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 2013 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 2013 3 Cramer et al. Science 292:1863, 2001 RNA polymerase II, 12 subunits

4. BCHM352, Spring 2013 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 2013 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. Protein-protein interaction identification Experimental  Yeast two-hybrid  Tandem affinity purification Computational  Gene fusion  Conservation of gene neighborhood  Phylogenetic profiling  Coevolution  Ortholog interaction  Domain interaction BCHM352, Spring 2013 6 Valencia et al. Curr. Opin. Struct. Biol, 12:368, 2002

7. PPI 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 Object Network Databank (BOND) http://bond.unleashedinformatics.com/ 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 iRef http://wodaklab.org/iRefWeb The International Molecular Exchange Consortium (IMEX) http://www.imexconsortium.org BCHM352, Spring 2013 7

8. HPRD BCHM352, Spring 2013 8

9. 9 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

10. BCHM352, Spring 2013 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 10

11. Biological networks 11 BCHM352, Spring 2013

12. Degree, path, shortest path Degree: the number of edges adjacent to a node. 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 2013 12 YDL176W Degree: 3 Fhl1 Out degree: 4 In degree: 0

13. Properties of complex networks 13 Scale-freeModular Hierarchical Small world BCHM352, Spring 2013

14. Obama vs Lady Gaga: who is more influential? BCHM352, Spring 2013 14 Obama 7,035,548701,301 Gaga 8,873,525144,263 Eminem 3,509,4690 Twitter followers (in degree) Twitter following (out degree) 28,490,739664,606 35,158,014136,511 13,946,8130

15. Role of hubs in biological networks Based on data from model organisms S. cerevisiae and C. elegans  Correspond to essential genes  Be older and have evolved more slowly  Have a tendency to be more abundant  Have a larger diversity of phenotypic outcomes resulting from their deletion BCHM352, Spring 2013 15 Vidal et al. Cell, 144:986, 2011

16. BCHM352, Spring 2013 16 Jeong et al, Nature, 411:41, 2001 Red, lethal; green, non-lethal; orange, slow growth; yellow, unknown Pearson's correlation coefficient r = 0.75, demonstrates a positive correlation between lethality and connectivity Connectivity vs protein lethality

17. BCHM352, Spring 2013 17 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  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

18. 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. GO semantic similarity Sharan et al. Mol Syst Biol, 3:88, 2007 18 BCHM352, Spring 2013 Hu et al. Nature Rev Cancer, 7:23, 2007

19. Network-based prediction: protein function, protein expression, disease association Direct neighborhood method (local)  Direct interaction partners of a protein are likely to share the same function, expression status and disease association. Diffusion-based method (global)  Proteins located in close network proximity (through direct or indirect interaction) are more likely to share the same function, expression status, and disease association. Module-based method  Proteins in the same network module are more likely to share the same function, expression status, and disease assocaition. BCHM352, Spring 2013 19

20. Protein identification in shotgun proteomics Protein digestion LC-MS/MS Database search Protein assembly 20 BCHM352, Spring 2013

21. Background Zhang, et al. J Proteome Res 6:3549, 2007 ab Protein assembly and classification 21 BCHM352, Spring 2013

22. Current protein assembly pipelines treat proteins as individual entities. Biologically interesting proteins may be eliminated due to insufficient experimental evidence. Most biological functions arise from interactions among proteins. Can we use protein interaction network information to improve protein identification? Hypothesis: an eliminated protein is more likely to be present in the original sample if it involves in a module in which other protein components are confidently identified. Network-assisted protein identification: motivation BCHM352, Spring 2013 22

23. Module-based prediction of protein expression 23 BCHM352, Spring 2013 Li et al. Mol Syst Biol,5:303, 2009 Class definition of proteins  Positive  Negative  Unknown Network mapping Module identification Statistical evaluation

24. Rescued proteins  Normal: 139 (23%)  Tumor: 95 (8%) Rescued cancer-related proteins  Ctnnb1  Top1  … Cancer specific sub-networks  Wnt signaling pathway  Cell adhesion  Apoptosis  … Application: Breast cancer data set (normal vs tumor) BCHM352, Spring 2013 24

25. Network-based disease gene prioritization Kohler et al. Am J Hum Genet. 82:949, 2008 25 BCHM352, Spring 2013 For a specific disease, candidate genes can be ranked based on their proximity to known disease genes.

26. Network visualization tools Cytoscape  http://www.cytoscape.org http://www.cytoscape.org BCHM352, Spring 2013 26 Gehlenborg et al. Nature Methods, 7:S56, 2010