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蛋白质相互作用的生物信息学

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. 蛋白质相互作用的生物信息学. 实验数据 蛋白质相互作用数据库 高通量实验数据的验证 蛋白质相互作用网络 计算预测蛋白质相互作用. 实验数据. 蛋白质相互作用的知识来源于实验。 高通量地应用传统实验方法获取大量相互作用信息。 高通量的数据需要验证。. 高通量实验方法. Curr Opin Struct Biol 2003,13:377. Yeast two-hybrid assay. Benefits: in vivo. Don’t need pure proteins.

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蛋白质相互作用的生物信息学

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  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 • 实验数据 • 蛋白质相互作用数据库 • 高通量实验数据的验证 • 蛋白质相互作用网络 • 计算预测蛋白质相互作用

  3. 实验数据 • 蛋白质相互作用的知识来源于实验。 • 高通量地应用传统实验方法获取大量相互作用信息。 • 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: • in vivo. • Don’t need pure proteins. • Don’t need Ab. • Drawbacks: • onlytwo 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: • several members of a complex can betagged, giving an internal check for consistency; • and it detectsreal complexes in physiological settings. • Drawbacks: • it mightmiss 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: • it is an in vivo technique, albeit an indirect one; • and it has much broader coverage of cellular conditions than other methods. • 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 tounbiased genome-wide screens. • Drawbacks: not necessarily physical interactions

  9. 蛋白质相互作用的生物信息学 • 实验数据 • 蛋白质相互作用数据库 • 高通量实验数据的验证 • 蛋白质相互作用网络 • 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 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

  13. DIP数据库结构 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 • 实验数据 • 蛋白质相互作用数据库 • 高通量实验数据的验证 • 蛋白质相互作用网络 • 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 Curr Opin Struct Biol 2003,13:377

  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.

  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 .

  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.

  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. Nature 2001, 411, 41 - 42

  25. 蛋白质相互作用的生物信息学 • 实验数据 • 蛋白质相互作用数据库 • 高通量实验数据的验证 • 蛋白质相互作用网络 • 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 Structures • 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. • 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)

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

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

  31. 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 eachpeptide recognition module. • 2) On the basis of these consensus sequences, computationally derive a protein-protein interaction network that links eachpeptide recognition module to proteins containing a preferredpeptide ligand. Science 2002 295, 321

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

  35. 高友鹤 gaoyouhe@pumc.edu.cn

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