Protein protein interaction

1. Protein-protein
Protein-protein
interactions
interactions

2. Outline
 Why protein-protein interactions?.
 Experimental methods for discovering PPIs:
• Yeast-two-hybrid
• AP-MS
 PPIs databases:
• DIP
• MIPs
 Computational prediction of PPIs
• Phylogenetic based method
• Expression correlation based method
• STRING (EMBL)

3. Why protein-protein interactions (PPI)?
Gene is the basic
unit of heredity.
Genomes are
availabe.
genome Proteome （蛋白质组） interactome
Proteins, the working
molecules of a cell,
carry out many
biological activities
Proteins function by
interacting with other
proteins.

4. Why protein-protein interactions (PPI)?
PPIs are involved in many biological processes:
 Signal transduction
 Protein complexes or molecular machinery
 Protein carrier
 Protein modifications (phosphorylation)
 …
PPIs help to decipher the molecular mechanisms underlying the
biological functions, and enhance the approaches for drug discovery

5. High throughput experimental methods
for discovering PPIs
 Yeast-two-hybrid (Y2H ，）
 Ito T. et al., 2001
 Uetz P. et al., 2000
 Affinity purification followed by mass
spectrometry (AP-MS ，）
 Gavin AC et al., 2002, 2006
 Ho Y. et al., 2002
 Krogan NJ et al., 2006

6. Y2H experiments
Idea:
 Bait & (prey) protein is fused
to the binding domain
(activation domain).
 If bait and prey proteins
interact, the transcription of
the reporter gene is initiated.
 High throughput screening the
interactions between the bait
and the prey library.
 In yeast nucleus

7. AP-MS experiments
 Fuse [a TAP tag consisting of protA (IgG binding
peptides) and calmodulin binding peptide (CBP)
separated by TEV protease cleavage site] to the
target protein
 After the first AP step using an IgG matrix, many
contaminants are eliminated.
 In the second AP step, CBP binds tightly to
calmodulin coated beads. After washing which
removes remained contaminants and the TEV
protease, the bound meterial is released under mild
condition with EGTA
 Proteins are identified by mass spectrometry

8. PPIs Databases.
 DIP- Database of Interacting Protein.
(http://dip.doe-mbi.ucla.edu/ )
 MIPS-Munich Information center for Protein
Sequences.
(http://mips.gsf.de/ )

9. DIP
 Protein function
 Protein-protein relationship
 Evolution of protein-protein interaction
 The network of interacting proteins
 Unknown protein-protein interaction
 The best interaction conditions
•PPIs databases

10. DIP-Statistics
 Number of proteins: 20731
 Number of organisms: 274
 Number of interactions: 57687
 Number of distinct experiments describing an interaction:
65735
 Number of data sources (articles): 3915

11. DIP-Searching information

12. Find information about your protein

13. DIP Node (DIP:1143N)

14. Graph of PPIs around DIP:1143N
 Nodes are proteins
 Edges are PPIs
 The center node is DIP:1143N
 Edge width encodes the number
of independent experiments
identyfying the interaction.
 Green (red) is used to draw core
(unverified) interactions.
 Click on each node (edge) to
know more about the protein
(interaction).

15. List of interacting partners of
DIP:1143N

16. MIPS
Services:
 Genomes
 Databanks retrieval systems
 Analysis tools
 Expression analysis
 Protein protein interactions
 MPact: the MIPS protein interaction resource on yeast.
 MPPI: the MIPS Mammalian Protein-Protein Interaction Database.
 Protein complexes
 Mammalian protein complexes at MIPS

17. MPact: the MIPS protein interaction
resource on yeast
Query all PPIs of a yeast protein

18. MPact: the MIPS protein interaction
resource on yeast

19. MPact: Interaction Visualization

20. MPPI: the MIPS Mammalian Protein-Protein
Interaction Database
Query PPIs of a mamalian protein. You can use x-ref, for example Uniprot
accession number.

21. Results for PPI search
In short format

22. Results for PPI search
In full format

23. Mammalian protein complexes at MIPS

25. Search information of complexes

26. Assessment of large–scale data sets of
PPIs
 The overlap between the individual methods is
surprisingly small
 The methods may not have reached saturation.
 Many of the methods may produce a significant
fraction of false positives.
 Some methods may have difficulties for certain
types of interactions
Von Mering C, et al. Nature, (2002) 417 : 399–403

27. Functional biases
 AP-MS discovers few PPIs involved in transport and sensing
 Y2H detects few PPIs involved in translation.
 Different methods complement each other
Von Mering C, et al. Nature, (2002) 417 : 399–403

28. Computational methods of prediction
Current approaches:
 Genomic methods
 Biological context methods
 Structural based methods

29. Genomic methods
 Protein a and b whose genes are close in different genomes are
predicted to interact.
 Protein a and b are predicted to interact if they combine (fuse) to
form one protein in another organism.
 Protein a and c are predicted to interact if they have similar
phylogenetic profiles.

30. Biological context methods
 Gene expression: Two protein whose genes exhibit
very similar patterns of expression across multiple
states or experiments may then be considered
candidates for functional association and posibly
direct physical interaction.
 GO annotations: two interacting proteins likely have
the same GO term annotations.
 Machine learning techniques are adopted for PPI
classification by intergrating all known information.

31. STRING: Search Tool for the Retrieval of
Interacting Genes/Proteins
 A database of known and predicted protein interactions
 Direct (physical) and indirect (functional) associations
 The database currently covers 2,483,276 proteins from 630
organisms
 Derived from these sources:
 Supported by

32. Searching information
Query infomation via protein names or protein sequences.

33. Graph of PPIs
 Nodes are proteins
 Lines with color is an evidence of
interaction between two proteins.
The color encodes the method
used to detect the interaction.
 Click on each node to get the
information of the corresponding
protein.
 Click on each edge to get
information of the interaction
between two proteins.

34. List of predicted partners
 Partners with discription and confidence score.
 Choose different types of views to see more detail

35. Neighborhood View
 The red block is the queried protein and others are its neighbors in
organisms. Click on the blocks to obtain the information about
corresponding proteins.
 The close organisms show the similar protein neighborhood patterns.
 Help to find out the close genes/proteins in genomic region.

36. Occurence Views
 Represents phylogenetic profiles of proteins.
 Color of the boxes indicates the sequence similarity between the proteins and
their homologus protein in the organisms.
 The size of box shows how many members in the family representing the
reported sequence similarity.
 Click on each box to see the sequence alignment.

37. Gene Fusion View
 This view shows the individual gene fusion events per species
 Two different colored boxes next to each other indicate a fusion
event.
 Hovering above a region in a gene gives the gene name; clicking on
a gene gives more detailed information

38. References
 Ito T et.al: A comprehensive two-hybrid analysis to explore the yeast protein
interactome. Proc. Natl Acad. Sci. USA 2001, 98:4569-4574.
 Uetz P et. al: A comprehensive analysis protein-protein interactions in
Saccharomyces cerevisiae. Nature 2000, 403:623-627.
 Gavin AC et.al: Functional organization of the yeast proteome by systematic
analysis of protein complexes. Nature 2002, 415:141-147.
 Gavin AC et.al: Proteome survey reveals modularity of the yeast cell
machinery. Nature 2006, 440:631-636.
 Ho Y et.al: Systematic identification of protein complexes in Saccharomyces
cerevisiae by mass spectrometry. Nature 2002, 415:180-183.
 Von Mering C et.al: Comparative assessment of large-scale data sets of
protein-protein interactions. Nature 2002, 417:399-403.