Protein-protein interactions Courtesy of Sarah Teichmann & Jose B. Pereira-Leal MRC Laboratory of Molecular Biology, Cambridge, UK EMBL-EBI

Protein-protein interactions

Courtesy of

Sarah Teichmann & Jose B. Pereira-Leal

MRC Laboratory of Molecular Biology, Cambridge, UK

EMBL-EBI

Stable complex Transient Interaction

Transient Signaling Complex Rap1A – cRaf1

Interface 1310 Å2

Stable complex: homodimeric citrate synthase

Interface 4890 Å2

Hydrophobic interfaces“Hydrophilic” interfaces

Stable vs. transientprotein-protein interactions

Multi-domain protein

Stable vs. transientprotein-protein interactions

Sequence Identity Distribution for Proteins in Stable Complexes, Transient Interactions and Remaining

Proteins

0

0.1

0.2

0.3

0.4

10 20 30 40 50 60 70 80 90 100

Sequence identities

Fraction of

sequences in range

Remainder

Transient

Stable Complexes

Interface Constraints in Multi-domain Proteins

42%

43%

45%

No. proteins in S. cerevisiae:

Average identity S. cerevisiae-S. pombe:

1844

572

186

Summary

• Sequence conservation:

Stable complexes > transient > other

• Co-expression/co-regulation:

Stable complexes > transient

Using publicly available interaction data

1. There are interactors for your protein in the literature

2. There are databases of interactions where your protein may appear

3. There are homologues of your protein in the protein interaction databases

4. You can predict interactors by other means?

5. This failing, at this point you go back to the bench…

Are there know interaction partners for you pet protein?Check if:


Problems:•Low coverage•Does not include results from high throughput experiments•Gene names may not be consistent

1. Are there interactors for my protein in the literature ?

Using publicly available interaction data2. Are there databases of interactions where my protein may appear?

Some DBs:BIND, MINT (General) + organism specific databases (e.g. MIPS/CYGD)

Caution! Check: -the experimental methods used to identify the interaction (e.g. high error rate in large scale yeast-two hybrids)-check the method used to incorporate the interaction in the database(e.g. manual curation vs. literature mining using “intelligent” algorithms)


3. Are there homologues of my protein in the protein interaction databases?

We are assuming that protein interactions are conserved in evolution•Plenty of evidence that they are…•BUT, how do you define homologous/orthologous ?

Make sure that you understand the limits of such “prediction”:

two single-gene family products interact in one organisms, and they also exist as single gene-family products in another genome --> potentially good prediction

-but the original interactions was identified in a large scale Y2H, is not supported by any other observation and one of the proteins has 133 described interactors in that experiment… --> likely a false positive (you learned nothing about your protein!)

Computational Prediction of protein interactions(functional associations)

Caution:•Computational methods are good at finding functional associations•A functional association is not the same thing as a physical interaction•Since :

•we don’t know how many of the experimentally derived interactions are true/biologically significant•We don’t know how many interactions exist•Impossible to determine how good predictions REALLY are(this becomes more important as the number of predictions you make increase [automation])

Apparently no one in the world ever bothered to look at your favorite

protein… now what?

Experimental techniques

Yeast two-hybrid screens

MS analysis of tagged complexes

Correlated mRNA expression levels

Synthetic lethality Tagged protein

Protein AProtein BProtein C

Purified complex with 3 proteins3 proteins separated 3 proteins identified byby gel electrophoresis mass spectrometry





Synthetic lethality-2

-1

0

1

2

3

4

0 4 8 12 16

RPL19B

TFIIIC

Microarray timecourse of 1 ribosomal protein

mR

NA

exp

ress

ion

leve

l (ra

tio)

Time->

-2

-1

0

1

2

3

4

0 4 8 12 16

RPL19B

TFIIIC

Random relationship from 18M

-2

-1

0

1

2

3

4

0 4 8 12 16

RPL19B

RPS6B

Close relationship from 18M (2 Interacting Ribosomal Proteins)

-2

-1

0

1

2

3

4

0 4 8 12 16

RPL19B

RPS6B

RPP1A

RPL15A

?????

Predict Functional Interaction of

Unknown Member of Cluster





Synthetic lethality

How good is the data?(von Mering et al., Nature 417:399)



”We estimate that more than half of all current high-throughput interaction data are spurious”

Computational prediction of protein interactions

Tryptophan synthetase fusion

1PII

TrpC TrpF

Fused in E.coliUnfused in some other genomes(Synechocystis sp. and Thermotoga maritima.)

Enright et al (1999) Nature 409:86Marcotte et al (1999) Science 285: 751

Gene fusion events

Pellegrini et al (1999) PNAS 96: 4285


Phylogenetic profiles


Conservation of Gene neighborhood

e.g. operons in bacteriaNot really applicable to eukaryotes, except, to some extent, C.

elegans

However, there is hope for eukaryotes:-adjacent genes are frequently co-expressed (co-regulated)-co-regulated proteins are likely to be functionally associated

maybe this principle may be used for prediction of interactions


Mirror trees

Proteins that physically interact tend to co-evolve

Pazos and Valencia (2001) Protein eng. 14: 609

Computational prediction of protein interactionsPre-computed predictions: where to find them?

The Big Picture

Scale-free networks(Ravasz et al., Science 297:1551)

Some examples of systems with a scale invariant organization

QuickTime™ and aTIFF (Uncompressed) decompressorare needed to see this picture.

Broccoli

QuickTime™ and aTIFF (Uncompressed) decompressorare needed to see this picture.

World-wide web

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

(some) Food webs



Social networks



Roads

Scale free behavior in protein interaction networks:scale free or scale invariance self-similarity

Scale invariance gives insight into robustness of biological systems

Modular networks

Hierarchical networks

Identification of functional modules from protein interaction data

Graph theory

formalisms

Custering

Messy data Functional modules

Pereiral-Leal, Enright and Ouzounis (2003) Proteins in press

From functional modules to pathways

Canonical pathways

Pereiral-Leal, Enright and Ouzounis (2003) Proteins in press

Prediction of the molecular basis of protein interactions

So.. You know your two proteins interact…

do you want to know how?

Molecular basis of protein interaction

“Tree determinant residues”

Rab

RasRhoArfRan

x

REP

REP



+

_

MSA

Prediction

Experimentaltests

Pereira-Leal and Seabra (2001) J. Mol. Biol.Pereira-Leal et al (2003) Biochem. Biophys. Res. Com.


“Tree determinant residues”

Continued…

Sequence Space algorithm

Casari et al (1995) Nat. Struct. Biol 2(2)

AMAS(part of a bigger package)




In silico docking

Requires 3D structures of components

Conformational changes cannot be considered(rigid body)

Documents

Protein-protein interactions Courtesy of Sarah Teichmann & Jose B. Pereira-Leal MRC Laboratory of Molecular Biology, Cambridge, UK EMBL-EBI