1. 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) Protein-protein interactions

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. Proteins A and B form a multisubunit complex which is fused into a single protein in sequence C - thermodynamics- pieces don’t need to find each other in cell - efficiency- cell needs to produce much less of each as a result metabolic channeling- mentioned in Nature paper’s 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?) Rationale

4. 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 id’ing statistical significant scores Marcotte, et al (July 1999)

5. Trying to test accuracy of independent predictions… Method 1: shared keywords in SWISS-PROT annotations - golly gee… it’s 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 week’s papers) - wow it’s better than random too… - 5%, eight times as many as random Marcotte, et al (July 1999) (2)

6. use BLASTP to compare query genome against itself - formation of a binary matrix (1’s or 0’s 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 Enright, et al (NOV 1999)

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

8. 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 don’t work (don’t 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) High throughput y2h…

9. kinase cascades are ubiquitous in signaling pathways -MKKK => MKK => 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 A brief discussion on signal transduction