ChemProt: A Disease Chemical Biology Database

1. ChemProt: A Disease Chemical Biology Database Sonny Kim Nielsen – PhD student Computational Chemical Biology - CBSDepartment of Systems Biology - DTUsonny@cbs.dtu.dk Translational Informatics, january 2011

2. PLAN • Drug-Target interactions and Target-Disease associations • Chemical Biology repository (ChemProt) • Biological Network and visualisation using Cytoscape

3. We hope for a simple concept… Drug Gene Phenotype

4. But in reality it is not so simple Phenotype Phenotype Gene Phenotype Gene Gene Phenotype Gene Phenotype Gene Drug Gene Gene Gene Phenotype Phenotype

5. What is the number of targets for a drug? 4400 drugs, 2.7 targets/drug in average 1081 drugs, 5.69 targets/ drug in average Wombat-PK

6. The pharmacology of a drug is still sparse Chemical similarity Proteins Garcia-Serna R et al. Bioinformatics 2010 Compounds Keiser MJ et al. Nat. Biotech 2007

7. What about phenotypes? Protein-Protein interactions Network (PPI) disease Especially genetic disorders (color blindness, Huntington’s disease, Cystic fibrosis) Prader-Willi syndrome (7 genes) disease disease Cancer, diabetes, mental illness

8. A quality-controlled human protein interactionnetwork 500 000 interactions between 10,300 human proteins Download and reformat PPI databases Trans organism ppi transferral Automated scoring of all interactions Lage et al. Nat. Biotech 2007

9. Tissue-specific pathology and gene expression of human disease genes and complexes Lage K, et al.,PNAS, 52, 20870-20875 (2008)

10. Integration of chemical space into biological space Chemical space Phenome-interactome Chemical bioactivity Disease chemical biology network

11. ChemProt: a disease chemical biology database MACCs and pharmacophore fingerprints computed 700.000 unique bioactive compounds for 30.000 proteins Protein-Protein Interactions data integration** Structural similarity search Compound 1 Compound 2 428000 PPIs D1 D2 P1 P2 Protein 1 Protein 2 Disease-protein complexes OMIM, AKS2, GO, Tissue specificity Taboureau O et al. 2011 Nucleic Acids Res.

12. An example with citalopram (antidepressant) Drug Bank Chembl 7 proteins ChemProt PPIs disease 49 proteins 629 genes forming 4141 interactions

13. An example with citalopram: Genes enrichment • cell communication (p-value 1.32 e-86) • - signal transduction (p-value 4.07 e-81) GO OMIM - Major Depressive Disorder (p-value 3.77 e-06) TPH2;FKBP5;HTR2A - Obsessive-Compulsive Disorder 1 (p-value 1.67 e-04) HTR2A;SLC6A4 AKS2 • Schizophrenia 9.02 e-24 • Bipolar disorder 6.92 e-21 • - Anorexia nervosa 6.61 e-10 • Bulimia nervosa1.41 e-07 • Obesity 2.20 e-05

14. An example with citalopram: Genes enrichment for DRD4 Leukemia

15. An example with citalopram: Src regulation of hERG? Citalopram inhibits hERG (25 μM in ChEMBL) Associated to LQTS and arrhythmia

16. Future plan in ChemProt: Drugs categorization based on the Anatomical Therapeutic Chemical (ATC) Classification. In ATC classification system, the active substances are divided into different group according to the organ or system on which they act and their therapeutic, pharmacologcal and chemical properties

17. Drug – Target – Disease Classification through ATC Drugs categorization based on the Anatomical Therapeutic Classification (ATC)

18. Disease – Disease Network

19. Disease – Disease Network

20. Conclusion Systems chemical biology allow to investigate new direction in understanding molecular (adverse) effects of chemicals in biological systems.

21. Some technical information about biological network and Cytoscape.

22. Biological networks in Bioinformatics Graph theory Graph G=(V, E) is a set of vertices V (nodes) and edges E A subgraph G´of G is induced by some V´  V and E´  E Graph properties: - neighborhood - Connectivity (node degree, paths) - Directed vs. undirected Path theory A path is a sequence {X1, X2,…, Xn} such that (X1, X2), (X2, X3),…,(Xn-1, Xn) are edges of the graph. A closed path Xn= X1 on a graph is called a graph cycle or circuit.

23. Biological networks in Bioinformatics Protein network representations

24. Biological networks in Bioinformatics Sparse vs dense

25. Clustering coefficient

26. How to represent interaction network? Network visualization and analysis tool. A community based framework for networks modeling www.cytoscape.org

27. Cytoscape Desktop Network Management panel Network graph and view Network overview panel Attribute browser

28. Input – output data • Cytoscape reads an interaction network: • Using a interaction file (.sif, .txt, .csv…) • A XML format (essentially to store information and allows network data exchange with a variety of other network display programs • Output can be a .sif format, txt, but also image (png, pdf, jpeg…) A A B A D B C C D D B B D C

29. Input – output data

30. Add information on nodes and edges

31. Visual style (Vizmapper) - Layout

32. Time for you to play And don’t stress…