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蛋白质相互作用的生物信息学

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					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
 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
       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。
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.
  – Don’t need Ab.
• 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.
 Mass spectrometry of purified
          complexes
• Benefits:
  – several members of a complex can be tagged, giving
    an internal check for consistency;
  – and it detects real complexes in physiological
    settings.
• 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.
 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.
Genetic interactions (synthetic
          lethality).
• Benefits: it is an in vivo technique, albeit
  an indirect one; and it is amenable to
  unbiased genome-wide screens.
• Drawbacks: not necessarily physical
  interactions
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
蛋白质相互作用数据库




      Curr Opin Struct Biol 2003,13:377
     THE DIP DATABASE

• Database of Interacting Proteins
• The DIP database catalogs
  experimentally determined interactions
  between proteins.
DIP相互作用的表达




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




       Nucleic Acids Research, 2000, 28, 289-291
  BIND:the Biomolecular
Interaction Network Database




                     Nucleic Acids Research, 2001, 29, 242-245
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
高通量实验数据需要验证




       Curr Opin Struct Biol 2003,13:377
与可信的数据相比




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




     Curr Opin Struct Biol 2003,13:377
Paralogous Verification Method
• PVM ScoreThe Paralogous Verification
  (PVM) method judges an interaction
  probable if the putatively interacting pair
  has paralogs that also interact .
    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.
Correlation distance




              Nature Biotechnology 2003, 22, 78
蛋白质相互作用网络




        Nature 2001, 411, 41 - 42
       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
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
计算预测蛋白质相互作用




       Curr Opin Struct Biol 2003,13:377
               Docking
• Need 3D Structures
• CAPRI: Critical Assessment of Predicted
  Interactions, a community-wide
  experiment for assessing the predictive
  power of these procedures.
                          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)
          Gene Clustering
• Based on: Functional coupling genes are
  in conserved gene clusters in different
  genomes.
Gene Clustering




                  Overbeek R PNAS 96, 2896 (1999)
Overbeek R PNAS 96, 2896 (1999)
Phylogenetic profile




                   PNAS (1999) 96, 4285-4288
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.
• 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
A Combined Experimental and
   Computational Strategy
• 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.

                                       Science 2002 295, 321
       高友鹤

gaoyouhe@pumc.edu.cn

				
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