Hierarchical database screenings for HIV-1 reverse transcriptase using a pharmacophore model, rigid docking,...

Area of Science:

• Computational chemistry
• Drug discovery
• Molecular modeling

Background:

• Human immunodeficiency virus type 1 reverse transcriptase (HIV-1 RT) is a critical target for antiviral drug development.
• Existing drug discovery methods require efficient strategies for identifying novel inhibitors.
• Virtual screening plays a crucial role in accelerating the identification of potential drug leads.

Purpose of the Study:

• To develop and validate an efficient hierarchical database screening strategy for identifying drug leads against HIV-1 RT.
• To assess the effectiveness of a multi-step virtual screening approach combining pharmacophore modeling, rigid docking, solvation docking, and MM-PB/SA calculations.
• To discover novel chemical entities with potential inhibitory activity against HIV-1 RT.

Main Methods:

• Hierarchical virtual screening approach.
• Pharmacophore modeling and database screening.
• Multiple-conformation rigid docking and solvation docking.
• Molecular mechanics-Poisson-Boltzmann/surface area (MM-PB/SA) calculations for binding free energy estimation.

Main Results:

• Achieved a 25-fold enrichment factor after the initial three screening filters.
• Predicted a 41% hit rate for the complete four-filter strategy in a control test.
• Identified 10 promising hits with MM-PB/SA binding free energies better than -6.8 kcal/mol, including a top candidate (HIT15) with -17.0 kcal/mol.

Conclusions:

• The developed hierarchical multiple-filter database searching strategy is effective for drug lead exploration.
• This approach significantly enhances the efficiency of identifying potential inhibitors for HIV-1 RT.
• The validated strategy offers an attractive method for accelerating antiviral drug discovery efforts.