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, Enright AJ, Iliopoulos I, Kyrpides NC, Ouzounis CA. (1999). Protein interaction maps for complete genomes based on gene fusion events. Nature 402, compare briefly with yeast-2-hybrid system (y2h) Protein-protein interactions

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

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

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)

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)

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)

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…

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…

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