GPCR Trajectory files with modulators

G-protein-coupled receptors (GPCRs) make up the largest superfamily of human membrane proteins and represent primary targets of ∼1/3 of currently marketed drugs. Allosteric modulators have emerged as more selective drug candidates compared with orthosteric agonists and antagonists. However, many X-ray and cryo-EM structures of GPCRs resolved so far exhibit negligible differences upon the binding of positive and negative allosteric modulators (PAMs and NAMs). The mechanism of dynamic allosteric modulation in GPCRs remains unclear. In this work, we have systematically mapped dynamic changes in free energy landscapes of GPCRs upon binding of allosteric modulators using the Gaussian accelerated molecular dynamics (GaMD), deep learning (DL), and free energy prOfiling Workflow (GLOW). GaMD simulations were performed for a total of 66 μs on 44 GPCR systems in the presence and absence of the modulator. DL and free energy calculations revealed significantly reduced dynamic fluctuations and conformational space of GPCRs upon modulator binding. While the modulator-free GPCRs often sampled multiple low-energy conformational states, the NAMs and PAMs confined the inactive and active agonist-G-protein-bound GPCRs, respectively, to mostly only one specific conformation for signaling. Such cooperative effects were significantly reduced for binding of the selective modulators to “non-cognate” receptor subtypes. Therefore, GPCR allostery exhibits a dynamic “conformational selection” mechanism. In the absence of available modulator-bound structures as for most current GPCRs, it is critical to use a structural ensemble of representative GPCR conformations rather than a single structure for compound docking (“ensemble docking”), which will potentially improve structure-based design of novel allosteric drugs of GPCRs.

G蛋白偶联受体（G-protein-coupled receptors, GPCRs）是人类最大的膜蛋白超家族，亦是当前约三分之一上市药物的核心作用靶点。相较于正位激动剂与拮抗剂，变构调节剂已成为兼具更高选择性的药物候选分子。然而，目前已解析的大量GPCR的X射线与冷冻电镜（cryo-electron microscopy, cryo-EM）结构中，正、负变构调节剂（positive allosteric modulators, PAMs；negative allosteric modulators, NAMs）结合前后的结构差异微乎其微。GPCR的动态变构调控机制至今仍未明确。本研究借助高斯加速分子动力学（Gaussian accelerated molecular dynamics, GaMD）、深度学习（deep learning, DL）及自由能分析流程（free energy profiling workflow, GLOW），系统刻画了GPCR结合变构调节剂后的自由能景观动态变化。本研究针对44个GPCR体系开展了总计66微秒的GaMD模拟，涵盖有无调节剂结合的两种状态。深度学习与自由能计算结果表明，结合调节剂后，GPCR的动态波动幅度与可及构象空间均显著缩减。未结合调节剂的GPCR通常可采样多种低能构象状态，而NAMs与PAMs则分别将静息态、激活态的G蛋白结合型GPCR限制为仅一种主导信号转导的特定构象。当选择性调节剂结合至“非同源”受体亚型时，这类协同调控效应会显著减弱。综上，GPCR的变构调控遵循动态“构象选择”机制。针对当前多数尚无调节剂结合结构的GPCR而言，采用代表性GPCR构象集而非单一结构开展化合物对接（即“集束对接”）至关重要，这有望优化GPCR新型变构药物的基于结构设计流程。

搜集汇总

背景与挑战

背景概述

该数据集包含G蛋白偶联受体（GPCRs）在变构调节剂存在和不存在情况下的动态轨迹文件，通过高斯加速分子动力学（GaMD）模拟、深度学习和自由能分析揭示了GPCRs的动态变构调节机制。数据集支持变构药物设计的结构基础研究，特别强调了使用结构集合进行化合物对接的重要性。