Hybrid Virtual Screening Approach to Predict Novel Natural Compounds against HIV-1 CCR5.

Area of Science:

• Computational chemistry
• Drug discovery
• Virology

Background:

• Human immunodeficiency virus (HIV) entry relies on the CCR5 coreceptor.
• Current CCR5 antagonist maraviroc has limitations and adverse effects.
• Need for novel CCR5 inhibitors with improved safety profiles.

Purpose of the Study:

• To virtually screen natural product databases for novel CCR5 inhibitors.
• To identify natural compounds with potential therapeutic advantages over synthetic drugs.

Main Methods:

• Developed and validated a pharmacophore model for CCR5 antagonists.
• Screened large natural product databases using the pharmacophore model and molecular docking.
• Conducted molecular dynamics simulations and pharmacokinetic predictions.

Main Results:

• Identified three natural compounds with interactions and binding energies comparable to maraviroc.
• Demonstrated comparable complex stability through molecular dynamics simulations.
• One lead compound showed favorable predicted absorption and minimal metabolism.

Conclusions:

• Computational screening identified promising natural CCR5 inhibitors.
• One lead compound offers potential for improved bioavailability and reduced interaction risks.
• This hybrid approach effectively discovers novel drug-like molecules from natural products.