nannolyze ligand target prediction by structural network
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nAnnolyze : ligand-target prediction by structural network biology Francisco Martnez-Jimnez Workshop , ECCB, Strasbourg Drug Development Sunday, September 7, 14 Finding out the mode of action.. Phenotype Sunday, September 7, 14 Finding

  1. nAnnolyze : ligand-target prediction by structural network biology Francisco Martínez-Jiménez Workshop , ECCB, Strasbourg Drug Development Sunday, September 7, 14

  2. Finding out the mode of action.. Phenotype Sunday, September 7, 14

  3. Finding out the mode of action.. Phenotype Sunday, September 7, 14

  4. Existing computational methods Prediction details & accuracy Computational time Sunday, September 7, 14

  5. Existing computational methods Prediction details & accuracy free structure methods ★ Based on previous knowledge. ★ Many different methods. ★ Good performance. ★ Poor information about the interaction. Computational time Sunday, September 7, 14

  6. Existing computational methods Prediction details & accuracy structure based methods free structure methods Virtual Docking ★ Very precise. Ligand and ★ Based on previous knowledge. receptor orientation. ★ Many different methods. ★ Needs the binding-site. ★ Good performance. ★ Needs the structure or a ★ Poor information about the interaction. reliable 3D-model. ★ Not applicable at wide scale. Computational time Sunday, September 7, 14

  7. Existing computational methods Prediction details & accuracy structure based methods free structure methods Virtual Docking Comparative Docking ★ Outputs binding-site localization. ★ Very precise. Ligand and ★ Based on previous knowledge. ★ Based on structural comparisons. receptor orientation. ★ Many different methods. ★ Applicable at wide scale. ★ Needs the binding-site. ★ Good performance. ★ Needs the structure or a reliable ★ Needs the structure or a ★ Poor information about the interaction. 3D-model. reliable 3D-model. ★ Not applicable at wide scale. Computational time Sunday, September 7, 14

  8. Comparative Docking Sunday, September 7, 14

  9. Similar binding-sites tend to bind similar ligands Activin receptor type-1 co-crystallized A3F Similar binding-sites AQ4 co-crystallized Epidermal growth factor receptor Sunday, September 7, 14

  10. Similar binding-sites tend to bind similar ligands Activin receptor type-1 co-crystallized A3F Similar binding-sites AQ4 co-crystallized Epidermal growth factor receptor Sunday, September 7, 14

  11. Similar binding-sites tend to bind similar ligands Activin receptor type-1 Similar ligands co-crystallized VGM A3F Similar binding-sites AQ4 co-crystallized Epidermal growth factor receptor Sunday, September 7, 14

  12. Similar binding-sites tend to bind similar ligands Activin receptor type-1 Similar ligands co-crystallized VGM A3F Similar binding-sites AQ4 co-crystallized Epidermal growth factor receptor Sunday, September 7, 14

  13. Network-based Annolyze nAnnolyze Sunday, September 7, 14

  14. Ligand subnetwork • Retrieved 7,609 high drug-likeness* compounds from PDB. • Nodes of highly similar compounds: cliques of similarities. • 4,101 nodes of ligand clusters and 24,856 edges . • Edges weight = normalized similarity score. Network ligand node clique degree 6 * Bickerton, G. R., Paolini, G. V, Besnard, J., Muresan, S., & Hopkins, A. L. (2012). Quantifying the chemical beauty of drugs. Nature chemistry , 4 (2), 90–8. Sunday, September 7, 14

  15. Protein binding-site network • Retrieved binding-sites for the 7,609 compounds: 28,299 binding-sites. • Similarities between proteins by structural comparisons of the binding-site. • Cluster highly similar groups of binding-sites: cliques of binding-sites. • 19,483 nodes of binding-sites and 29,811 edges . • Edges weight = normalized binding-site similarity score. Network binding-site node clique degree3 Link the two subnetworks by edges between protein structures and their co-crystallized ligands. Sunday, September 7, 14

  16. Looking for targets... t1 t2 Query . DZP . . tN Sunday, September 7, 14

  17. Looking for targets... t1 t2 Query . DZP . . tN Sunday, September 7, 14

  18. Looking for targets... t1 t2 Query . DZP . . tN Sunday, September 7, 14

  19. Looking for targets... t1 t2 Query . DZP . . tN Ligand Target Distance Global Z-score Local Z-score DZP t1 1.3 -1.6 -2.5 DZP t2 2.5 2.3 1.02 DZP tM 1.9 -1.6 -3.16 DZP tN 2.6 2.42 2.97 Sunday, September 7, 14

  20. Benchmarking • 232 approved FDA drugs co-crystallized with a protein. • Test-set = 6,282 true drug-protein pairs and 5,981 negative pairs. • Drug ID = 0.97 AUC • Anonymous compounds = 0.73 AUC Sunday, September 7, 14

  21. Applying the method, modeling genomes... 2. Binding-site inheritance 1. Modeling 3D model Mycobacterium smegmatis Mycobacterium tuberculosis Human proteome Mycobacterium bovis PDB templates Human Bacterial proteomes 3D reliable models 31,734 with overlapping 5,008 no overlapping Different Proteins 14,000 5,008 different proteins Inherited binding-sites 64,000 30,000 Sunday, September 7, 14

  22. Searching for Drugbank drugs interactions... Human Drugbank Bacterial Sunday, September 7, 14

  23. Searching for Drugbank drugs interactions... Human Drugbank Bacterial Sunday, September 7, 14

  24. Human Cyclooxygenase-1 targeted by NSAID drugs • 21 out of the 44 approved FDA drugs against COX-1 ( score > 0.85 ). • Human structure model from the sheep COX-1. • Predicted binding site includes Tyrosine 385. nAnnoLyze score Drug ID Drug name DB00712 Flurbiprofen 0.97 DB00328 Indomethacin 0.97 DB01600 Tiaprofenicacid 0.96 DB00870 Suprofen 0.96 DB00821 Carprofen 0.96 DB00788 Naproxen 0.96 DB00500 Tolmetin 0.94 DB00465 Ketorolac 0.94 DB00963 Bromfenac 0.92 DB00586 Diclofenac 0.91 DB06802 Nepafenac 0.90 DB01283 Lumiracoxib 0.90 DB00784 Mefenamicacid 0.89 DB00861 Diflunisal 0.88 DB04552 NiflumicAcid 0.88 DB00991 Oxaprozin 0.88 DB01050 Ibuprofen 0.87 DB00939 Meclofenamicacid 0.86 DB01399 Salsalate 0.86 DB01009 Ketoprofen 0.86 DB00605 Sulindac 0.85 Sunday, September 7, 14

  25. Sorafenib pathway targeting through binding of several protein Sunday, September 7, 14

  26. Sorafenib pathway targeting through binding of several protein KEGG Target Score Structure Pathway MAPK signaling Fox0 signaling VEGF signaling MAPK 14 0.99 Yes Rap1 signaling RIG-I-like receptor signaling Acute myeloid leukemia CDK19 - 0.97 No FLT1 0.90 Yes Ras signaling pathway MAPK signaling Ras signaling Rap1 signaling RAF 1 0.89 Yes VEGF signaling Fox0 signaling pathway Acute myeloid leukemia Fox0 signaling ARAF 0.88 Yes Acute myeloid leukemia CDK10 0.88 No - MAPK signaling Rap1 signaling BRAF 0.88 Yes Fox0 signaling Acute myeloid leukemia CDK8 - 0.87 Yes FLT3 0.86 Yes Acute myeloid leukemia MAPK 15 - 0.86 No Annotated ( Chembl, PubChem, Drugbank, PDB ) Not Annotated Sunday, September 7, 14

  27. Sorafenib pathway targeting through binding of several protein CDK8 BRAF MAPK 14 KEGG Target Score Structure Pathway MAPK signaling Fox0 signaling VEGF signaling MAPK 14 0.99 Yes Rap1 signaling RIG-I-like receptor signaling Acute myeloid leukemia CDK19 - 0.97 No FLT1 0.90 Yes Ras signaling pathway MAPK signaling Ras signaling Rap1 signaling RAF 1 0.89 Yes VEGF signaling Fox0 signaling pathway Acute myeloid leukemia Fox0 signaling ARAF 0.88 Yes Acute myeloid leukemia CDK10 0.88 No - MAPK signaling Rap1 signaling BRAF 0.88 Yes Fox0 signaling Acute myeloid leukemia CDK8 - 0.87 Yes FLT3 0.86 Yes Acute myeloid leukemia MAPK 15 - 0.86 No Annotated ( Chembl, PubChem, Drugbank, PDB ) Not Annotated Sunday, September 7, 14

  28. Antimicrobial drugs against Mycobacterium tuberculosis Target Prediction for an Open Access Set of Compounds Active against Mycobacterium tuberculosis ´nez 1,2 , George Papadatos 3 , Lun Yang 4 , Iain M. Wallace 3 , Vinod Kumar 4 , Francisco Martı ´nez-Jime Ursula Pieper 5 , Andrej Sali 5 , James R. Brown 4 * , John P. Overington 3 * , Marc A. Marti-Renom 1,2 * 1 Genome Biology Group, Centre Nacional d’Ana `lisi Geno `mica (CNAG), Barcelona, Spain, 2 Gene Regulation Stem Cells and Cancer Program, Centre for Genomic Regulation (CRG), Barcelona, Spain, 3 European Molecular Biology Laboratory – European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom, 4 Computational Biology, Quantitative Sciences, GlaxoSmithKline, Collegeville, Pennsylvania, United States of America, 5 Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California, United States of America Sunday, September 7, 14

  29. http://nannolyze.cnag.cnat Sunday, September 7, 14

  30. Acknowledgments Davide Baù Gireesh K. Bogu François le Dily Marc A. Marti-Renom David Dufour François Serra Michael Goodstadt Yasmina Cuartero COLLABORATORS Jim Brown (GSK) LLuís Ballell (GSK) John Overington (EBI-EMBL) Andrej Sali (UCSF) Anna Tramontano ( Sapienza University ) http://marciuslab.org http://integrativemodeling.org http://cnag.cat · http://crg.cat Sunday, September 7, 14

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