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 and Bar-Joseph 2011

6. Jeong et al. Nature 411, 41 - 42 (2001) Biological Networks

7. Proteins Physical Interaction Protein-Protein A B Protein Interaction Transcription factor Target genes Transcriptional Interaction Protein-DNA A B Transcriptional Different types of Biological Networks Nodes Edges

8. What can we learn from the topology of biological networks Hubs tend to be “older” proteins Hubs are evolutionary conserved Hubs are highly connected nodes Are hubs functionally important ?

9. Hubs are usually critical proteins for the species Lethal Slow-growth Non-lethal Unknown Jeong et al. Nature 411, 41 - 42 (2001)

10. Networks can help to predict function

11. Can the network help to predict function Begley TJ, Mol Cancer Res. 2002 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

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

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

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

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

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

17. 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)

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

19. Looking at the ribosome as a network A1191

20. Many biological network have characteristics of a Small World Network Every node can be reached from every other by a small number of steps

21. Does the ribosome network have characteristics of a Small World Network? Ribosome GraphSWN 11.98.5 (L) Average Path Length 0.420.63( C) Clustering Coefficient

22. What can we learn from the ribosome network? 1.Critical sites in the ribosome network may represent functional sites (not discovered before) 2. New functional sites may be good sites for drug design

23. Looking for critical positions in a network

24. Degree: the number of edges that a node has. The node with the highest degree in the graph (HUB)

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

26. Closeness (centrality) Closeness: measure how close a node to all other nodes in the network. The nodes with the highest closeness

27. Betweenness (connectivity) The node with the highest betweenness Betweenness: quantify the number of all shortest paths that pass through a node.

28. 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

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

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

31. Lethal mutations Neutral mutations 1 2 When mutating the critical site on the ribosome the bacteria will not grow

32. Critical site on the ribosome have very high centrality values (closeness) Lethal Mutations Neutral Mutations David-Eden and Mandel-Gutfreund, 2008 nucleotides with the highest closeness nucleotides with the highest closeness P-value~0 P-value=1

33. Critical site on the ribosome have very high connectivity (betweenness) Lethal Mutations Neutral Mutations David-Eden et al, 2008 nucleotides with the highest betweennes nucleotides with the highest betweennes P-value~0 P-value=1

34. p~0 p=0.01 Critical site on the ribosome have unique network properties Lethal mutationsNeutral mutations David-Eden et al, NAR (2008)

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

36. Pockets with the highest ‘Druggability Index’ overlap known drug binding site s David-Eden et al. NAR (2010) DI=1DI=0.98 ErythromycinTelithromycin Girodazole DI=0.94DI=0.93

37. Course Summary and How to start working on your project

38. What did we learn Pairwise alignment – Dynamic Programing Local and Global Alignments When? How ? Recommended Tools : for local alignment blast2seq last.ncbi.nlm.nih.gov/Blast.cgi?PAGE_TYPE=BlastSearch&PROG_DE F=blastn&BLAST_PROG_DEF=megaBlast&BLAST_SPEC=blast2seq For global best use MSA tools such as Clustal W2, Muscle (see next slide)

39. 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 Recommended Tools : Clustal W2 http://www.ebi.ac.uk/Tools/msa/clustalw2/ (best for DNA and RNA), MUSCLE http://www.drive5.com/muscle/ (best for proteins)http://www.ebi.ac.uk/Tools/msa/clustalw2/ Phylogeny.fr phylogenetic trees http://www.phylogeny.fr/http://www.phylogeny.fr/

40. 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 BLAST http://blast.ncbi.nlm.nih.gov/Blast.cgi

41. What did we learn >Motif search When? How ? -Searching for overabundance of unknown regulatory motifs in a set of sequences ; e.g promoters of genes which have similar expression pattern (MEME) >Domain search Pfam (database to search for protein domains) Suggested Tools : MEME http://meme.nbcr.net/meme/http://meme.nbcr.net/meme/ DRIMUST http://drimust.technion.ac.il/ PFAM http://pfam.sanger.ac.uk/

42. What did we learn Protein Secondary Structure Prediction- When? How ? – Helix/Beta/Coil – Most successful approaches rely on information from the environment and MSA - Predictions level around 80% Suggested tools Jpred: http://www.compbio.dundee.ac.uk/www-jpred/http://www.compbio.dundee.ac.uk/www-jpred/

43. What did we learn Protein Tertiary Structure Prediction- When? How ? – First we must look at sequence identity to a sequence with a known structure!! – Sequence homology based methods- Homology modeling – Structure homology based methods- Threading Remember : Low quality models can be miss leading !! Database and tools Protein Data Bank http://www.rcsb.org/pdb/home/home.do Suggested tool for molecular visualization http://www.pymol.org/http://www.pymol.org/ Good tool for homology modeling http://modbase.compbio.ucsf.edu/http://modbase.compbio.ucsf.edu/

44. What did we learn RNA Structure and Function Prediction- When? How ? – MFE based methods– good for local interactions, several predictions of low energy structures – Adding information from MSA can help but usually not available – RNA families are characterized by their structure (Rfam). Suggested tools: RNAfold http://rna.tbi.univie.ac.at/cgi-bin/RNAfold.cgihttp://rna.tbi.univie.ac.at/cgi-bin/RNAfold.cgi RFAM http://rfam.sanger.ac.uk/

45. What did we learn Gene expression When? How ? > Unsupervised methods- Different clustering methods : K-means, Hierarchical Clustering > Supervised methods-such as SVM –GO annotation (analysis of gene clusters..) Selected databases and tools GEO http://www.ncbi.nlm.nih.gov/geo/ EPclust http://www.bioinf.ebc.ee/EP/EP/EPCLUST/ http://www.bioinf.ebc.ee/EP/EP/EPCLUST/ David http://david.abcc.ncifcrf.gov/

46. What did we learn? Biological Networks Different types of Biological Networks Protein-Protein (non-directed) Regulatory networks (directed) structural networks Network Motifs Network Topology Selected tools String http://string-db.org/http://string-db.org/ Biogrid http://thebiogrid.org/http://thebiogrid.org/ Cytoscape http://www.cytoscape.org/http://www.cytoscape.org/

47. Most useful databases Genomic database The human genome browser http://genome.ucsc.edu/ http://genome.ucsc.edu/ Protein database Uniprot http://www.uniprot.org/ Structure database PDB (RCSB) http://www.rcsb.org Gene expression database GEO http://www.ncbi.nlm.nih.gov/geo/

48. So How do we start … Now that you have selected a project you should carefully plan your next steps: A.Make sure you understand the problem and read the necessary background to proceed B. formulate your working plan, step by step C. After you have a plan, start from extracting the necessary data and decide on the relevant tools to use at the first step. When running a tool make sure to summarize the results and extract the relevant information you need to answer your question, it is recommended to save the raw data for your records, don't present raw data in your final project. Your initial results should guide you towards your next steps. D. When you feel you explored all tools you can apply to answer your question you should summarize and get to conclusions. Remember NO is also an answer as long as you are sure it is NO. Also remember this is a course project not only a HW exercise..

49. Preparing a poster Prepare in PPT poster size 90-120 cm Title of the project Names and affiliation of the students presenting The poster should include 5 sections : Background should include description of your question (can add figure) Goal and Research Plan: Describe the main objective and the research plan Results (main section) : Present your results in 3-4 figures, describe each figure (figure legends) and give a title to each result Conclusions : summarized in points the conclusions of your project References : List the references of paper/databases/tools used for your project

50. Key date reminder 16.1 Submission project proposal 20.1 Meetings with supervisors 19.3 Poster submission 26.3 Poster presentation (POSTER DAY 12:30-14:30)