1. 蛋白质相互作用的生物信息学 高友鹤 中国医学科学院 基础医学研究所

2. 蛋白质相互作用的生物信息学 1. 实验数据 2. 蛋白质相互作用数据库 3. 高通量实验数据的验证 4. 蛋白质相互作用网络 5. 计算预测蛋白质相互作用

3. 实验数据 1. 蛋白质相互作用的知识来源于实验。 2. 高通量地应用传统实验方法获取大量相 互作用信息。 3. 高通量的数据需要验证。

4. 高通量实验方法 Curr Opin Struct Biol 2003,13:377

5. Yeast two-hybrid assay Benefits:Benefits: –in vivo. –Don’t need pure proteins. –Don’t need Ab. Drawbacks:Drawbacks: –only two proteins are tested at a time (no cooperative binding); –it takes place in the nucleus, so many proteins are not in their native compartment; and it predicts possible interactions, but is unrelated to the physiological setting.

6. Mass spectrometry of purified complexes Benefits:Benefits: –several members of a complex can be tagged, giving an internal check for consistency; –and it detects real complexes in physiological settings. Drawbacks:Drawbacks: –it might miss some complexes that are not present under the given conditions; –tagging may disturb complex formation; and loosely associated components may be washed off during purification.

7. Correlated mRNA expression Benefits:Benefits: –it is an in vivo technique, albeit an indirect one; –and it has much broader coverage of cellular conditions than other methods. Drawbacks:Drawbacks: –it is a powerful method for discriminating cell states or disease outcomes, but is a relatively inaccurate predictor of direct physical interaction; –and it is very sensitive to parameter choices and clustering methods during analysis.

8. Genetic interactions (synthetic lethality). Benefits: it is an in vivo technique, albeit an indirect one; and it is amenable to unbiased genome-wide screens.Benefits: it is an in vivo technique, albeit an indirect one; and it is amenable to unbiased genome-wide screens. Drawbacks: not necessarily physical interactionsDrawbacks: not necessarily physical interactions

9. 蛋白质相互作用的生物信息学 1. 实验数据 2. 蛋白质相互作用数据库 3. 高通量实验数据的验证 4. 蛋白质相互作用网络 5. 计算预测蛋白质相互作用

10. 蛋白质相互作用数据库 Curr Opin Struct Biol 2003,13:377

11. THE DIP DATABASE Database of Interacting ProteinsDatabase of Interacting Proteins The DIP database catalogs experimentally determined interactions between proteins.The DIP database catalogs experimentally determined interactions between proteins.

12. DIP 相互作用的表达 Nucleic Acids Research, 2000, 28, 289 － 291 Nucleic Acids Research, 2000, 28, 289 － 291

13. DIP 数据库结构 Nucleic Acids Research, 2000, 28, 289 － 291 Nucleic Acids Research, 2000, 28, 289 － 291

14. BIND ： the Biomolecular Interaction Network Database Nucleic Acids Research, 2001, 29, 242-245

15. 蛋白质相互作用的生物信息学 1. 实验数据 2. 蛋白质相互作用数据库 3. 高通量实验数据的验证 4. 蛋白质相互作用网络 5. 计算预测蛋白质相互作用

16. 高通量实验数据需要验证 Curr Opin Struct Biol 2003,13:377

17. 与可信的数据相比

18. Expression Profile Reliability EPR IndexExpression Profile Reliability Index (EPR Index) evaluates the quality of a large-scale protein-protein interaction data sets by comparing the expression profile of the interacting dataset with that of the high-quality subset of the DIP database.EPR IndexExpression Profile Reliability Index (EPR Index) evaluates the quality of a large-scale protein-protein interaction data sets by comparing the expression profile of the interacting dataset with that of the high-quality subset of the DIP database.EPR IndexEPR Index

19. 高通量数据互相比 Curr Opin Struct Biol 2003,13:377

20. Paralogous Verification Method PVM ScoreThe Paralogous Verification (PVM) method judges an interaction probable if the putatively interacting pair has paralogs that also interact.PVM ScoreThe Paralogous Verification (PVM) method judges an interaction probable if the putatively interacting pair has paralogs that also interact.PVM ScorePVM Score

21. Domain Pair Verification DPV ScoreThe Domain Pair Verification (DPV) method judges an interaction probable if potential domain-domain interactions between the pair are deemed probable.DPV ScoreThe Domain Pair Verification (DPV) method judges an interaction probable if potential domain-domain interactions between the pair are deemed probable.DPV ScoreDPV Score

22. Correlation distance Nature Biotechnology 2003, 22, 78

23. 蛋白质相互作用网络 Nature 2001, 411, 41 - 42

24. 相互作用网络的用途 The most highly connected proteins in the cell are the most important for its survival.The most highly connected proteins in the cell are the most important for its survival. Nature 2001, 411, 41 - 42

25. 蛋白质相互作用的生物信息学 1. 实验数据 2. 蛋白质相互作用数据库 3. 高通量实验数据的验证 4. 蛋白质相互作用网络 5. 计算预测蛋白质相互作用

26. 计算预测蛋白质相互作用 Curr Opin Struct Biol 2003,13:377

27. Docking Need 3D StructuresNeed 3D Structures CAPRI: Critical Assessment of Predicted Interactions, a community-wide experiment for assessing the predictive power of these procedures.CAPRI: Critical Assessment of Predicted Interactions, a community-wide experiment for assessing the predictive power of these procedures.

28. Protein Fusion Based on: Some pairs of interacting proteins encoded in separate genes in one organism are fused to produce single homologous proteins in other organism.Based on: Some pairs of interacting proteins encoded in separate genes in one organism are fused to produce single homologous proteins in other organism. Compare E. Coli with other genomes: 6,809 putative protein-protein interactions Marcotte EM Science 285,751(1999)Compare E. Coli with other genomes: 6,809 putative protein-protein interactions Marcotte EM Science 285,751(1999) Compare yeast with others: 45,502 putative interactions Enright AJ Nature 402,86 (1999)Compare yeast with others: 45,502 putative interactions Enright AJ Nature 402,86 (1999)

29. Gene Clustering Based on: Functional coupling genes are in conserved gene clusters in different genomes.Based on: Functional coupling genes are in conserved gene clusters in different genomes.

30. Gene Clustering Overbeek R PNAS 96, 2896 (1999)

32. Phylogenetic profile PNAS (1999) 96, 4285-4288

33. A Combined Experimental and Computational Strategy 1) Screen random peptide libraries by phage display to define the consensus sequences for preferred ligands that bind to each peptide recognition module.1) Screen random peptide libraries by phage display to define the consensus sequences for preferred ligands that bind to each peptide recognition module. 2) On the basis of these consensus sequences, computationally derive a protein-protein interaction network that links each peptide recognition module to proteins containing a preferred peptide ligand.2) On the basis of these consensus sequences, computationally derive a protein-protein interaction network that links each peptide recognition module to proteins containing a preferred peptide ligand. Science 2002 295, 321

34. 3) Experimentally derive a protein-protein interaction network by testing each peptide recognition module for association to each protein of the inferred proteome in the yeast two-hybrid system.3) Experimentally derive a protein-protein interaction network by testing each peptide recognition module for association to each protein of the inferred proteome in the yeast two-hybrid system. 4) Determine the intersection of the predicted and experimental networks and test in vivo the biological relevance of key interactions within this set.4) Determine the intersection of the predicted and experimental networks and test in vivo the biological relevance of key interactions within this set. A Combined Experimental and Computational Strategy Science 2002 295, 321

35. 高友鹤gaoyouhe@pumc.edu.cn