contact


Co

rre

lati

on

Mirror tree approach can be used to recognize

interacting domains

MSA of

Protein A

MSA of

Protein B

Do

ma

in

Ali

gn

me

nts

Sim

ilari

ty

Ma

tric

es

Protein A Protein B

Organism 3

Organism 1

Organism 2

Organism n

Ort

ho

log

s

0.63 0.83 0.79

0.59 0.91 0.89

In 64% cases, the domain pair with

highest correlation was interacting

(55% expected by chance)

RESULTS: Jothi et. al. JMB 2007


Predicting interacting domains


Given protein-protein interaction network


Guimaraes et. al. Genome Biology 2008

Given interacting multi-domain proteins




evolution

• Predicting domain interaction from

protein interaction networks


Parsimony approach

Assumption: Protein interactions are

mediated by domain interactions

Hypothesis: Interactions evolved in

most parsimonious way

Method: Find the smallest set of

domain pairs whose interaction

would explain all protein

interactions in the network



Additional problems to solve:

Constraints: one per protein interaction Pm Pn

Linear programming formulation

For each domain pair Di Dj: variable xij taking value 0 or 1xij

• Model the noise in the network

• Estimate p-values

Pm

Pn

Objective function(representing parsimony assumption):

Interacting domains pairs – domains pairs with Xij=1


BoSC, October 2006 18

Results compared to previous methods: Identifying

interacting domain pair in interacting protein pair



evolution

• Predicting domain interaction from

protein interaction networks

• Combining genetic sequence variation,

genome wide expression profile and

protein interaction to infer pathways

dysregulated in complex diseases

Genetic variations in individuals affects gene

expression level

20

Co

ntr

ol

1

Co

ntr

ol

2

Co

ntr

ol

3

Ca

se 1

Ca

se 2

Ca

se 3

Ca

se 4

Ca

se 5

Ca

se 6

Ca

se 7

Case 8

Gene 1

Gene 2

Gene 3

.

.

.

.

.

Gene 3

Genomes

loci eQTL

Case 1

Ca

se

2

Case 7

…

…

Genotype variations

Huang et.al Bioinformatics 2009

Bringing PPI network and other high throughput

networks

21

Co

ntr

ol

1

Co

ntr

ol

2

Co

ntr

ol

3

Ca

se 1

Ca

se 2

Ca

se 3

Ca

se 4

Ca

se 5

Ca

se 6

Ca

se 7

Case 8

Gene 1

Gene 2

Gene 3

.

.

.

.

.

Gene 3

Genomes

loci

Target geneCausal gene

Case 1

Ca

se

2

Case 7

…

…

Kim et.al in submitted

eQTL

Associations of genes expressed differently

in disease /control groups are primary target

22

Co

ntr

ol

1

Co

ntr

ol

2

Co

ntr

ol

3

Ca

se 1

Ca

se 2

Ca

se 3

Ca

se 4

Ca

se 5

Ca

se 6

Ca

se 7

Case 8

Gene 1

Gene 2

Gene 3

.

.

.

.

.

Gene 3

loci

controls Disease Cases C

ase 1

Ca

se

2

Case 7

…

…

eQTL

Representative target disease genesPutative causal mutations

Uncovering causal genes and dys-regulated

pathways

23

Co

ntr

ol

1

Co

ntr

ol

2

Co

ntr

ol

3

Ca

se 1

Ca

se 2

Ca

se 3

Ca

se 4

Ca

se 5

Ca

se 6

Ca

se 7

Case 8

Gene 1

Gene 2

Gene 3

.

.

.

.

.

Gene 3

loci

controls Disease Cases C

ase 1

Ca

se

2

Case 7

…

…

Kim et.al. submitted

eQTL

Disease markers

Causal genes

Acknowledgments


Former lab members

Katia Guimaraes (associate professor, Brazil)

Raja Jothi (currently PI at NIEHS )

Elena Zotenko (currently Max Planck Institute)

Current lab members

Dong Yeon Cho

Yoo-ah Kim

Yang Huang

Damian Wojtowicz

Jie Zheng

Collaborators

Maricel Kann UMBC


Compute

conservation profile

Use conserved positions only

Additional correction using previously mentioned methods

Evolutionarily conserved regions help separate functional

co-evolution from co-evolution due common speciation

historyLow entropy (evolutionarily conserved)


Documents

Sequence variations, protein interactions, and diseases€¦ · Sequence variations, protein interactions, and diseases Teresa Przytycka NIH / NLM / NCBI October 7, 2009 Inferring