20120615_Granatum_COST_v2
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Presentation about the Virtual Screening Scientific Workflow for Cancer Chemoprevention developed within the GRANATUM project.
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20120615_Granatum_COST_v2
- 1. Towards a modular Web-based Workflow environment for enabling large scale Virtual Screening in Cancer Chemoprevention Research 19 June 2012 COST Conference Personalised Medicine: Better Healthcare for the Future Christos Kannas Computer Science Dept., University of Cyprus
- 2. June 19, 2012 2 Outline • About the Project • Overview of the Project • Objectives • State of the Art Review • Implementation • Virtual Screening Process • Predictive Model Preparation • In-Silico Tools and Methods • Early in silico experiments • Concluding Remarks
- 3. June 19, 2012 3 About the Project • The vision of the GRANATUM project is to: • bridge the information, knowledge and collaboration gap among biomedical researchers in Europe (at least), • ensure that the biomedical scientific community has homogenized, integrated access to the globally available information and data resources needed to perform complex cancer chemoprevention experiments, and conduct studies on large-scale datasets. • The GRANATUM project is partially funded by the European Commission under the Seventh Framework Programme in the area of Virtual Physiological Human (ICT-2009.5.3). • http://www.granatum.org/
- 4. June 19, 2012 4 Overview of the Project
- 5. June 19, 2012 5 Objectives • Design a scientific algorithmic workflow for the development of in silico chemoprevention models. • Implement workflow(s) for the selection of promising chemopreventive agents. • Connect the custom in-silico models for compound selection to other datasets, and evidence included in the Linked Biomedical Data Space. • Test the performance of custom in-silico models.
- 6. June 19, 2012 6 State Of the Art • Significant overlap of chemoprevention and traditional drug discovery process (DDP). • Special case with additional constraints, e.g. no toxicity • In Silico Models and Tools: heavily borrowing from DDP. SOA Review Online resources Databases (e.g. ChemBL), journals, reports, … Infrastructure tools Chemoinformatics toolkits (e.g. RDKit and CDK): compound representation, property and descriptor calculation, substructure mining, … Advanced comp. chem. Biological property predictive models, compound 3D conformations, docking tools, … Machine learning Classification and regression methods, available open source libraries Scientific workflow Knime, Taverna, Galaxy, … systems
- 7. June 19, 2012 7 Virtual Screening Process Template Output • Storage Postprocessing • Visualization • Cleaning • Formatting Processing • Attribute filter • Similarity search Preprocessing • Substructure • File format Search transformations • Docking • Standardization • Predictive • Descriptor Models Input Calculation • Linked • Fragmentation Biomedical Space • Files
- 8. June 19, 2012 8 Predictive Model Preparation Template Chemical data Algorithm Biological • Algorithm data parameters Predictive Model
- 9. June 19, 2012 9 Chemopreventive Property Models Anti – Anti – Anti – metastatic Estrogenic Anti – oxidant Apoptotic inflammatory proliferating / Anti – agiogenic Activity Anti-apoptotic Cyclin D1 members of COX-2 but not COX-2 down- ER-alpha Direct Effect down- Bcl-2 family COX-1 inhibitor regulation binding affinity regulation down- regulation IAP family Reduction of Her-2 down- VEGF down- ER-beta Indirect Effect down- TNF-a regulation regulation binding affinity regulation Caspase up- Direct/Indirect Reduction of Cyclin E down- PDGF down- ER-alpha/beta regulation/activ Effect LOX regulation regulation binding affinity ation Induction of EGFR down- No affinity AP-1 regulation Reduction of Estrogen Interleukins Antagonists Selective Estrogen Receptor Modulators (SERMs) Estrogen Receptor Modulators
- 10. June 19, 2012 10 In Silico Tools and Methods • Generic Chemoinformatics Tool: • E-Health Lab and collaborators resources • RDKit • Docking Experiment Tools: • AutoDock Vina • Chil2 GlamDock • Data Mining & Statistics Tools: • In house tools • R • Scientific Workflow System: • Galaxy
- 11. June 19, 2012 11 Early In Silico Experiments • In silico tool & models validation • Steps: • Prepare compound dataset • Mix of natural products and known inhibitors (4% actives) • Implementation/application of predictive models • Rule of Five • Toxicity model • Implementation/application of docking model • ER-alpha • Compound prioritization • Top selections visualization/evaluation
- 12. June 19, 2012 12 Virtual Screening Process Example Natural products Calculate collection + physicochemical Rule of Five filter known ER-alpha molecular inhibitors descriptors Compound prioritization; Docking to ER- Toxicity model Report on top alpha selections
- 13. June 19, 2012 13 Cytotoxicity Predictive Model Cytotoxicity Cytotoxicity Clean Oral Drug- Morgan Predictive Dataset Molecules like Filtering Fingerprints Model • Source : The • Remove Salts • HBA <= 10 • Bit Vector 2048- • Cytotoxicity Scripps • HBD <= 5 bits Bio-Chemical Research • Molecular data Institute Weight <=500 • SVM: Molecular • Kernel: Linear • logP <= 5 Screening • Stratified K- Center Fold: • PubChem Bio- • 5-folds Assay: AID 464 • 10-folds • Tested: 706 • Active: 331 • Inactive: 375
- 14. June 19, 2012 14 Virtual Screening Process Example Demo Dataset Result: 2451 OK, Remove 85 (valence errors, empty Known ER-Alpha Inhibitors (42) Indofine Dataset (2494) molecule block) Clean Molecules Remove Salts 2451 molecules (42 Known, 2409 Indofine) Oral Druglike Filtering HBA <= 10 HBD <= 5 Molecular Weight <=500 logP <= 5 Result: 2035 pass, 416 not pass Calculate Morgan Fingerprints row-20-top-known row-36-top-known row-42-top-known Bit Vector 2048-bits Cytotoxicity (Predictive Model) SVM Classifier Trained with Bio-Assay 464 dataset Predict: 2451 molecules ER-Alpha Docking (GlamDock) ER-Alpha Protein 2451 molecules for docking experiments Ranked order of Cytotoxicity Prediction, Docking and Oral Druglikness Filtering results row-1988-top-unknown row-729-top-unknown row-1652-top-unknown
- 15. June 19, 2012 15 Docking results: known ER inhibitors row-20-top-known
- 16. June 19, 2012 16 Docking results: known ER inhibitors row-36-top-known
- 17. June 19, 2012 17 Docking results: known ER inhibitors row-42-top-known
- 18. June 19, 2012 18 Docking results: Indofine compounds row-729-top-unknown
- 19. June 19, 2012 19 Docking results: Indofine compounds row-1652-top-unknown
- 20. June 19, 2012 20 Docking results: Indofine compounds row-1988-top-unknown
- 21. June 19, 2012 21 Concluding Remarks • Support of chemopreventive specific predictive models. • Initial promising results on ERa (based on Indofine dataset). • Modular architecture and workflow management. • Integrated with additional tools within the Granatum Project. • Linked Biomedical Data Space. • Social Collaborative Workspace. • Product Release: • Advanced Prototype Version: October 2012 • Final Version: April 2013
- 22. June 19, 2012 22
Hinweis der Redaktion
- Emphasize Chemopreventive specific Predictive Models.
- Expert User with knowledge on biology, chemistry, data mining and knowledge extraction algorithms.
- Search from LBS, get ZINC database.Molecular DescriptorsGet compounds that might be active to estrogen receptors.Prepare files required for docking.Docking to ER-a using AutoDock Vina.Gather results.