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Protein-protein interaction. Data sets and comparisons Roland Krause. Outline. Important data sets of protein-protein interaction Comparison of data sets in yeast Overview of current methods Suggested modifications. Biochemical purifications Yeast Gavin et al. (2002) Nature

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protein protein interaction

Protein-protein interaction

Data sets and comparisons

Roland Krause

outline
Outline
  • Important data sets of protein-protein interaction
  • Comparison of data sets in yeast
  • Overview of current methods
  • Suggested modifications
comparison of different data sets
Biochemical purifications

Yeast

Gavin et al. (2002) Nature

Ho et al. (2002) (HMS-PCI) Nature

Krogan et al. (2004) Mol Cell

Yeast-two hybrid

Ito et al. PNAS (2000, 2001), Uetz et al. Nature (2000) (yeast)

Giot et al. Science (2004) (Drosophila)

Li S., et al (2004) Science (C. Elegans)

Synthetic lethals

Tong (2004)

In silico predictions

Methods

Co-occurrence (Phylogenetic profiling)

Neighborhood (Operon)

Fusion (Rosetta)

Non-orthologous replacement

See review by Osterman and Overbeek in Curr Opin Chem Biol. (2003)

mRNA-co-expression

Eisen et al., PNAS (1998) Marcotte Nature (1999)

Comparison of different data sets
reference data bases
Interactions

MIPS

DIP

YPD

Intact (EBI)

BIND/ Blueprint

GRID

MINT

Prediction server

Predictome (Boston U)

Plex (UTexas)

STRING (EMBL)

Protein complexes

MIPS

YPD

Reference data bases
comparison of protein protein interaction screens

Comparison of protein-protein interaction screens

Differences between individual methods and reference sets

interaction density

energy production

aminoacid metabolism

other metabolism

translation

transcription

transcriptional control

protein fate

cellular organization

transport and sensing

stress and defense

genome maintenance

cellular fate/organization

uncharacterized

Interaction density

E

G

0

10

M

Interaction pro

1000 possible

P

T

B

F

O

A

R

D

C

U

E

G

M

P

T

B

F

O

A

R

D

C

U

Interaction density
conclusions
Conclusions
  • The overlap between the individual methods is surprisingly small
  • Different methods complement each other
  • Individual methods are not exhausted
  • Single experimental methods can be as reliable as combined sets
  • Integration

[ Bader, G. and Hogue, C. (2002) Nat. Biot.]

[Kemmeren H., et al. (2002) Mol. Cell]

[Von Mering C., Krause, R., et al. (2002) Nature]

[Edwards et al. (2002) Trends Genet. ]

motivation
Motivation
  • Modules are an important aspect of networks
    • Delineation of functional groups/ processes
    • Transfer of information
    • Structural principle
    • Deconvolute the network
proposal for the algorithms work group
Proposal for the algorithms work group
  • Identification of protein complexes in interaction networks to obtain shared components
    • Modules/complexes received some attention in the last year
    • Current methods are not satisfactory to several questions posed
      • Coverage
      • Boundaries
      • Shared components
early work
Early work
  • Snel et al (2000) PNAS – Modules of predicted functional interaction networks
  • Date et al (2003) Nat Biot. – Novel functional clusters
  • Von Mering et al (2003) PNAS
finding protein complexes
Finding protein complexes
  • MCODE
    • Bader and Hogue, BMC Bioinformatics (2003)
    • Vertex weighting, dense regions are identified as clusters
  • Spirin
    • Spirin and Mirny, PNAS (2003)
    • Monte Carlo simulation
  • Clustering purifications
    • Krause et al, Bioinformatics (2003)
  • Bayesian approach
    • Sharan et al. (2004)
suggestion
Suggestion
  • Use weighted edges
    • Repetition
    • Estimate false negatives/ false positives
  • Use of all available experimental information
    • Bait-prey direction
    • Existence of disproving experimental information (such as failure by reversal in Y2H or complex purification)
  • Novel algorithms (rather that k-core or cliques)