1. Biological networks Bing Zhang Department of Biomedical Informatics Vanderbilt University bing.zhang@vanderbilt.edu

2. Protein-protein interaction (PPI) Definition  Physical association of two or more protein molecules Examples  Receptor-ligand interactions  Kinase-substrate interactions  Transcription factor-co-activator interactions  Multiprotein complex, e.g. multimeric enzymes BCHM352, Spring 2011 Cramer et al. Science 292:1863, 2001 RNA polymerase II, 12 subunits 2

3. Significance of protein interaction Most proteins mediate their function through interacting with other proteins  To form molecular machines  To participate in various regulatory processes Distortions of protein interactions can cause diseases BCHM352, Spring 2011 3

4. Method  Bait strain: a protein of interest, bait (B), fused to a DNA-binding domain (DBD)  Prey strains: ORFs fused to a transcriptional activation domain (AD)  Mate the bait strain to prey strains and plate diploid cells on selective media (e.g. without Histidine)  If bait and prey interact in the diploid cell, they reconstitute a transcription factor, which activates a reporter gene whose expression allows the diploid cell to grow on selective media  Pick colonies, isolate DNA, and sequence to identify the ORF interacting with the bait Pros  High-throughput  Can detect transient interactions Cons  False positives  Non-physiological (done in the yeast nucleus)  Can’t detect multiprotein complexes Uetz P. Curr Opin Chem Biol. 6:57, 2002 Yeast two-hybrid 4

5. BCHM352, Spring 2011 Tandem affinity purification Method  TAP tag: Protein A, Calmodulin binding domain, TEV protease cleavage site  Bait protein gene is fused with the DNA sequences encoding TAP tag  Tagged bait is expressed in cells and forms native complexes  Complexes purified by TAP method  Components of each complex are identified through gel separation followed by MS/MS Pros  High-throughput  Physiological setting  Can detect large stable protein complexes Cons  High false positives  Can’t detect transient interactions  Can’t detect interactions not present under the given condition  Tagging may disturb complex formation  Binary interaction relationship is not clear Chepelev et al. Biotechnol & Biotechnol 22:1, 2008 5

6. BCHM352, Spring 2011 Large scale protein interaction identification Experimental  Yeast two-hybrid  Tandem affinity purification Computational  Gene fusion  Ortholog interaction  Phylogenetic profiling  Microarray gene co-expression Valencia et al. Curr. Opin. Struct. Biol, 12:368, 2002 6

7. BCHM352, Spring 2011 Protein interaction data in the public domain Database of Interacting Proteins (DIP) http://dip.doe-mbi.ucla.edu/ The Molecular INTeraction database (MINT) http://mint.bio.uniroma2.it/mint/ The Biomolecular Interaction Network Database (BIND) http://www.binddb.org/ The General Repository for Interaction Datasets (BioGRID) http://www.thebiogrid.org/ Human Protein Reference Database (HPRD) http://www.hprd.org Online Predicted Human Interaction Database (OPHID) http://ophid.utoronto.ca The Munich Information Center for Protein Sequences (MIPS) http://mips.gsf.de 7

8. HPRD BCHM352, Spring 2011 8

9. Protein interaction networks Saccharomyces cerevisiae Jeong et al. Nature, 411:41, 2001 Drosophila melanogaster Giot et al. Science, 302:1727, 2003 Caenorhabditis elegans Li et al. Science, 303:540, 2004 Homo sapiens Rual et al. Nature, 437:1173, 2005 9

10. Gene regulatory networks Experimental  Chromatin immunoprecipitation (ChIP) ChIP-chip ChIP-seq Computational  Promoter sequence analysis  Reverse engineering from microarray gene expression data Public databases  Transfac (http://www.gene-regulation.com)http://www.gene-regulation.com  MSigDB (http://www.broadinstitute.org/gsea/msigdb)http://www.broadinstitute.org/gsea/msigdb  hPDI (http://bioinfo.wilmer.jhu.edu/PDI/ )http://bioinfo.wilmer.jhu.edu/PDI/ BCHM352, Spring 2011 10 Shen-orr et al. Nat Genet, 31:64, 2002

11. KEGG metabolic network BCHM352, Spring 2011 11

12. Network visualization tools Cytoscape  http://www.cytoscape.org http://www.cytoscape.org BCHM352, Spring 2011 12 Gehlenborg et al. Nature Methods, 7:S56, 2010

13. BCHM352, Spring 2011 13 Graph representation of networks Cramer et al. Science 292:1863, 2001 edge node Graph: a graph is a set of objects called nodes or vertices connected by links called edges. In mathematics and computer science, a graph is the basic object of study in graph theory. RNA polymerase II

14. BCHM352, Spring 2011 14 Undirected graph vs directed graph Protein interaction network Nodes: protein Edges: physical interaction Undirected Transcriptional regulatory network Nodes: transcription factors and genes Edges: transcriptional regulation Directed TF->target gene Metabolic network Nodes: metabolites Edges: enzymes Directed Substrate->Product Krogan et al. Nature 440:637, 2006 Ravasz et al. Science 297:1551, 2002 Lee et al. Science 298:799, 2002 Fhl1 RPL2B

15. Degree, path, shortest path Degree: the number of edges adjacent to a node. A simple measure of the node centrality. Path: a sequence of nodes such that from each of its nodes there is an edge to the next node in the sequence. Shortest path: a path between two nodes such that the sum of the distance of its constituent edges is minimized. BCHM352, Spring 2011 15 YDL176W Degree: 3 Fhl1 Out degree: 4 In degree: 0

16. Obama vs Lady Gaga: who is more influential? BCHM352, Spring 2011 16 Obama 7,035,548701,301 Gaga 8,873,525144,263 Eminem 3,509,4690 Twitter followers (in degree) Twitter following (out degree)

17. BCHM352, Spring 2011 17 Albert et al., Nature, 406:378, 2000 Random network 130 nodes, 215 edges Homogeneous: most nodes have approximately the same number of links Five red nodes with the highest number of links reach 27% of the nodes Scale-free network 130 nodes, 215 edges Heterogeneous: the majority of the nodes have one or two links but a few nodes have a large number of links Five red nodes with the highest degrees reach 60% of the nodes (hubs) Network properties (I): hubs

18. BCHM352, Spring 2011 18 Scale-free biological networks Jeong et al, Nature, 407:651, 2000Noort et al, EMBO Reports,5:280, 2004 Stelzl et al. Cell, 122:957, 2005 Metabolic network C. elegans Protein interaction network H. sapiens Gene co-expression network S. cerevisiae

19. BCHM352, Spring 2011 19 Network properties (II): small world network Stanly Milgram’s small world experiment  Social network  Average path length between two person Small world network: a graph in which most nodes can be reached from every other by a small number of steps. Biological interpretation: Efficiency in transfer of biological information Six degrees of separation Omaha Boston Wichita "If you do not know the target person on a personal basis, do not try to contact him directly. Instead, mail this folder to a personal acquaintance who is more likely than you to know the target person."

20. BCHM352, Spring 2011 20 Network properties (III): motifs Network motifs: Patterns that occur in the real network significantly more often than in randomized networks. Three-node patterns Milo et al., Science, 298:824, 2002 Feed-forward loop Feedback loop

21. BCHM352, Spring 2011 21 Network properties (IV): modularity Modularity refers to a group of physically or functionally linked molecules (nodes) that work together to achieve a relatively distinct function. Examples  Transcriptional module: a set of co- regulated genes sharing a common function  Protein complex: assembly of proteins that build up some cellular machinery, commonly spans a dense sub-network of proteins in a protein interaction network  Signaling pathway: a chain of interacting proteins propagating a signal in the cell Protein interaction modules Palla et al, Nature, 435:841, 2005 Gene co-expression modules Shi et al, BMC Syst Biol, 4:74, 2010

22. Network distance vs functional similarity Proteins that lie closer to one another in a protein interaction network are more likely to have similar function and involve in similar biological process. Sharan et al. Mol Syst Biol, 3:88, 2007 22 BCHM352, Spring 2011

23. Network-based disease gene prioritization Kohler et al. Am J Hum Genet. 82:949, 2008 23 BCHM352, Spring 2011 For a specific disease, candidate genes can be ranked based on their proximity to known disease genes.

24. Summary Biological networks  Protein-protein interaction network; Gene regulatory network; Metabolic network Graph representation of networks  Graph, node, edge, undirected graph, directed graph, degree, path, shortest path Network properties  Hubs and scale-free degree distribution  Small-world  Motifs  Modularity Network-based applications  Disease gene prioritization BCHM352, Spring 2011 24