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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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