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.

人类免疫缺陷病毒（Human Immunodeficiency Virus, HIV）蛋白酶抑制剂（protease inhibitors, PIs）是高效抗反转录病毒治疗（highly active anti-retroviral therapy, HAART）的关键组成部分，此类药物可通过阻断HIV蛋白酶的催化位点，阻止HIV病毒颗粒的成熟。然而，随着PI类药物在临床应用已达二十余年，目前所有获批的PI类药物均已对应出现耐药HIV突变株。因此，持续研发新型PI类药物，对于对抗现有耐药HIV毒株、为未来患者提供治疗方案均具有重要意义。

本研究提出一种HIV PI类药物设计策略，用于筛选结合能分布主要依赖于与野生型及多种耐药突变株中保守蛋白酶残基相互作用的候选PI类化合物。基于该策略，我们搭建了一套完整的虚拟筛选流程，涵盖组合文库构建、组合分子对接、基于MM/GBSA的重打分，以及结合能分布导向的重新排序环节。

我们以洛匹那韦（lopinavir）为模型化合物，对其两个官能团进行修饰以验证该策略。从初始的751689个候选分子中，通过该筛选流程选出18个候选抑制剂用于实验验证。通过半数抑制浓度（IC50）检测与耐药性预测分析，最终成功鉴定出两个兼具HIV蛋白酶抑制活性，且在2382株HIV突变株中展现出更优耐药谱的配体。

本研究验证了在虚拟筛选阶段整合MM/GBSA能量分析与耐药性信息的可行性，为未来HIV药物研发以及利用虚拟筛选技术对抗药物耐药性提供了新的研究思路。