Accelerated Combinatorial Drug Design for Human Immunodeficiency Virus Resistance through Seeded Multisite...

Area of Science:

• Computational chemistry and molecular dynamics
• Drug discovery and development
• Virology and infectious diseases

Background:

• Drug-resistant mutations in human immunodeficiency virus (HIV) present a major hurdle for effective therapeutics.
• Understanding molecular mechanisms of drug resistance is key for designing next-generation antiviral drugs.

Purpose of the Study:

• To explore a large chemical space of HIV-1 Reverse Transcriptase (HIV-RT) inhibitors against wild-type and resistant strains.
• To identify resilient drug scaffolds and substituents that maintain binding affinity across diverse mutations.
• To establish an efficient computational framework for predicting drug resistance landscapes.

Main Methods:

• Application of multisite λ-dynamics (MSλD) to simulate over 12,000 protein-ligand combinations.
• Exploration of indole and indolizine inhibitor scaffolds against wild-type HIV-RT and key resistance mutations (Y181, Y188).
• Introduction of a bias seeding method leveraging solvent-phase free energy simulations for efficient sampling.

Main Results:

• Smaller substituents (H, F, Cl) are preferred on both indole and indolizine scaffolds.
• Binding affinity rankings of top inhibitors are conserved across mutations, indicating potential universal binders.
• The indolizine scaffold demonstrates higher intrinsic resilience against mutations compared to the indole scaffold.
• The Y188I mutation is identified as a resistance hotspot significantly reducing binding affinity.

Conclusions:

• MSλD provides a powerful framework for understanding drug resistance and designing robust antiviral therapies.
• The study identifies promising inhibitor scaffolds and substituents for overcoming HIV-1 drug resistance.
• Computational approaches are crucial for guiding the development of resilient therapies against rapidly evolving pathogens.