Protein-protein interactions - PowerPoint PPT Presentation

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Protein-protein interactions

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Rosetta stone sequences. protein A is homologous to subsequence from protein C ... Rosetta Stone is not comprehensive (more on this later) ... – PowerPoint PPT presentation

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Title: Protein-protein interactions


1
Protein-protein interactions
  • Marcotte EM, Pellegrini M, Ng HL, Rice DW,
    Yeates TO, Eisenberg D. (1999). Detecting protein
    function and protein-protein interactions from
    genome sequences. Science 285, 751-3
  • Enright AJ, Iliopoulos I, Kyrpides NC, Ouzounis
    CA. (1999). Protein interaction maps for complete
    genomes based on gene fusion events. Nature 402,
    86-90
  • compare briefly with yeast-2-hybrid system (y2h)

2
Rosetta stone sequences
  • protein A is homologous to subsequence from
    protein C
  • protein B is homologous to subsequence from
    protein C
  • subsequences from A and B are NOT homologous to
    each other

3
Rationale
  • Proteins A and B form a multisubunit complex
    which is fused into a single protein in sequence
    C
  • thermodynamics- pieces dont need to find each
    other in cell
  • efficiency- cell needs to produce much less of
    each as a result
  • metabolic channeling- mentioned in Nature
    papers last paragraph
  • - enzymes in related biochemical pathway may form
    functional complexes
  • substrates could then pass from one enzyme to
    another directly, instead of diffusing into the
    cytosol at large
  • not clear if there is direct evidence showing
    metabolic channeling anywhere- (tryptophan
    synthase?)

4
Marcotte, et al (July 1999)
  • Method 1 use domain subsequences defined by
    Pro-Dom
  • all pairs of subsequence matches considered and
    searched for
  • two proteins which have one from each pair
    matched against
  • Method 2 sequence comparison
  • two non-overlapping local sequence alignments to
    a third protein
  • both use a minimal threshold for iding
    statistical significant scores

5
Marcotte, et al (July 1999) (2)
  • Trying to test accuracy of independent
    predictions
  • Method 1 shared keywords in SWISS-PROT
    annotations
  • - golly gee its better than random
  • - 68 vs 15 in E. coli
  • - 32 vs 15 in S. cerevisiae (yeast)
  • Method 2 Database of interacting Proteins
  • 6.4 of applicable sequences are also in the
    database
  • Rosetta Stone is not comprehensive (more on this
    later)
  • Method 3 phylogenetic profiles (see last weeks
    papers)
  • wow its better than random too
  • 5, eight times as many as random

6
Enright, et al (NOV 1999)
  • use BLASTP to compare query genome against
    itself
  • formation of a binary matrix (1s or 0s in each
    entry)
  • - symmetrification with Smith-Waterman (local
    alignments
  • BLASTP to compare query vs reference genome
  • get a second binary matrix
  • all pairs of query proteins similar to a
    reference protein
  • Z-score to test for significance of alignments
  • - set an arbitrary Z-score cutoff to determine
    coverage/accuracy

7
And now for something completely different
  • y2h gt yeast-2-hybrid detection of
    protein-protein interactions
  • - Fields, S. and Song, O. Nature 1989, if youre
    curious.
  • transcription factors composed of two separable
    domains
  • - DNA-binding and transcriptional-complex
    recruitment

8
High throughput y2h
  • is only high-throughput if you are not a
    postdoc.
  • lots of transformations and assays
  • fortunately you only have to transform once.
  • has major problems with false negatives
  • integral membrane proteins dont work (dont
    fold properly)
  • post-translational modifications
  • require nuclear localization
  • misfolding or steric hindrances
  • transcription factors (?)
  • also has significant false positives also- not
    sure why
  • - Uetz et al (2000) had 20 of interactions
    screen twice
  • other validation methods
  • genetic techniques
  • biochemical (coIP, affinity chromatography, mass
    spec, etc)

9
A brief discussion on signal transduction
  • kinase cascades are ubiquitous in signaling
    pathways
  • MKKK gt MKK gt MAPK
  • kinase cascades are ubiquitous in signaling
    pathways
  • interesting when looking at given Rosetta Stone
    examples
  • will kinase cascades be detected?
  • SH2 and SH3 domains (Marcotte, et al)
  • SH2 bind phosphorylated tyrosine residues, SH3
    bind proline-rich sequences
  • both are common motifs but have
    sequence-specific affinity
  • Kinase cascades/signaling pathways are sometimes
    Y2H targets
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