biological networks: types and origin
DESCRIPTION
Biological networks: Types and origin. Protein-protein interactions, complexes, and network properties. Thomas Skøt Jensen Center for Biological Sequence Analysis The Technical University of Denmark. Networks in electronics. Radio kindly provided by Lazebnik, Cancer Cell, 2002. Model - PowerPoint PPT PresentationTRANSCRIPT
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Biological networks:Types and origin
Protein-protein interactions, complexes, and network properties
Thomas Skøt JensenCenter for Biological Sequence AnalysisThe Technical University of Denmark
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Networks in electronics
Radio kindly provided by Lazebnik, Cancer Cell, 2002
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Model Generation
Interactions
Radio kindly provided by Lazebnik, Cancer Cell, 2002
Parts List
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• Sequencing
• Gene knock-out
• Microarrays
• etc.
Interactions
• Genetic interactions
• Protein-Protein interactions
• Protein-DNA interactions
• Subcellular Localization
Dynamics
• Microarrays
• Proteomics
• Metabolomics
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Types of networks
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Interaction networks in molecular biology
• Protein-protein interactions• Protein-DNA interactions• Genetic interactions• Metabolic reactions• Co-expression interactions• Text mining interactions• Association networks
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Interaction networks in molecular biology
• Protein-protein interactions• Protein-DNA interactions• Genetic interactions• Metabolic reactions• Co-expression interactions• Text mining interactions• Association networks
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Characterization of physical interactions
• Obligation– obligate (protomers only found/function together)– non-obligate (protomers can exist/function alone)
• Time of interaction– permanent (complexes, often obligate)– strong transient (require trigger, e.g. G proteins)– weak transient (dynamic equilibrium)
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ol
Examples: GPCR
obligate, permanent
non-obligate,
strong transient
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Approaches by interaction type
• Physical Interactions– Yeast two hybrid screens– Affinity purification (mass spec)– Protein-DNA by chIP-chip
• Other measures of ‘association’– Genetic interactions (double deletion
mutants)
– Functional associations (STRING)– Co-expression
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Yeast two-hybrid method
Y2H assays interactions in vivo.
Uses property that transcription factors generally have separable transcriptional activation (AD) and DNA binding (DBD) domains.
A functional transcription factor can be created if a separately expressed AD can be made to interact with a DBD.
A protein ‘bait’ B is fused to a DBD and screened against a library of protein “preys”, each fused to a AD.
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Yeast two-hybrid method
Fields and Song
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Issues with Y2H
• Strengths– High sensitivity (transient & permanent PPIs)– Takes place in vivo– Independent of endogenous expression
• Weaknesses: False positive interactions– Auto-activation– ‘sticky’ prey– Detects “possible interactions” that may not take place under real
physiological conditions– May identify indirect interactions (A-C-B)
• Weaknesses: False negatives interactions– Similar studies often reveal very different sets of interacting proteins (i.e.
False negatives)– May miss PPIs that require other factors to be present (e.g. ligands,
proteins, PTMs)
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Protein interactions by immuno-precipitation followed by mass spectrometry
• Start with affinity purification of a single epitope-tagged protein
• This enriched sample typically has a low enough complexity to be fractionated on a standard polyacrylamide gel
• Individual bands can be excised from the gel and identified with mass spectrometry.
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Affinity Purification
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Affinity PurificationStrengths
• High specificity
• Well suited for detecting permanent or strong transient interactions (complexes)
• Detects real, physiologically relevant PPIs
Weaknesses
• Less suited for detecting weaker transient interactions (low sensitivity)
• May miss complexes not present under the given experimental conditions (low sensitivity)
• May identify indirect interactions (A-C-B)
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Protein-protein interaction data growth
Error rate may be as high as 30-50 %
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Topology based scoring of interactions
Low confidence (4 unshared interaction partners)High confidence (1 unshared interaction partners)
A B C
Yeast two-hybrid
Low confidence (rarely purified together)
High confidence (often purified together)
Complex pull-downs
D
de Lichtenberg et al., Science, 2005
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Filtering by subcellular localization
de Lichtenberg et al., Science, 2005
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Filtering reduces coverage and increases specificity
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Network Properties
Graphs, paths, topology
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Graphs
• Graph G=(V,E) is a set of vertices V and edges E
• A subgraph G’ of G is induced by some V’ V and E’ E
• Graph properties:– Connectivity (node degree, paths)– Cyclic vs. acyclic– Directed vs. undirected
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Sparse vs Dense
• G(V, E) where |V|=n, |E|=m the number of vertices and edges
• Graph is sparse if m~n
• Graph is dense if m~n2
• Complete graph when m=n2
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Connected Components
• G(V,E)
• |V| = 69
• |E| = 71
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Connected Components
• G(V,E)
• |V| = 69
• |E| = 71
• 6 connected components
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Paths
A path is a sequence {x1, x2,…, xn} such that (x1,x2), (x2,x3), …, (xn-1,xn) are edges of the graph.
A closed path xn=x1 on a graph is called a graph cycle or circuit.
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Shortest-Path between nodes
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Shortest-Path between nodes
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Longest Shortest-Path
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Degree or connectivity
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Random vs scale-free networks
P(k) is probability of each degree k, i.e fraction of nodes having that degree.
For random networks, P(k) is normally distributed.
For real networks the distribution is often a power-law:
P(k) ~ k
Such networks are said to be scale-free
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“The Swedish sex web”
Target the ‘hubs’ to have an efficient safe sex education campaign
Lewin Bo, et al., Sex i Sverige; Om sexuallivet i Sverige 1996, Folkhälsoinstitutet, 1998
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y = 1.2x-1.91
1.0E-04
1.0E-03
1.0E-02
1.0E-01
1.0E+00
1.0E+01
1 10 100
Degree k
P (k
)Knock-out lethality and
connectivity
0
10
20
30
40
50
60
0 5 10 15 20 25
Degree k
% E
ssen
tial G
enes
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Clustering coefficient
12
2
kk
nkn
C III
k: neighbors of I
nI: edges between
node I’s neighbors
The density of the network surrounding node I, characterized as the number of triangles through I. Related to network modularity
The center node has 8 (grey) neighbors
There are 4 edges between the neighbors
C = 2*4 /(8*(8-1)) = 8/56 = 1/7
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Protein complexes have a high clustering coefficient
Proteins subunits are highly interconnected and thus have a high
clustering coefficient
There exists algorithms, such as MCODE, for identifying subnetworks (complexes) in large protein-protein
interaction networks
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Hierarchical Networks
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Detecting hierarchical organization
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Scale-free networks are robust
• Complex systems (cell, internet, social networks), are resilient to component failure
• Network topology plays an important role in this robustness– Even if ~80% of nodes fail, the remaining ~20%
still maintain network connectivity
• Attack vulnerability if hubs are selectively targeted
• In yeast, only ~20% of proteins are lethal when deleted, and are 5 times more likely to have degree k>15 than k<5.
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Other interesting features
• Cellular networks are assortative, hubs tend not to interact directly with other hubs.
• Hubs tend to be “older” proteins (so far claimed for protein-protein interaction networks only)
• Hubs also seem to have more evolutionary pressure—their protein sequences are more conserved than average between species (shown in yeast vs. worm)
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Sub-cellular localization coverage
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Co-localization of interacting proteins
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Tendency to interact with your cousin
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Over-representation of highly abundant proteins
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Coverage versus Accuracy
say a lot, of which most is wrong
say a lot, of which most is right
say little, of which most is wrong
say little, of which most is right
Specificity
Sensitivity