biological networks: types and sources protein-protein interactions, protein complexes, and network...

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

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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)