1. Protein Interactions Michel Dumontier, Ph.D. Carleton University
June 17, 2005
Protein Interactions
Michel Dumontier, Ph.D.
Carleton University
Lecture 4.1
(c) 2005 CGDN

2. Outline Protein interactions Discovery Storage Experimental
Lecture 4.1

3. Molecular InteractionsB A
Between two molecular objects
DNA, RNA, gene, protein, molecular complex, small molecule, photon
Binding Sites
Under some Experimental Condition
With a particular Cellular Location
Possibly having a Chemical Action
Lecture 4.1

4. Interaction Discovery
Michel Dumontier
Interaction Discovery
June 17, 2005
Databases
Fully electronic
Easily computer readable
Literature
Increasingly electronic
Human readable
Biologist’s brains
Richest data source
Limited bandwidth access
Experiments
Basis for models
Lecture 4.1
(c) 2005 CGDN

5. Yeast Two Hybrid Assay
The two-hybrid system is a molecular genetic tool which facilitates the study of protein-protein interactions.
If two proteins interact, then a reporter gene is transcriptionally activated.
e.g. gal1-lacZ - the beta-galactosidase gene
A colour reaction can be seen on specific media.
You can use this to
Study the interaction between two proteins which you expect to interact
Find proteins (prey) which interact with a protein you have already (bait).
Lecture 4.1

6. Two-hybrid assay 1. 3. 2. 4. B A SNF4 SNF1 GAL4-DBD
Michel Dumontier
Two-hybrid assay
June 17, 2005
SNF4 1. B
SNF1 A 3. 2.
GAL4-DBD
Transcription activation domain
UASG 4.
Fields S. Song O.
Nature Jul 20;340(6230): PMID:
GAL1
Allows growth on galactose
Lecture 4.1
(c) 2005 CGDN

7. Some Two-hybrid caveats
Michel Dumontier
Some Two-hybrid caveats
June 17, 2005 1. A 3. 2. 4.
Does the DNA Binding Domain
fusion have activity by itself?
Lecture 4.1
(c) 2005 CGDN

8. Some Two-hybrid caveats
Michel Dumontier
Some Two-hybrid caveats
June 17, 2005 1. C B A 3. 2. 4.
Is the ‘interaction’ mediated
by some other protein?
Lecture 4.1
(c) 2005 CGDN

9. Some Two-hybrid questions
Michel Dumontier
Some Two-hybrid questions
June 17, 2005 1. B A 3. 2.
Are the proteins expresssed? Are they over-expressed? Are they in-frame? Are the interacting domains defined? Was the observation reproducible? Was the strength of interaction significant? Was another method used to back-up the conclusion?
Are the two proteins from the same compartment? 4.
Lecture 4.1
(c) 2005 CGDN

10. Affinity purification
Michel Dumontier
Affinity purification
June 17, 2005 A
this molecule will bind
the ‘tag’.
tag modification (e.g. HA/GST/His)
Protein of interest
Lecture 4.1
(c) 2005 CGDN

11. Affinity purification
Michel Dumontier
Affinity purification
June 17, 2005
the cell A
Lecture 4.1
(c) 2005 CGDN

12. Affinity purification
Michel Dumontier
Affinity purification
June 17, 2005
lots of other untagged proteins
the cell A B
naturally binding protein
Lecture 4.1
(c) 2005 CGDN

13. Affinity purification
Michel Dumontier
Affinity purification
June 17, 2005
Ruptured membranes A B
cell extract
Lecture 4.1
(c) 2005 CGDN

14. Affinity purification
Michel Dumontier
Affinity purification
June 17, 2005 A B
untagged proteins go through fastest (flow-through)
Lecture 4.1
(c) 2005 CGDN

15. Affinity purification
Michel Dumontier
Affinity purification
June 17, 2005 A B
tagged complexes
are slower and come out
later (eluate)
Lecture 4.1
(c) 2005 CGDN

16. Some affinity purification questions
Michel Dumontier
June 17, 2005
Some affinity purification questions
Is the bait protein expressed and in frame?
Is the bait protein observed? Is the bait protein over-expressed? Are the interacting domains defined? Was the observation reproducible? Was the interactor found in the background? Was the strength of interaction significant?
Was the interaction saturable?
Was the interactor stoichiometric with the bait protein? Was another method used to back-up the conclusion? Was tandem-affinity purification (TAP) used?
Was the interaction shown using an extract or a purified protein?
Is the inverse interaction observable?
Are the two proteins from the same compartment?
Are the two proteins known to be involved in the same process?
Is the interactor likely to be physiologically significant? A B
Lecture 4.1
(c) 2005 CGDN

17. Some affinity purification caveats
Michel Dumontier
June 17, 2005
Some affinity purification caveats
First and most importantly, this is only a representation of the observation.
You can only tell what proteins are in the eluate;
you can’t tell how they are connected to one another.
If there is only one other protein present (B), then its likely that
A and B are directly interacting.
But, what if I told you that two other proteins (B and C) were
present along with A…. A B A C B
Lecture 4.1
(c) 2005 CGDN

18. Complexes with unknown topology
Michel Dumontier
June 17, 2005
Complexes with unknown topology A A A B C B C B C
Which of these models is correct?
The complex described by this experimental result is
said to have an Unknown Topology.
Lecture 4.1
(c) 2005 CGDN

19. Complexes with unknown stoichiometry
Michel Dumontier
June 17, 2005
Complexes with unknown stoichiometry A A B C
Here’s another possibility?
The complex described by this experimental result is
also said to have Unknown Stoichiometry.
Lecture 4.1
(c) 2005 CGDN

20. High-throughput Mass Spectrometric Protein Complex Identification (HMS-PCI)
Michel Dumontier
June 17, 2005
Mike Tyers, SLRI
Ste12
Ho et al. Nature Jan 10;415(6868):180-3
Lecture 4.1
(c) 2005 CGDN

21. Michel Dumontier
June 17, 2005
Lecture 4.1
(c) 2005 CGDN

22. Synthetic Genetic Interactions
Michel Dumontier
Synthetic Genetic Interactions
June 17, 2005
Synthetic genetic interactions (lethal, slow growth)
Mate two mutants without phenotypes to get a daughter cell with a phenotype
Synthetic lethal (SL), slow growth
robotic mating using the yeast deletion library
Genetic interactions provide functional data on protein interactions or redundant genes
About 23% of known SLs ( YPD+MIPS) are known protein interactions in yeast
Tong et al. Science Dec 14;294(5550):2364-8
Lecture 4.1
(c) 2005 CGDN

23. Working overtime Charlie Boone’s Robots
Michel Dumontier
Working overtime Charlie Boone’s Robots
June 17, 2005
Lecture 4.1
(c) 2005 CGDN

24. Synthetic Genetic Interactions in Yeast
Cell Polarity
Cell Wall Maintenance
Cell Structure
Mitosis
Chromosome Structure
DNA Synthesis
DNA Repair
Unknown
Others
Michel Dumontier
Synthetic Genetic Interactions in Yeast
June 17, 2005
Lecture 4.1
Tong, Boone
(c) 2005 CGDN

25. SGA Synthetic Genetic Interaction Network 2004
Michel Dumontier
June 17, 2005
~1000 Genes ~4000 Interactions 132 SGA Screens
Lecture 4.1
Tong, Boone, Science, Feb 2004
(c) 2005 CGDN

26. A measure of confidence?
Michel Dumontier
A measure of confidence?
June 17, 2005
How do you know if the interaction really exists?
Each method has its advantages and disadvantages.
Be aware of systematic errors (i.e. tag effects)
Be aware of contaminating proteins.
Each method observes interactions from a slightly different experimental condition.
Support from many different sources is certainly better than just one.
Lecture 4.1
(c) 2005 CGDN

27. Outline Molecular interactions Discovery Storage Data Mining Databases
File Formats
Data Mining
Lecture 4.1

28. Interaction/Pathway Databases
Michel Dumontier
Interaction/Pathway Databases
June 17, 2005
Arguably the most accessible data source, but...
Varied formats, representation, coverage
Pathway data extremely difficult to combine and use
Pathway Resource List (
Lecture 4.1
(c) 2005 CGDN

29.
A free, open-source database for archiving and exchanging molecular assembly information. BIND is managed by the Blueprint Initiative at Mount Sinai Hospital in Toronto.
The database contains
Interactions/Reactions
Molecular complexes
Pathways
BIND has an extensive data model, GNU software tools and is based on the NCBI toolkit; extended recently to XML/Java
The ~ BIND records are curated and validated.
Bader GD, Betel D, Hogue CW. (2003) BIND: the Biomolecular Interaction Network Database. Nucleic Acids Res. 31(1): PMID:
Lecture 4.1

30. BIND Interaction Types
Lecture 4.1

31. Interaction Experimental Evidence in BIND
Remaining 1%
Lecture 4.1

32. Lecture 4.1

33. Lecture 4.1

34. 55 Identifier Searches Supported!
Lecture 4.1

35. Lecture 4.1

36. GI Pair - CSV Export
Lecture 4.1

37. BIND Record Header BIND record identifier Description & Division
Publications that support or dispute interaction
Export Options
Network Visualization
Lecture 4.1

38. BIND Record View
Lecture 4.1

39. BIND Record View The Interacting Molecules (A and B)
Main identifier: GI
Organism
Cross-references and aliases
Gene Ontology terms
Proteoglyphs
Graphical representations of domain and protein structure.
Ontoglyphs
Graphical representations of molecule function, localization and binding
Lecture 4.1

40. Gene Ontology Functional protein annotation
Michel Dumontier
Gene Ontology
June 17, 2005
Functional protein annotation
Controlled vocabulary for protein function and localization
Molecular function e.g. DNA helicase
Biological process e.g. mitosis
Cellular Component e.g. nucleus
Thousands of terms…
Lecture 4.1
(c) 2005 CGDN

41. Lecture 4.1

42. Lecture 4.1

43. Ontoglyph Summary View
Michel Dumontier
Ontoglyph Summary View
June 17, 2005
Lecture 4.1
(c) 2005 CGDN

44. Ontoglyph Filtering
Lecture 4.1

45. Lecture 4.1

46. Lecture 4.1

47. Other Interaction Databases
DIP
MINT
MIPS
IntAct – EBI’s interaction database
Human Protein Interaction Database
TRANSFAC – transcription factors
Lecture 4.1

48. Information Exchange Software Database User With Data
Michel Dumontier
Information Exchange
June 17, 2005
Database
Software
User
With Data
Exchange Format
>100 DBs and tools
Tower of Babel
Lecture 4.1
(c) 2005 CGDN

49. Data Exchange File Formats
BIND
Peer reviewed but closed process (Spec v3.1)
ASN.1 or XML DTD/Schema
PSI-MI
Peer reviewed, HUPO community standard
Widely adopted
BioPax
Community schema (Sloan Kettering, BioPathways Consortium)
XML Schema, OWL, Protégé and GKB
SBML
Widely adopted for representing models of biochemical reaction networks
Lecture 4.1

50. BIND
ASN.1 (text)
XML
Flat File
Lecture 4.1

51. PSI level 2
Lecture 4.1

52. PSI Record Format
Lecture 4.1

53. Michel Dumontier
BioPAX
June 17, 2005
Represent:
Metabolic pathways
Signaling pathways
Protein-protein, molecular interactions
Gene regulatory pathways
Genetic interactions
Accommodate representations used in existing databases such as BioCyc, BIND, WIT, aMAZE, KEGG, Reactome, etc.
Community effort (open meetings)
Lecture 4.1
(c) 2005 CGDN

54. Conclusion Many experimental techniques to generate interaction data
Interaction databases like BIND are a great resource for building up interaction networks into pathways
Common standards for file formats imperative for making use of all this data!
Lecture 4.1