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


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


Types of networks


Interaction networks in molecular biology

• Protein-protein interactions• Protein-DNA interactions• Genetic interactions• Metabolic reactions• Co-expression interactions• Text mining interactions• Association networks


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)


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.


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)


Protein complementation assay (PCA)


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


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)


Recent binary PPI network

Y2H by Yu et al. 2008 : 2018 proteins, 2930 interactions

PCA by Tarassov et al. 2008 : 1124 proteins, 2770 interactions


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


Growth of PPI data: IntAct Statistics


IntAct Statistics


iRefIndex integration of PPI DBshttp://irefindex.uio.no/wiki/iRefIndex


Filtering by subcellular localization

de Lichtenberg et al., Science, 2005


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


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


Filtering reduces coverage and increases specificity

Network PropertiesGraphs, paths, topology


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


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


Connected Components

• G(V,E)• |V| = 69• |E| = 71


Connected Components

• G(V,E)• |V| = 69• |E| = 71• 6 connected

components


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.


Shortest-Path between nodes


Longest Shortest-Path


Degree or connectivity

Barabási AL, Oltvai ZN. Network biology: understanding the cell's functional organization. Nat Rev Genet. 2004 Feb;5(2):101-13


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


Essentiality vs node degree


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


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


Hierarchical Networks

Barabási AL, Oltvai ZN. Nat Rev Genet. 2004


Detecting hierarchical organization

Barabási AL, Oltvai ZN. Nat Rev Genet. 2004


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


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)

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

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