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 Protein Interaction Transcriptional Transcription factor Target genes Proteins Transcriptional Interaction Physical Interaction Protein-Protein Protein-DNA A A 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 (is there a?) 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 SummaryandHow to start working on your project

39. 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_DEF=blastn&BLAST_PROG_DEF=megaBlast&BLAST_SPEC=blast2seq For global best use MSA tools such as Clustal W2, Muscle (see next slide)

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 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) Phylogeny.frphylogenetic trees http://www.phylogeny.fr/

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

42. 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 : MEMEhttp://meme.nbcr.net/meme/ DRIMUST http://drimust.technion.ac.il/ PFAM http://pfam.sanger.ac.uk/

43. 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 GOR: http://gor.bb.iastate.edu/ Jpred: http://www.compbio.dundee.ac.uk/www-jpred/

44. 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/ Good tool for homology modeling http://swissmodel.expasy.org/

45. 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.cgi RFAM http://rfam.sanger.ac.uk/

46. 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/ David http://david.abcc.ncifcrf.gov/

47. Most useful databases Genomic database The human genome browser 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: • 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. 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 ?

50. 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