In silico

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In silico

  1. 1. In-Silico Drug Discovery and Development.Project: In-Silico Discovery of InfluenzaVirus Polymerase PB1-PB2 ProteinComplex InhibitorsCarla J. Figueroa1,2, Crystal K. Colón1,2Dr. Hector M. Maldonado31RISE Program, 2Univeristy of Puerto Rico at Cayey;3Universidad Central del Caribe, Medical School
  2. 2. 2ProblemHypothesisSignificanceCurrent treatment options are limited to Tamiflu and Relenza, withmany reported resistant viral strains.Highly conserved protein-protein interaction interface, present inInfluenza A Virus Polymerase subunits (PB1 and PB2), representpotencial new targets for antiviral drug development.Influenza is an Infectous Disease caused by a virus that kills hundreds ofthousands each year (seasonal epidemics alone), with the iinfluenza-Aserotype implicated in all INF-A related pandemics.
  3. 3. 3D Structurewww.pdb.orgPyMol3A1GBioAssaySecondary Screening: (AutoDock)Primary Screening: PharmacophoreModel (ZincPharmer)High AffinityLeadCompoundsIdentification of Top HitsIdentification ofLead Compounds.(Ranking of bindingenergies)Pharmacophoreidentification andPharmacophore ModelGeneration (LigandScout)Therapeuticallyrelevant proteinTargets:PB1-PB2Biological Problem(Influenza Virus)Drug-likeDatabases(17 milliondrug-likecompounds)BenzeneMappingDrug-likeDatabases(17 milliondrug-likecompounds)
  4. 4. Methodology4Software Used:• PyMOL Molecular Graphics System v1.3• AutoDock (protein-protein docking software)• Auto Dock Tools: Graphical Interfase for AutoDock• AutoDock Raccoon: an automated tool for preparing AutoDock virtual screening.• AutoDock Vina: improving the speed and accuracy of docking with a new scoringfunction, efficient optimization and multithreading.• LigandScout: Advanced Pharmacophore Modeling and Screening of DrugDatabases. Used:• SwissProt/TrEMBL; (Protein knowledgebase and Computer-annotatedsupplement to Swiss-Prot)• National Center for Biotechnology Information (NCBI), Basic Local AlignmentSearch Tool (BLAST)• Research Collaboratory for Structural Bioinformatics (RCSB)• ZINC: A free database for virtual screening:
  6. 6. 6Ben 01Ben 36Ben 41 Ben 79Ben 93Ben 131136417993 131Generation of Pharmacophore model: merged features from benzene mapping.Final Pharmacophore model
  7. 7. Top 50 Hits of first round of screening ofInfluenza A (PB1-PB2 target).# Drug Code Binding energy (kcal/mol)1 IPB1-1 -10.52 IPB1-2 -10.23 IPB1-3 -10.24 IPB1-4 -105 IPB1-5 -106 IPB1-6 -9.97 IPB1-7 -9.98 IPB1-8 -9.99 IPB1-9 -9.810 IPB1-10 -9.811 IPB1-11 -9.812 IPB1-12 -9.813 IPB1-13 -9.814 IPB1-14 -9.815 IPB1-15 -9.816 IPB1-16 -9.717 IPB1-17 -9.718 IPB1-18 -9.719 IPB1-19 -9.720 IPB1-20 -9.721 IPB1-21 -9.722 IPB1-22 -9.623 IPB1-23 -9.624 IPB1-24 -9.625 IPB1-25 -9.6# Drug Code Binding energy (kcal/mol)26 IPB1-26 -9.627 IPB1-27 -9.628 IPB1-28 -9.629 IPB1-29 -9.630 IPB1-30 -9.531 IPB1-31 -9.532 IPB1-32 -9.533 IPB1-33 -9.534 IPB1-34 -9.535 IPB1-35 -9.536 IPB1-36 -9.537 IPB1-37 -9.538 IPB1-38 -9.539 IPB1-39 -9.540 IPB1-40 -9.541 IPB1-41 -9.542 IPB1-42 -9.543 IPB1-43 -9.544 IPB1-44 -9.445 IPB1-45 -9.446 IPB1-46 -9.447 IPB1-47 -9.448 IPB1-48 -9.449 IPB1-49 -9.450 IPB1-50 -9.47
  8. 8. Conclusions• High affinity clusters or “Hot-spots” were identifiedwith benzene mapping• With this information an initial Pharmacophore modelwas developed• This Pharmacophore model was used to perform aprimary screening (filtering) of a large database• 80,231 compounds were identified in that initialscreening that fulfill all requirements of this model• Secondary screening (docking) was performed• The result were organized by binding energy rankingand the top hits identified8