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虚拟现实中的交互式分子动力学用于精确的柔性蛋白配体对接。

Interactive molecular dynamics in virtual reality for accurate flexible protein-ligand docking.

机构信息

Intangible Realities Laboratory, School of Chemistry, University of Bristol, Bristol, England, United Kingdom.

Department of Computer Science, University of Bristol, Bristol, England, United Kingdom.

出版信息

PLoS One. 2020 Mar 11;15(3):e0228461. doi: 10.1371/journal.pone.0228461. eCollection 2020.

Abstract

Simulating drug binding and unbinding is a challenge, as the rugged energy landscapes that separate bound and unbound states require extensive sampling that consumes significant computational resources. Here, we describe the use of interactive molecular dynamics in virtual reality (iMD-VR) as an accurate low-cost strategy for flexible protein-ligand docking. We outline an experimental protocol which enables expert iMD-VR users to guide ligands into and out of the binding pockets of trypsin, neuraminidase, and HIV-1 protease, and recreate their respective crystallographic protein-ligand binding poses within 5-10 minutes. Following a brief training phase, our studies shown that iMD-VR novices were able to generate unbinding and rebinding pathways on similar timescales as iMD-VR experts, with the majority able to recover binding poses within 2.15 Å RMSD of the crystallographic binding pose. These results indicate that iMD-VR affords sufficient control for users to carry out the detailed atomic manipulations required to dock flexible ligands into dynamic enzyme active sites and recover crystallographic poses, offering an interesting new approach for simulating drug docking and generating binding hypotheses.

摘要

模拟药物结合和解离是一项挑战,因为将结合态和未结合态分离的崎岖能量景观需要广泛的采样,这会消耗大量的计算资源。在这里,我们描述了虚拟现实中的交互式分子动力学 (iMD-VR) 在柔性蛋白配体对接中的准确、低成本策略的应用。我们概述了一个实验方案,使经验丰富的 iMD-VR 用户能够将配体引导到和离开胰蛋白酶、神经氨酸酶和 HIV-1 蛋白酶的结合口袋中,并在 5-10 分钟内重新创建它们各自的晶体蛋白-配体结合构象。在简短的培训阶段之后,我们的研究表明,iMD-VR 新手能够在与 iMD-VR 专家相似的时间尺度上生成解吸和再结合途径,大多数新手能够将结合构象恢复到晶体结合构象的 2.15Å RMSD 以内。这些结果表明,iMD-VR 为用户提供了足够的控制,以进行将柔性配体对接进入动态酶活性位点并恢复晶体构象所需的详细原子操作,为模拟药物对接和生成结合假说提供了一种有趣的新方法。

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