Transcript 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
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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
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.
2.
3.
4.
5.
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
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.
2.
3.
4.
5.
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
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.
2.
3.
4.
5.
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
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
[email protected]