1. Bioinformatics MEDC601 Lecture by Brad Windle Ph# 628-1956 email: bwindle@vcu.edu Office: Massey Cancer Center, Goodwin Labs Room 319 Web site for lecture: http://www.people.vcu.edu/~bwindle/Courses/MEDC601

2. Profile A set of data or characteristics pertaining to an item Profiles are sometimes referred to as Signatures or Fingerprints

3. Cellular Profiles Gene Expression Protein Expression Misc Data SNPs DNA Methylation Cell State Drug Response Metabolitics Structural Genomic Protein States Disease Gene/Protein Sequence Protein Structure Drug Structure

4. Cellular Profiles Gene Expression Protein Expression Misc Data SNPs DNA Methylation Drug Response Metabolitics Structural Genomic Protein States Gene/Protein Sequence Protein Structure Drug Structure

5. Profiles Have Two Sides A gene profile across samples and a sample profile across genes

6. Bioinformatics uses tools for learning from the Profiles There are two basic forms of learning Unsupervised Learning Supervised Learning

7. Unsupervised Learning Definition: Learning from observation Exploration Let the data reveal what you learn You can learn that what you did not expect Allows you to formulate relevant hypotheses It’s a hypothesis generator Supervised Learning Definition: Learning from example It’s focused Allows you to test relevant hypotheses but it doesn’t usually allow you to prove the hypothesis It often involves statistical or computational modeling Usually requires experimental validation of what you learn

8. Supervised Learning Examples of methods QSAR Modeling, Prediction model Classification Model, eg., Is a patient a good candidate for a particular drug treatment? Simulation Modeling, eg., Cell simulation

9. Why can’t we observe the patterns unaided? The patterns are too complex or abstract. There’s too much data. There’s too much noise.

17. Drug-related profiles drug1 R R R R R R e1 e2 e3 e4 e5 e6 Drug profile based on structure drug1 drug 2 drug 3 drug 4 e1 Structure profile based on drugs How phys/chem properties relate to biological or biochemical properties, such cell killing or enzyme activity is within the realm of QSAR

18. cell1 drug1 drug2 drug3 drug4 drug5 drug6 Cell profile based on various drug sensitivities Drug profile based on cell sensivities cell1 cell2 cell3 cell4 cell5 cell6 drug 1

19. COMPARE NCI 60 cell lines profiled for sensitivity to drugs How do drugs relate to each other based on cellular response? How do cells relate to each other based on drug response?

20. A major tool in unsupervised learning is Cluster Analysis It evaluates how similar items are to each other Do they have similar patterns within their profiles? How relatively close the items are to each other There are various ways to measure closeness

22. Cell response profile Monks et al. Anti-Cancer Drug Design 12:553 (1997)

23. Cell line clusters based on drug response Cell clusters correspond cell type to a limited extent Scherf et al, nature genetics 24:236 (2000)

24. Drug clusters correspond to drug targets or mechanisms of action not necessarily drug structure. Scherf et al, nature genetics 24:236 (2000)

30. COMPARE is a resource for exploring targets and mechanisms Your compound of interest can be profiled and compared to the profiles for >70,000 compounds Compounds with good matches may have known characteristics, such as target and mechanism, thus revealing a possible target and mechanism for your compound http://dtp.nci.nih.gov/docs/compare/compare.html

31. Wallqvist Study Wallqvist et al, Molecular Cancer Therapeutics 1:311-320 (2002) Found genes that correlated with drug sensitivity Hypothesized that some of those gene’s proteins are the target of the drugs that correlated with gene expression

33. Start with drug response, identify drugs based on correlation with genes Drugs 123123 Identified genes based on correl with drugs 123123 Genes Identify corresponding protein for each gene from structural protein database (PDB) 123123 Proteins Identify small compounds (ligands) that have been fitted to proteins using 3D modeling 123123 Ligands Identify ligands with structural correlation with drugs

34. Compounds screened in NCI 60 cell lines Genes that correlate with compounds in NCI 60 cell lines Corresponding proteins in 3D structural database Compounds that bind in silico to proteins Compounds with structural similarity to ligands

35. The similarity between the ligand specific for the protein and the drug that correlates with expression of the protein (gene) suggests that the drug is targeting the protein (interacting) calcium/calmodulin-dependent protein kinase I protein kinase C Wallqvist et al, Molecular Cancer Therapeutics 1:311-320 (2002)

36. alcohol dehydrogenase 5 Wallqvist et al, Molecular Cancer Therapeutics 1:311-320 (2002)

37. 4 sets of data DR GEP PDB Ligand Gene-Drug correlation Gene to Protein translation Protein-Ligand prediction Ligand-Drug correlation