Secondary Screening: Vina Docking and Ranking by Binding Energy

Juan C. TorresCarolina MontanezGretel MontanezLuzmarie Reyes

Objective

To perform a secondary screening to identify the using AutoDock Vina.

Drug Discovery Strategy

Target Analysis Number, quality and distance of

“hot spots’

Primary Sequence Analysis; degree

conservation (NCBI/Swiss-Prot)

3D Structurewww.pdb.org

PyMol

Optimal target (s) for drug

development

BioAssay

Secondary Screening (AutoDock)

Primary Screening:Pharmacophore

Model(Ligand Scout)

High AffinityLead

Compounds

Identification of Top Hits

Identification of Lead Compounds.

(Ranking of binding energies)

. Pharmacophore identification and

Pharmacophore Model Generation (LigandScout)

Further refinement of Pharmacophore

Model

FTmap Chemical probes

cluster number & quality

Therapeutically relevant protein

targets

Biological Problem (Biomedically Relevant Condition or Process)

Drug-like Databases(≈ 9.5 million drugs)Lead-like Database(≈ 1.3 million drugs)

Part 1: Run the Docking Screening (AutoDock Vina)

Part 2: Obtain the Results/ Ranking of Top Hits

Part 3: Analyze Interactions using Auto Dock tools

Pharmacophore Generation

Part 4: Possible Model Refinement

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.