Using Hierarchical Virtual Screening To Combat Drug Resistance of the HIV‑1 Protease

Human
immunodeficiency virus (HIV) protease inhibitors (PIs) are
important components of highly active anti-retroviral therapy (HAART)
that block the catalytic site of HIV protease, thus preventing maturation
of the HIV virion. However, with two decades of PI prescriptions in
clinical practice, drug-resistant HIV mutants have now been found
for all of the PI drugs. Therefore, the continuous development of
new PI drugs is crucial both to combat the existing drug-resistant
HIV strains and to provide treatments for future patients. Here we
purpose an HIV PI drug design strategy to select candidate PIs with
binding energy distributions dominated by interactions with conserved
protease residues in both wild-type and various drug-resistant mutants.
On the basis of this strategy, we have constructed a virtual screening
pipeline including combinatorial library construction, combinatorial
docking, MM/GBSA-based rescoring, and reranking on the basis of the
binding energy distribution. We have tested our strategy on lopinavir
by modifying its two functional groups. From an initial 751 689
candidate molecules, 18 candidate inhibitors were selected using the
pipeline for experimental validation. IC50 measurements
and drug resistance predictions successfully identified two ligands
with both HIV protease inhibitor activity and an improved drug resistance
profile on 2382 HIV mutants. This study provides a proof of concept
for the integration of MM/GBSA energy analysis and drug resistance
information at the stage of virtual screening and sheds light on future
HIV drug design and the use of virtual screening to combat drug resistance.