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Biological networks: Types and origin Protein-protein interactions, complexes, and network properties Thomas Skøt Jensen 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 Networks in electronics Radio kindly provided by Lazebnik, Cancer Cell, 2002

Interactions YER 001 W YBR 088 C YOL 007 C YPL 127 C YNR Interactions YER 001 W YBR 088 C YOL 007 C YPL 127 C YNR 009 W YDR 224 C YDL 003 W YBL 003 C … YDR 097 C YBR 089 W YBR 054 W YMR 215 W YBR 071 W YBL 002 W YNL 283 C YGR 152 C … Parts List • Sequencing • Gene knock-out • Microarrays • etc. • Genetic interactions • Protein-Protein interactions Interactions • Protein-DNA interactions • Subcellular Localization • Microarrays • Proteomics • Metabolomics Dynamics Radio kindly provided by Lazebnik, Cancer Cell, 2002 Model Generation

Types of networks Types of networks

Interaction networks in molecular biology • • Protein-protein interactions Protein-DNA interactions Genetic interactions Metabolic Interaction networks in molecular biology • • Protein-protein interactions Protein-DNA interactions Genetic interactions Metabolic reactions Co-expression interactions Text mining interactions Association networks

Interaction networks in molecular biology • • Protein-protein interactions Protein-DNA interactions Genetic interactions Metabolic Interaction networks in molecular biology • • Protein-protein interactions Protein-DNA interactions Genetic interactions Metabolic reactions Co-expression interactions Text mining interactions Association networks

Characterization of physical interactions • Obligation – obligate (protomers only found/function together) – non-obligate 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)

Examples: GPCR obligate, permanent ol non-obligate, strong transient Examples: GPCR obligate, permanent ol non-obligate, strong transient

Approaches by interaction type • Physical Interactions – Yeast two hybrid screens – Affinity Approaches by interaction type • Physical Interactions – Yeast two hybrid screens – Affinity purification (mass spec) – Protein-DNA by ch. IP-chip • Other measures of ‘association’ – Genetic interactions (double deletion mutants) – Functional associations (STRING) – Co-expression

Yeast two-hybrid method Y 2 H assays interactions in vivo. Uses property that transcription Yeast two-hybrid method Y 2 H 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.

Yeast two-hybrid method Fields and Song Yeast two-hybrid method Fields and Song

Issues with Y 2 H • Strengths – High sensitivity (transient & permanent PPIs) Issues with Y 2 H • 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)

Protein interactions by immuno-precipitation followed by mass spectrometry • Start with affinity purification of Protein interactions by immuno-precipitation followed by mass spectrometry • Start with affinity purification of a single epitopetagged 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.

Affinity Purification Affinity Purification

Affinity Purification Strengths • High specificity • Well suited for detecting permanent or strong Affinity Purification Strengths • 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)

Protein-protein interaction data growth Error rate may be as high as 30 -50 % Protein-protein interaction data growth Error rate may be as high as 30 -50 %

Topology based scoring of interactions D Yeast two-hybrid A B C High confidence (1 Topology based scoring of interactions D Yeast two-hybrid A B C High confidence (1 unshared interaction partners) Low confidence (4 unshared interaction partners) Complex pull-downs Low confidence (rarely purified together) High confidence (often purified together) de Lichtenberg et al. , Science, 2005

Filtering by subcellular localization de Lichtenberg et al. , Science, 2005 Filtering by subcellular localization de Lichtenberg et al. , Science, 2005

Filtering reduces coverage and increases specificity Filtering reduces coverage and increases specificity

Network Properties Graphs, paths, topology Network Properties Graphs, paths, topology

Graphs • Graph G=(V, E) is a set of vertices V and edges E 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, |E|=m the number of vertices and 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~n 2 • Complete graph when m=n 2

Connected Components • G(V, E) • |V| = 69 • |E| = 71 Connected Components • G(V, E) • |V| = 69 • |E| = 71

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

Paths A path is a sequence {x 1, x 2, …, xn} such that Paths A path is a sequence {x 1, x 2, …, xn} such that (x 1, x 2), (x 2, x 3), …, (xn-1, xn) are edges of the graph. A closed path xn=x 1 on a graph is called a graph cycle or circuit.

Shortest-Path between nodes Shortest-Path between nodes

Shortest-Path between nodes Shortest-Path between nodes

Longest Shortest-Path Longest Shortest-Path

Degree or connectivity Degree or connectivity

Random vs scale-free networks P(k) is probability of each degree k, i. e fraction 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 powerlaw: P(k) ~ k-g Such networks are said to be scale-free

“The Swedish sex web” Target the ‘hubs’ to have an efficient safe sex education “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

Knock-out lethality and connectivity Knock-out lethality and connectivity

Clustering coefficient The density of the network surrounding node I, characterized as the number Clustering coefficient The density of the network surrounding node I, characterized as the number of triangles through I. Related to network modularity k: neighbors of I The center node has 8 (grey) neighbors There are 4 edges between the neighbors C = 2*4 /(8*(8 -1)) = 8/56 = 1/7 n. I: edges between node I’s neighbors

Protein complexes have a high clustering coefficient Proteins subunits are highly interconnected and thus 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

Hierarchical Networks Hierarchical Networks

Detecting hierarchical organization Detecting hierarchical organization

Scale-free networks are robust • Complex systems (cell, internet, social networks), are resilient to 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.

Other interesting features • Cellular networks are assortative, hubs tend not to interact directly 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)

Sub-cellular localization coverage Sub-cellular localization coverage

Co-localization of interacting proteins Co-localization of interacting proteins

Tendency to interact with your cousin Tendency to interact with your cousin

Over-representation of highly abundant proteins Over-representation of highly abundant proteins

Coverage versus Accuracy Sensitivity say a lot, of which most is wrong say a Coverage versus Accuracy Sensitivity 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