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  • l
  • Here is the top lead compund obtained in the secondary screening with a binding energy of -10.6.That drug was preveiously known. (comercially available)
  • Here are the different amino acids that are interacting with the drug.
  • Model of the drug with the chemical featuresThis was other drug, it wasn’t that different.
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Transcript

  • 1. Secondary Screening: Vina Docking and Ranking by Binding Energy Juan C. Torres Carolina Montanez Gretel Montanez Luzmarie Reyes
  • 2. ObjectiveTo perform a secondary screening to identify theusing AutoDock Vina.
  • 3. Drug Discovery Strategy Biological Problem (Biomedically Relevant Condition or Process) Primary Sequence Optimal target (s) Analysis; degree for drug development conservationTherapeutically (NCBI/Swiss-Prot) FTmap Target Analysis Chemical probes Number, qualityrelevant protein cluster and distance of targets number & quality “hot spots’ Pharmacophore . 3D Structure identification and www.pdb.org PyMol Pharmacophore Model Generation (LigandScout) Primary Screening: Pharmacophore Drug-like Databases Model (≈ 9.5 million drugs) (Ligand Scout) Lead-like Database Further refinement (≈ 1.3 million drugs) of Pharmacophore Model Identification of Top Hits Secondary Screening (AutoDock) Identification of High Affinity Lead Compounds. BioAssay Lead (Ranking of binding Compounds energies)
  • 4. Part 1: Run the Docking Screening (AutoDock Vina)
  • 5. Part 2: Obtain the Results/ Ranking of Top Hits
  • 6. Part 3: Analyze Interactions using Auto Dock tools
  • 7. Pharmacophore Generation
  • 8. Part 4: Possible Model Refinement
  • 9. Conclusion• Our drug model did not have the same chemical features as the one generated and used in the primary screening.• The initial model can be refined.