Proteome and interactome

1. Bioinformatics Proteome and interactome Jacques.van.Helden@ulb.ac.be

2. Contents • Protein-protein interactions • Two-hybrid assays • Mass spectrometry • Cellular localization of proteins • GFP tags • Protein-DNA interactions • ChIP-on-chip

3. Functional genomics Two-hybrid assays Jacques.van.Helden@ulb.ac.be

4. Activation RNA pol DNA-binding ORF A Activation DNA-binding ORF B A A A RNA pol RNA pol B B B Two-hybrid method Transcription factor Hybrid constructions Bait Prey Interaction  reporter gene is expressed No interaction  reporter gene is not expressed Prey Prey Bait Bait

5. Two-hybrid Uetz et al. (2000). Nature 403: 623-631 Ito et al. (2001) PNAS 98: 4569-4574

6. Ito et al. (2001) PNAS 98: 4569-4574 Comparison of the results • When the second “comprehensive” analysis was published, the overlap between thee results obtained in the two independent studies was surprisingly low. • How to interpret this ? • Problem of coverage ? Each study would only represent a fraction of what remains to be discovered. • Problem of noise ? Either or both studies might contain a large number of false positives. • Differences in experimental conditions ?

7. Connectivity in protein interaction networks • Jeong et al (2001) calculate connectivity in the protein interaction network revealed by the two-hybrid analysis of Uetz and co-workers. • The connectivity follows a power law: • most proteins have a few connections; • a few proteins are highly connected • Highly connected proteins correspond to essential proteins. Jeong, H., S.P. Mason, A.L. Barabasi, and Z.N. Oltvai. 2001. Lethality and centrality in protein networks. Nature411: 41-42.

8. Functional genomics Mass-spectrometry Jacques.van.Helden@ulb.ac.be

9. Y ORF tag Y C B Y A D E Isolation of protein complexes 1. Construction of a bank of TAG-fused ORFs 2. Expression of the tagged baits in yeast tagged bait + All cellular proteins,… 3. Cell lysis 4. Affinity purification anti-tag epitope Other proteins,… Slide from Nicolas Simonis

10. B A E D C Y E C B C B Y A D E Mass spectrometry - Protein identification 1 dimension SDS-PAGE isolation Mass spectrometry B = YLR258w = YER133w = YER054c = YPR184w = YKL085w = YPR160w A C D E Y Slide from Nicolas Simonis

11. Protein complexes Gavin et al. (1999). Nature 415: 141-147 Tandem Affinity Purification (TAP) CELLZOME: 232 complexes Ho et al. (1999). Nature 415: 180-183 High-throughput mass-spectrometric protein complex identification (HMS-PCI) MDS proteomics 493 complexes

12. Network of complexes Gavin et al. (1999). Nature 415: 141-147

13. Functional genomics Assessment of interactome data Jacques.van.Helden@ulb.ac.be

14. Assessment of interactome data von Mering et al (2002). Nature.

15. Comparison of large-scale interaction data • von Mering et al (2002) compared the results from • Two-hybrid assays • Mass spectrometry (TAP and HMS-PCI) • Co-expression in microarray experiments • Synthetic lethality • Comparative genomics (conservation of operons, phylogenetic profiles, and gene fusion) • Among 80,000 interactions, no more than 2,400 are supported by two different methods. • Each method is more specifically related to some • functional classes • cellular location Reference: von Mering et al. (2002). Nature 750

16. Comparison of pairs of interacting proteins with functional classes von Mering et al (2002). Nature 750.

17. Validation with annotated complexes • von Mering et al (2002) collected information on experimentally proven physical protein-protein interactions, and measured the coverage and positive predictive value of each predictive method • Coverage • fraction of reference set covered by the data. • Positive predictive value • Fraction of data confirmed by reference set. • (Note: they call this “accuracy”, but this term is usually not used in this way) • Beware: the scale is logarithmic ! • This enforces the differences in the lower part of the percentages (0-10), but “compresses” the values between 10 and 50, which gives a false impression of good accuracy. von Mering et al (2002). Nature.

18. Bioinformatics Cellular localization of proteins Jacques.van.Helden@ulb.ac.be

19. Nature (2003) 425: 686-691 4156 proteins detected by fluorescence microscopy analysis Slide adapted from Bruno André

20. Global analysis of protein localization • This analysis allowed to obtain information for thousands of proteins for which the cellular localization was previously unknown. Slide adapted from Bruno André

21. Localisation and ORF function • For historical reasons, the yeast genome is “over-annotated”. • The method used for predicting genes from genome sequences included many false positives, especially among short predicted ORFs. • Most of the questionable ORFs were unobserved in the global localization analysis. These mainly correspond to short ORFs. Source: Bruno André

22. Functional genomics Protein-DNA interactionsChIP-on-chip technology Jacques.van.Helden@ulb.ac.be

23. The ChIP-on-chip method • Chromatin Immuno-precipitation (ChIP) • Tagging of a transcription factor of interest with a protein fragment recognized by some antibody. • Immobilization of protein-DNA interactions with a fixative agent. • DNA fragmentation by ultrasonication. • Precipitation of the DNA-protein complexes. • Un-binding of the DNA-protein bounds. • Measurement of DNA enrichment. • Two extracts are co-hybridized on a microarray (chip),where each spot contains one DNA fragment where a factor is likely to bind (e.g. an intergenic region, or a smaller fragment).. • For the yeast S.cerevisiae, chips have been designed with all the intergenic regions (6000 regions, avg. 500bp/region) • Recent technology allows to spot 3e+5 300bp DNA fragments on a single slide. • The first extract (labelled in red) is enriched in DNA fragments bound to the tagged transcription factor. • The second extract (labelled in green) has not been enriched. • The log-ratio between red and green channels indicate the enrichment for each intergenic region.

24. Lee et al (2002) • In 2002, Lee et al publish a systematic characterization of the binding regions of 106 yeast transcription factors. Lee et al. 2002. Science298: 799-804.