1. Interactions and more interactions
Rob Russell
Cell Networks
University of Heidelberg

2. Aloy & Russell Nature Rev Mol Cell Biol 2006

3. But instead of a cell dominated by randomly colliding individual protein molecules, we now know that nearly every major process in a cell is carried out by assemblies of 10 or more protein molecules
Bruce Alberts, Cell 1998

4. X Y X Y Yeast two-hybrid system Fields & Song, Nature, 340, 245, 1989
UASG
GAL1-lacZ a
Native GAL4
UASG
GAL1-lacZ X Y b
Individual hybrids with GAL4 domains
GAL4 DNA-
binding domain
GAL4 activating region
UASG
GAL1-lacZ X
GAL4 DNA-
binding domain Y c
Interaction between hybrids reconstitutes GAL4 activity
Applied to whole Yeast genome
Uetz et al, Nature, 403, 623, 2000.
Ito et al, PNAS, 98, 4569, 2001.

5. Interaction discovery I The two-hybrid system
Binary interactions:
Bait Prey
FUS3 DIG2
DIG2 FUS3
LSM2 PAT1
CKS1 CLB1
NPL4 UFD1
NPL4 CDC48
NPL4 FUC1
NPL4 SUA7
x1000s
Uetz et al, Nature, (Yeast)
Ito et al, PNAS, (Yeast)
Rain et al, Nature, (H.pylori)
Giot et al, Science, 2003 (D. melanogaster)
Li et al, Science, 2004 (C. elegans)

6. The system works, but how?
UASG
GAL1-lacZ X
GAL4 DNA-
binding domain Y c
Interaction between hybrids reconstitutes GAL4 activity
CDC28
CKS
Cyclin A
Gal-4 (N)
Gal-4 (C)
(hypothetical)
S12
L22
Native GAL4

7. Two datasets in Yeast See: Ito et al, PNAS, 2001
(comparing to Uetz et al, Nature, 2000)

8. Interaction discovery II Affinity purification (e.g. TAP/MS)
x1000s
Complexes:
Bait Co-purification partners
FUS3 DIG2 DIG1 DIG3
DIG2 FUS3 DIG2
NPL4 UFD1 CDC48 FUC1…
(Etc.)
Gavin et al, Nature, (Yeast)
Ho et al, Nature, 180, (Yeast)

9. Trying to define binary interactions from purification data
Reality
Purifications only report a collection of proteins and don’t provide any information about precisely who interacts with whom.
There are thus two models for representing binary interactions from complexes, neither of which are real.
Purification
Spoke
Matrix
Hakes et al, Comp Funct Genomics, 2006

10. Different worlds
Comparing interactions to known 3D structures shows that original yeast two-hybrid datasets contain more transient interactions, compared to affinity purification datasets that contain more stable complexes
(e.g. of 25 Uetz et al interactions with structures, 23 are transient, 2 are dedicated or stable)
Aloy & Russell, Trends Biochem Sci, 2003

11. Error rates in interaction discovery
False negatives: interactions known to occur that are missed by a screen
- To asses this one needs a reference set of positives (i.e. known interactions) among a set of proteins being screened. The fraction of these missed is the false-negative rate. Relatively simple
- normally one has a set of previously determined interactions or “gold standard”
False positives: interactions reported by a screen that are incorrect
- To assess this one needs a set of interactions that are known not to occur that are seen in a screen. Very difficult to obtain – how can you know that two proteins definitely do not interact?
- tricks include taking pairs of proteins presumed to never see each other (i.e. different cellular compartment, etc.)
Von Mering et al, Nature, 2002

12. Error rates in interaction discovery: the old view
Von Mering et al, Nature, 2002

13. Error rates in interaction discovery: the new view
Yu et al, Nature, 2002

14. Sociological bias affects the perceived performance
Interactions determined on a protein by protein basis are focused around what the investigator wants to study, and thus biased towards particular areas of biology that are hot.
High-throughput techniques are used precisely to find new interactions.
Thus using the previously determined networks as a “gold standard” is likely to be unfair.
Braun et al, Nature Methods, 2008

15. Interaction data: predictions I
Proteins in the same bacterial operon are typically functionally associated, and often physically interacting.
Groups of proteins entirely absent in one or more organisms among a closely related set are often functionally/physically associated
Proteins that are separate in some organisms and fused in others are likely interacting physically.
Aloy & Russell, Nature Rev Mol Cell Biol 2006

16. Interaction data: predictions II
Pairs of proteins homologous to pairs of proteins seen to interact in known 3D structures can interact in the same way.
Pairs of proteins containing a pair of domains often seen in interacting proteins can be used to infer interactions in proteins where interactions have not been observed.
The presence of a linear motif can indicate interactions with proteins known to bind this motif..
Aloy & Russell, Nature Rev Mol Cell Biol 2006

17. Interaction databases
Resources are very different in appearance and content
Efforts are underway to make a unified search/view, but not complete
Thus one needs currently to look at several sites to check if an interaction is known
Some are content (e.g. IntAct, MINT) others are processed and augmented (e.g. STRING) with additional predicted/inferred interactions

18. Interaction networks
Sos-1
Grb-2
RGS-4
RGS-3
Ga/q
Node
Edge

19. Biological interaction networks
Nodes:
Proteins
Genes
Chemicals
Effects(?)
Edges:
Physical interaction (e.g. yeast two-hybrid)
Co-expression (e.g. microarrays)
Same operon
Regulation of gene expression (protein to gene)
Catalysis (e.g. metabolic networks)
Node
Node
Edge

20. Interaction networks Jeong et al, Nature, 2001.
Biological networks tend to be scale free: most nodes (e.g. proteins) are connected to only a few others with a handful of “hubs” making many more interactions.
They are also “small-world” in that any pair of nodes tends to be connected via a relatively small number of intermediate nodes.

21. “Hubs” in networks Hubs are more likely to be lethal when deleted
Jeong et al, Nature, 2001
Hubs are more likely to be disordered.
Haynes et al, PLoS Comp Biol, 2006

22. p53 – the promiscuous transcription factor

23. Russell & Gibson, FEBS Lett. 2008
Linear motifs in p53
15:DNA-PK,RSK2,ATM P
18:CK1s P
NES P
20:CHK2
9:Unknown
Tetramerization
domain ( )
USP7 P S
386
MDM2
CYCLIN
DNA binding domain (95-289)
33:GSK-3s,CDK7,CDKs P P
55:MAPKs P P P
215:AuroraA P
37:DNA-PK/ATM P
371,376,378:CDK7 P
46:HIPK2 P P
315:AuroraA,CDKs
IUPred disorder prediction
392:CDK2s,CDK7,EIF2AK2
Russell & Gibson, FEBS Lett. 2008