Biological Networks

1. Biological Networks

2. Can a biologist fix a radio? Lazebnik, Cancer Cell, 2002

3. Building models from parts lists Lazebnik, Cancer Cell, 2002

4. Building models from parts lists

5. Computational tools are needed to distill pathways of interest from large molecular interaction databases Thinking computationally about biological process may lead to more accurate models, which in turn can be used to improve the design of algorithms Navlakha an Bar-Joseph 2011

6. The Protein-Protein Interaction Network in yeast Jeong et al. Nature411, 41 - 42 (2001)

7. Network Representation Non-directional edge (link) node binds protein A Protein B Directional regulates gene B gene A

8. Different types of Biological Networks Metabolic Protein Interaction Transcriptional Transcription factor Target genes Metabolites Proteins Enzymatic conversion Transcriptional Interaction Physical Interaction Protein-Protein Protein-DNA Protein-Metabolite A A A B B B Nodes Edges

9. Small-world Network Biological networks exhibit small-world network (SWN) characteristics (similar to social networks, internet etc) Every node can be reached from every other by a small number of steps

11. SWN vs Random Networks Random Network Small World Network (SWN) SWN have a small number of highly connected nodes

12. What can we learn from a network?

13. What can we learn from Biological Networks • Hubs tend to be “older” proteins • Hubs are evolutionary conserved Hubs are highly connected nodes Are hubs functionally important ?

14. Lethal Slow-growth Non-lethal Unknown Hubs are usually critical proteins for the species Jeong et al. Nature411, 41 - 42 (2001)

15. Networks can help to predict function

16. Can the network help to predict function • Systematic phenotyping of 1615 gene knockout strains in yeast • Evaluation of growth of each strain in the presence of MMS (and other DNA damaging agents) • Screening against a network of 12,232 protein interactions Begley TJ, Mol Cancer Res. 2002

17. Mapping the phenotypic data to the network Begley TJ, Mol Cancer Res. 2002

18. Mapping the phenotypic data to the network Begley TJ, Mol Cancer Res. 2002

19. Networks can help to predict function Begley TJ, Mol Cancer Res. 2002.

20. Case Study A network approach to predict new drug targets Aim :to identify critical positions on the ribosome which could be potential targets of new antibiotics

21. Keats (1795-1821) Kafka (1883-1924) Orwell (1903-1950) Mozart (1756-1791) Schubert (1797-1828) Chopin (1810-1849)

22. In our days… • Infectious diseases are still number 1 cause of premature death • (0-44 years of age) worldwide. • Annually kill >13 million people • (~33% of all deaths)

23. The ribosome is a target for approximately half of antibiotics characterized to date Antibiotics targets of the large ribosomal subunit

24. Looking at the ribosome as a network A1191

25. Looking at the ribosome as a network • Critical sites in the ribosome network may represent functional sites • (not discovered before) • 2. New functional sites may be good site for drug design

26. Looking for critical positions in a network

27. The node with the highest degree in the graph (HUB) Looking for critical positions in a network Degree: the number of edges that a node has.

28. The node with the highest degree in the graph (HUB) Looking for critical positions in a network Degree: the number of edges that a node has.

29. Closeness Closeness:measure how close a node to all other nodes in the network. The nodes with the highest closeness

30. Betweenness Betweenness:quantify the number of all shortest paths that pass through a node. The node with the highest betweenness

31. The node with the highest degree The node with the highest betweenness The nodes with the highest closeness Looking for critical positions in a network

32. Looking at macromolecular structures as a network A1191 have the highest closeness, betwenness, and degree. A1191

33. How can the network approach help identify functional sites in the ribosome ? Which property best characterizes the known function sites? ? Characterize the whole ribosome as a network Calculate the network properties of each nucleotide

34. When mutating the critical site on the ribosomethe bacteria will not grow 1 2 Strong mutations Mild mutations

35. Critical site on the ribosomehave unique network properties Strong mutations Mild mutations p~0 p~0 p=0.01 David-Eden et al, NAR (2008)

36. ‘Druggability Index’ Based on the network property Bad site Good site David-Eden et al. NAR (2010)

37. Pockets with the highest ‘Druggability Index’ overlap known drug binding sites DI=1 DI=0.98 Erythromycin Telithromycin Girodazole DI=0.94 DI=0.93 David-Eden et al. NAR (2010)

38. Course Summary

39. What did we learn • Pairwise alignment – Local and Global Alignments When? How ? Tools : for local blast2seq , for global best use MSA tools such as Clustal X, Muscle

40. What did we learn • Multiple alignments (MSA) When? How ? MSA are needed as an input for many different purposes: searching motifs, phylogenetic analysis, protein and RNA structure predictions, conservation of specific nts/residues Tools : Clustal X (for DNA and RNA), MUSCLE (for proteins) Tools for phylogenetic trees: PHYLIP …

41. What did we learn • Search a sequence against a database When? How ? - BLAST :Remember different option for BLAST!!! (blastP blastN…. ), make sure to search the right database!!! DO NOT FORGET –You can change the scoring matrices, gap penalty etc - PSIBLAST Searching for remote homologies - PHIBLAST Searching for a short pattern within a protein

42. What did we learn • Motif search When? How ? - Searching for known motifs in a given promoter (JASPAR) -Searching for overabundance of unknown regulatory motifs in a set of sequences ; e.g promoters of genes which have similar expression pattern (MEME) Tools : MEME, logo, Databases of motifs : JASPAR (Transcription Factors binding sites) PRATT in PROSITE (searching for motifs in protein sequences)

43. What did we learn • Protein Function Prediction When? How ? - Pfam (database to search for protein motifs/domain (PfamA/PfamB) - PROSITE - Protein annotations in UNIPROT (SwissProt/ Tremble)

44. What did we learn • Protein Secondary Structure Prediction- When? How ? • Helix/Beta/Coil(PHDsec,PSIPRED). • Predicts transmembrane helices (PHDhtm,TMHMM). • Solvent accessibility: important for the prediction of ligand binding sites (PHDacc).

45. What did we learn • Protein Tertiary Structure Prediction- When? How ? • First we must look at sequence identity to a sequence with a known structure!! • Homology modeling/Threading • MODEBase- database of models Remember : Low quality models can be miss leading !! Tools : SWISS-MODEL ,genTHREADER, MODEBase

46. What did we learn • RNA Structure and Function Prediction- When? How ? • RNAfold – good for local interactions, several predictions of low energy structures • Alifold – adding information from MSA • RFAM • Specific database and search tools: tRNA, microRNA …..

47. What did we learn • Gene expression When? How ? • Many database of gene expression GEO … • Clustering analysis EPClust (different clustering methods K-means, Hierarchical Clustering, trasformations row/columns/both…) • GO annotation (analysis of gene clusters..)

48. So How do we start … • Given a hypothetical sequence predict its function…. What should we do???

49. Example • Amyloids are proteins which tend to aggregate in solution. Abnormal accumulation of amyloid in organs is assumed to play a role in various neurodegenerative diseases. Question : can we predict whether a protein X is an amyolid ?