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Redefining Nodes and Edges: Relating 3D Structures to Yeast Protein Networks Provides Insights into their Evolution. 2006 Yeast Genetics Meeting Philip.

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Presentation on theme: "Redefining Nodes and Edges: Relating 3D Structures to Yeast Protein Networks Provides Insights into their Evolution. 2006 Yeast Genetics Meeting Philip."— Presentation transcript:

1 Redefining Nodes and Edges: Relating 3D Structures to Yeast Protein Networks Provides Insights into their Evolution Yeast Genetics Meeting Philip M. Kim, Ph.D., Yale University Princeton, NJ July 27th, 2006

2 060727_Yeast_Meet_Talk_PMK 1 MOTIVATION ≠ A B1-4 Cdk/cyclin complex Part of the RNA-pol complex ILLUSTRATIVE A B1 B2 B3 B4 Network perspective: Structural biology perspective: = There remains a rich source of knowledge unmined by network theorists!

3 060727_Yeast_Meet_Talk_PMK 2 OUTLINE Interaction Networks and their properties Network properties revisited A 3-D structural point of view Conclusions

4 060727_Yeast_Meet_Talk_PMK 3 OUTLINE Interaction Networks and their properties Network properties revisited A 3-D structural point of view Conclusions

5 060727_Yeast_Meet_Talk_PMK 4 PROTEIN INTERACTION NETWORKS IN YEAST Source: Gavin et al. Nature (2002), Uetz et al. Nature (2000), Cytoscape and DIP Determined by: –Large-scale Yeast-two-hydrid –TAP-Tagging –Literature curation Currently over 20,000 unique interactions available in yeast Spawned a field of computational “graph theory” analyses that view proteins as “nodes” and interactions as “edges” A snapshot of the current interactomeDescription and methodologies ILLUSTRATIVE DIP (Database of interacting Proteins)

6 060727_Yeast_Meet_Talk_PMK 5 TINY GLOSSARY: DEGREE AND HUBS C: Degree = 1 A: Degree = 5 A is a “Hub”* *The definition of hubs is somewhat arbitrary, usually a cutoff is used Source: PMK

7 060727_Yeast_Meet_Talk_PMK 6 HUBS TEND TO BE IMPORTANT PROTEINS, THEY ARE MORE LIKELY TO BE ESSENTIAL PROTEINS AND TEND TO BE MORE CONSERVED Source: Jeong et al. Nature (2001), Yu et al. TiG (2004) and Fraser et al. Science (2002) By now it is well documented that proteins with a large degree tend to be essential proteins in yeast. (“Hubs are essential”) Likewise, it has been found that hubs tend to evolve more slowly than other proteins (“Hubs are slower evolving”) There is some controversy regarding this relationship

8 060727_Yeast_Meet_Talk_PMK 7 THERE IS A RELATIONSHIP BETWEEN NETWORK TOPOLOGY AND GENE EXPRESSION DYNAMICS Source: Han et al. Nature (2004) and Yu*, Kim* et al. (Submitted) Frequency Co-expression correlation

9 060727_Yeast_Meet_Talk_PMK 8 OUTLINE Interaction Networks and their properties Network properties revisited A 3-D structural point of view Conclusions

10 060727_Yeast_Meet_Talk_PMK 9 THERE IS A PROBLEM WITH SCALE-FREENESS AND REALLY BIG HUBS IN INTERACTION NETWORKS Source: DIP, Institut fuer Festkoerperchemie (Univ. Tuebingen) A really big hub (>200 Interactions) Gedankenexperiment How many maximum neighbors can a protein have? Clearly, a protein is very unlikely to have >200 simultaneous interactors. Some of the >200 are most likely false positives Some others are going to be mutually exclusive interactors (i.e. binding to the same interface). Conclusion There appears to be an obvious discrepancy between >200 and 12. ILLUSTRATIVE Wouldn’t it be great to be able to see the different binding interfaces?

11 060727_Yeast_Meet_Talk_PMK 10 UTILIZING PROTEIN CRYSTAL STRUCTURES, WE CAN DISTINGUISH THE DIFFERENT BINDING INTERFACES *Many redundant structures Source: PMK ILLUSTRATIVE Interactome Use a high-confidence filter Map Pfam domains to all proteins in the interactome Distinguish interfaces Annotate interactions with available structures, discard all others PDB Homology mapping of Pfam domains to all structures of interactions ~10000 Structures of interactions* ~20000 interactions

12 060727_Yeast_Meet_Talk_PMK 11 THAT IS HOW THE RESULTING NETWORK LOOKS LIKE Source: PDB, Pfam, iPfam and PMK Represents a “very high confidence” network Total of 873 nodes and 1269 interactions, each of which is structurally characterized 438 interactions are classified as mutually exclusive and 831 as simultaneously possible While much smaller than DIP, it is of similar size as other high- confidence datasets The Structural Interaction Dataset (SID)Properties

13 060727_Yeast_Meet_Talk_PMK 12 OUTLINE Interaction Networks and their properties Network properties revisited A 3-D structural point of view Conclusions

14 060727_Yeast_Meet_Talk_PMK 13 THERE DO NOT APPEAR TO BE THE KINDS OF REALLY BIG HUBS AS SEEN BEFORE – IS THE TOPOLOGY STILL SCALE-FREE? Source: PMK With the maximum number of interactions at 13, there are no “really big hubs” in this network Note that in other high-confidence datasets (or similar size), there are still proteins with a much higher degree The degree distribution appears to top out much earlier and less scale free than that of other networks Degree distributionProperties

15 060727_Yeast_Meet_Talk_PMK 14 Entire genome All proteins In our dataset Single-interface hubs only Multi-interface hubs only Percentage of essential proteins IT’S REALLY ONLY THE MULTI-INTERFACE HUBS THAT ARE SIGNIFICANTLY MORE LIKELY TO BE ESSENTIAL Source: PMK

16 060727_Yeast_Meet_Talk_PMK 15 All proteins In our dataset Single-interface hubs only Multi-interface hubs only Expression Correlation Expression correlation DATE-HUBS AND PARTY-HUBS ARE REALLY SINGLE-INTERFACE AND MULTI-INTERFACE HUBS Source: Han et al. Nature (2004) and PMK Frequency

17 060727_Yeast_Meet_Talk_PMK 16 AND ONLY MULTI-INTERFACE PROTEINS ARE EVOLVING SLOWER, SINGLE-INTERFACE HUBS DO NOT Entire genome All proteins In our dataset Single-interface hubs only Multi-interface hubs only Evolutionary Rate (dN/dS) Source: PMK

18 060727_Yeast_Meet_Talk_PMK 17 OUTLINE Interaction Networks and their properties Network properties revisited A 3-D structural point of view Conclusions

19 060727_Yeast_Meet_Talk_PMK 18 CONCLUSIONS We constructed a genome-wide interaction network of direct physical interactions based on 3D structures Several genomic features that were previously thought to be correlated with the degree are in fact related to the number of interfaces and not the degree Specifically, a proteins evolutionary rate appears to be dependent on the fraction of surface area involved in interactions rather than the degree The current network growth model can only explain a part of currently known networks Source: PMK

20 060727_Yeast_Meet_Talk_PMK 19 ACKNOWLEDGEMENTS Mark Gerstein Long Jason Lu Yu Brandon Xia The Gersteinlab, in particular: Alberto Paccanaro Haiyuan Yu Jan Korbel Joel Rozowsky Tara Gianoulis Tom Royce

21 060727_Yeast_Meet_Talk_PMK 20 OUT

22 060727_Yeast_Meet_Talk_PMK 21 BACKUP

23 060727_Yeast_Meet_Talk_PMK 22 IN FACT, EVOLUTIONARY RATE CORRELATES BEST WITH THE FRACTION OF INTERFACE AVAILABLE SURFACE AREA Source: PMK DATA IN BINS Small portion of surface area involved in interfaces – fast evolving Large portion of surface area involved in interfaces – slow evolving

24 060727_Yeast_Meet_Talk_PMK 23 But the “Yes” side appears to be winning … OR ARE THEY? THERE IS AN ONGOING DEBATE ABOUT THE RELATIONSHIP BETWEEN EVOLUTIONARY RATE AND DEGREE Source: See text Yes, hubs are more conserved Fraser et al. Science (2002) Fraser et al. BMC Evol. Biol. (2003) Wuchty Genome Res. (2004) Jordan et al. Genome Res. (2002) Hahn et al. J. Mol. Evol. (2004) Jordan et al. BMC Evol. Biol. (2003) No, the relationship is unclear ? EXAMPLES Fraser Nature Genetics (2005)

25 060727_Yeast_Meet_Talk_PMK 24 INTERACTION NETWORKS ARE SCALE-FREE – THEIR TOPOLOGY IS DOMINATED BY SO-CALLED HUBS Source: Barabasi, A. and Albert, R., Science (1999) So-called scale-free topology has been observed in many kinds of networks (among them interaction networks) Scale freeness: A small number of hubs and a large number of poorly connected ones (“Power-law behavior”) Topology is dominated by “hubs” Scale-freeness is in stark contrast to normal (gaussian) distribution p(k) ~ k γ

26 060727_Yeast_Meet_Talk_PMK 25 SHORT DIGRESSION: THIS ALLOWS US TO DISTINGUISH SYSTEMATICALLY BETWEEN SIMULTANEOUSLY POSSIBLE AND MUTUALLY EXCLUSIVE INTERACTIONS Simultaneously possible interactions Mutually exclusive interactions Source: PMK

27 060727_Yeast_Meet_Talk_PMK 26 Mutually exclusive interactions Simultaneously possible interactions Fraction same biological process p<<0.001 Fraction same molecular function p<<0.001 Mutually exclusive interactions Simultaneously possible interactions Co-expression correlation p<<0.001 Fraction same cellular component p<<0.001 SIMULTANEOUSLY POSSIBLE INTERACTIONS (“PERMANENT”) MORE OFTEN LINK PROTEINS THAT ARE FUNCTIONALLY SIMILAR, COEXPRESSED AND CO-LOCATED Source: PMK

28 060727_Yeast_Meet_Talk_PMK 27 SCALE FREENESS GENERALLY EVOLVES THROUGH PREFERENTIAL ATTACHMENT (THE RICH GET RICHER) Source: Albert et al. Rev. Mod. Phys. (2002) and Middendorf et al. PNAS (2005) Theoretical work shows that a mechanism of preferential attachment leads to a scale- free topology (“The rich get richer”) The Duplication Mutation ModelDescription ILLUSTRATIVE In interaction network, gene duplication followed by mutation of the duplicated gene is generally thought to lead to preferential attachment Simple reasoning: The partners of a hub are more likely to be duplicated than the partners of a non-hub Gene duplication The interaction partners of A are more likely to be duplicated

29 060727_Yeast_Meet_Talk_PMK 28 But the “Yes” side appears to be winning … OR ARE THEY? THERE IS AN ONGOING DEBATE ABOUT THE RELATIONSHIP BETWEEN EVOLUTIONARY RATE AND DEGREE Source: See text Yes, hubs are more conserved Fraser et al. Science (2002) Fraser et al. BMC Evol. Biol. (2003) Wuchty Genome Res. (2004) Jordan et al. Genome Res. (2002) Hahn et al. J. Mol. Evol. (2004) Jordan et al. BMC Evol. Biol. (2003) No, the relationship is unclear ? This debate may have arisen because the two different sides were all looking at the wrong variable!

30 060727_Yeast_Meet_Talk_PMK 29 IS THERE A DIFFERENCE BETWEEN SINGLE-INTERFACE HUBS AND MULTI-INTERFACE HUBS WITH RESPECT TO NETWORK EVOLUTION? Source: PMK The Duplication Mutation Model Gene duplication The interaction partners of A are more likely to be duplicated In the structural viewpoint If these models were correct, there would be an enrichment of paralogs among B

31 060727_Yeast_Meet_Talk_PMK 30 Random pair Same partner Same partner different interface Same partner same interface Fraction of paralogs between pairs of proteins MULTI-INTERFACE HUBS DO NOT APPEAR TO EVOLVE BY A GENE DUPLICATION – THE DUPLICATION MUTATION MODEL CAN ONLY EXPLAIN THE EXISTENCE OF SINGLE-INTERFACE HUBS Source: PMK But that also means that the duplication-mutation model cannot explain the full current interaction network!

32 060727_Yeast_Meet_Talk_PMK 31 INTERESTING PROPERTIES OF INTERACTION NETWORKS Source: Various, see following slides Network topology Network Evolution Relationship of topology and genomic features Examples of studies What distribution does the degree (number of interaction partners) follow? What is the relationship between the degree and a proteins essentiality? Is there a relationship between a proteins connectivity and expression profile? What is the relationship between a proteins evolutionary rate and its degree? How did the observed network topology evolve? OVERVIEW

33 060727_Yeast_Meet_Talk_PMK 32 REMEMBER THE NETWORK PROPERTIES AS WE DESCRIBED BEFORE? Source: Various, see following slides Network topology Network Evolution Relationship of topology and genomic features Examples of studies What distribution does the degree (number of interaction partners follow?) Does the network easily separate into more than one component? What is the relationship between the degree and a proteins essentiality? Is there a relationship between a proteins connectivity and expression profile? What is the relationship between a proteins evolutionary rate and its degree? How did the observed network topology evolve? OVERVIEW


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