Biological networks:Types and sourcesProtein-protein interactions, Protein complexes, and network properties
27803::Systems Biology2 CBS, Department of Systems Biology
Networks in electronics
Lazebnik, Cancer Cell, 2002
27803::Systems Biology3 CBS, Department of Systems Biology
Model
Generation
Interactions
Lazebnik, Cancer Cell, 2002
Parts List
YER001W
YBR088C
YOL007C
YPL127C
YNR009W
YDR224C
YDL003W
YBL003C
…
YDR097C
YBR089W
YBR054W
YMR215W
YBR071W
YBL002W
YNL283C
YGR152C
…
• 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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Approaches by interaction/method type• Physical Interactions
– Yeast two hybrid screens (PPI)– Affinity purification mass spectrometry, APMS (PPI)– Protein complementation assays (PPI)– ChIP-Seq, ChIP-Chip (protein-DNA)– CLIP-Seq, RIP-Seq, HITS-CLIP, PAR-CLIP (protein-RNA)
• Other measures of ‘association’– Genetic interactions (double deletion mutants)– Co-expression– Functional associations– STRING (which includes many of the above and more)
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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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Issues with Y2H• Strengths
– Takes place in vivo– Independent of endogenous expression
• Weaknesses: False positive interactions– Detects “possible interactions” that may not take place under
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 complementation assay (PCA)
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Protein interactions by immunoprecipitation followed by mass spectrometry (APMS)
• Start with affinity purification of a single epitope-tagged protein
• This enriched sample typically has a low enough complexity to be fractionated by electrophoresis techniques
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Affinity Purification Mass Spec • Strengths
– High specificity– Well suited for detecting permanent or strong transient interactions
(complexes)– Detects real, physiologically relevant PPIs
• Weaknesses– Lower sensitivity: Less suited for detecting weaker transient
interactions – May miss complexes not present under the given experimental
conditions (low sensitivity)– May identify indirect interactions (A-C-B)
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Recent binary PPI network
Y2H by Yu et al. 2008 : 2018 proteins, 2930 interactions
PCA by Tarassov et al. 2008 : 1124 proteins, 2770 interactions
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Other characterizations of physical interactions
• Obligation– obligate (only found/function together)– non-obligate (can exist/function alone)
• Time of interaction– permanent (complexes, often obligate)– strong transient (require trigger, e.g. G proteins)– weak transient (dynamic equilibrium)
• Location/compartmentalization constraints– Same/different cellular compartment– Tissue specificity
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Growth of PPI data: IntAct Statistics
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IntAct Statistics
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IntAct Statistics
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iRefIndex integration of PPI DBshttp://irefindex.uio.no/wiki/iRefIndex
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Filtering by subcellular localization
de Lichtenberg et al., Science, 2005
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An example binary-interaction score• For the yeast two-hybrid experiments, the reliability of an interaction has
been found to correlate well with the number of non-shared interaction partners for each interactor [6]. This can be summarized in the following raw quality score
• where NA and NB are the numbers of non-shared interaction partners for an interaction between protein A and B.
Low confidence
(4 unshared interaction partners)
High confidence
(1 unshared interaction partners)
A B C
D
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An example “pull-down” interaction score• For APMS or other IP pull-down experiments, the reliability of the inferred
binary interactions has been found to correlate better with the number of times the proteins were co-purified vs. purified individually.
• where:
– NAB is the number of purifications containing both proteins, i.e. the intersection of experiments that find them,
– NAB is the total number of purifications that find either A or B, i.e. the union of experiments that find them,
– NA is the number of purifications containing A, and
– NB is the numbers of purifications containing B
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Filtering reduces coverage and increases specificity
Network PropertiesGraphs, 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 the number of vertices – And |E|=m the number of edges
• Graph is sparse if m ~ n
• Graph is dense if m ~ n2
• Complete graph when m = (n2-n)/2 ~ 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
Barabási AL, Oltvai ZN. Network biology: understanding the cell's functional organization. Nat Rev Genet. 2004 Feb;5(2):101-13
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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
Barabási AL, Oltvai ZN. Network biology: understanding the cell's functional organization. Nat Rev Genet. 2004 Feb;5(2):101-13
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Essentiality vs node degree
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Clustering coefficient
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 neighbors (green)
There are 4 edges between these neighbors
C = 1/7
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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
Protein complexes have a high clustering coefficient
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Hierarchical Networks
Barabási AL, Oltvai ZN. Nat Rev Genet. 2004
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Detecting hierarchical organization
Barabási AL, Oltvai ZN. Nat Rev Genet. 2004
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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
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Other interesting features
• Cellular networks are assortative, i.e. hubs tend not to interact directly with other hubs.
• Hubs have been claimed 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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