The document discusses methods for integrating diverse evidence like genomic context, gene fusions, expression data, and protein-protein interaction screens to infer functional associations between proteins across species. It describes benchmarking different data sources against a common reference to make scores comparable and combining evidence probabilistically. The methods are applied to construct an accurate protein interaction network for the yeast cell cycle by extracting a periodically expressed subset and integrating temporal expression and interaction data.

1. STRING Cross-species integration of known and predicted protein-protein interactions Lars Juhl Jensen EMBL Heidelberg

2. STRING provides a protein network based on integration of diverse types of evidence Genomic neighborhood Species co-occurrence Gene fusions Database imports Exp. interaction data Microarray expression data Literature co-mentioning

3. Inferring functional modules from gene presence/absence patterns T Resting protuberances Protracted protuberance Cellulose © Trends Microbiol, 1999 Cell Cell wall Anchoring proteins Cellulosomes Cellulose The “Cellulosome”

4. Genomic context methods © Nature Biotechnology, 2004

5. Formalizing the phylogenetic profile method Align all proteins against all Calculate best-hit profile Join similar species by PCA Calculate PC profile distances Calibrate against KEGG maps

6. Predicting functional and physical interactions from gene fusion/fission events Find in A genes that match a the same gene in B Exclude overlapping alignments Calibrate against KEGG maps Calculate all-against-all pairwise alignments

7. Inferring functional associations from evolutionarily conserved operons Identify runs of adjacent genes with the same direction Score each gene pair based on intergenic distances Calibrate against KEGG maps Infer associations in other species

8. Score calibration against a common reference Many diverse types of evidence The quality of each is judged by very different raw scores Quality differences exist among data sets of the same type Solved by calibrating all scores against a common reference Scores are directly comparable Probabilistic scores allow evidence to be combined Requirements for the reference Must represent a compromise of the all types of evidence Broad species coverage

9. Integrating physical interaction screens Complex pull-down experiments Yeast two-hybrid data sets are inherently binary Calculate score from number of (co-)occurrences Calculate score from non-shared partners Calibrate against KEGG maps Infer associations in other species Combine evidence from experiments

10. Mining microarray expression databases Re-normalize arrays by modern method to remove biases Build expression matrix Combine similar arrays by PCA Calculate pairwise linear correlation coefficients Calibrate against KEGG maps Infer associations in other species

11. Evidence transfer based on “fuzzy orthology” Orthology transfer is tricky Correct assignment of orthology is difficult for distant species Functional equivalence cannot be guaranteed for in-paralogs These problems are addressed by our “fuzzy orthology” scheme Confidence scores for functional equivalence are calculated from all-against-all alignment Evidence is distributed across possible pairs according to confidence scores in the case of many-to-many relationships ? Source species Target species

12. Multiple evidence types from several species

13. Getting more specific – generally speaking Benchmarking against one common reference allows integration of heterogeneous data The different types of data do not all tell us about the same kind of functional associations It should be possible to assign likely interaction types from supporting evidence types The aim: to construct an accurate, qualitative models of biological systems or processes The models should be accurate even at the level of individual interactions This allows specific, testable hypotheses to be made based on high-throughput experimental data

14. Getting the parts list Yeast culture Microarrays Gene expression Expression profile 600 periodically expressed genes (with associated peak times) that encode “dynamic proteins” The parts list New analysis Cho & Spellman et al.

15. Constructing a reliable protein network The stickiness of an interaction was scored based on its local network topology We benchmarked these scores for each individual data set against a common reference Impossible interactions were eliminated based on subcellular localization data By restricting the network to a particular system the error rate is further reduced

16. Extracting a cell cycle interaction network Cell cycle microarray data Physical PPI interactions with confidence scores Expand the set of proteins to include non-periodic proteins that are strongly connected to periodic proteins Raw Data Node selection List of periodically expressed proteins with peak time Interactions Require compatible compartments and high confidence Extract cell cycle network

17. The temporal interaction network Interacting proteins are expressed close in time Two thirds of the dynamic proteins lack interactions but likely participate in transient interactions

18. Static proteins comprise a third of the interactions at all times of the cell cycle Their time of action can be predicted from interactions with dynamic proteins Static proteins play a major role

19. Cdc28p and its interaction partners

20. Just-in-time synthesis vs. just-in-time assembly Most dynamic proteins are expressed just before they are needed to carry out their function Most complexes also contain static proteins Just-in-time assembly of complexes appear to be the general principle The time of assembly is controlled synthesizing the last subunits just-in-time

21. Assembly of the pre-replication complex

22. Network as a discovery tools The network enables us to place 30+ uncharacterized proteins in a temporal interaction context Quite detailed hypotheses can be made concerning the their function The network also contains entire novel modules and complexes

23. Transcription is linked to phosphorylation A genome-wide screen identified 332 Cdc28p targets, which include 6% of all yeast proteins 8% of the static proteins 27% of the dynamic ones A similar correlation was observed with predicted PEST regions This suggests a hitherto undescribed link between transcriptional and post-translational control

24. Conclusions Genomic context methods are able to infer the function of many prokaryotic proteins from genome sequences alone Integration of large-scale experimental data allows similar predictions to be made for eukaryotic proteins Benchmarking is a prerequisite for data integration It is possible to construct highly reliable models through careful integration of high-throughput experimental data Try STRING at http://string.embl.de

25. Acknowledgments The STRING team Christian von Mering Berend Snel Martijn Huynen Daniel Jaeggi Steffen Schmidt Sean Hooper Julien Lagarde Mathilde Foglierini Peer Bork New context methods Jan Korbel Christian von Mering Peer Bork Cell cycle analysis Ulrik de Lichtenberg Thomas Skøt Jensen Anders Fausbøll Søren Brunak

26. Thank you!