School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287, United States.
Department of Chemistry, Memorial University of Newfoundland, St. John's, NL A1C 5S7, Canada.
J Chem Inf Model. 2020 May 26;60(5):2591-2604. doi: 10.1021/acs.jcim.9b01167. Epub 2020 Mar 30.
Despite significant advances in resolution, the potential for cryo-electron microscopy (EM) to be used in determining the structures of protein-drug complexes remains unrealized. Determination of accurate structures and coordination of bound ligands necessitates simultaneous fitting of the models into the density envelopes, exhaustive sampling of the ligand geometries, and, most importantly, concomitant rearrangements in the side chains to optimize the binding energy changes. In this article, we present a flexible-fitting pipeline where molecular dynamics flexible fitting (MDFF) is used to refine structures of protein-ligand complexes from 3 to 5 Å electron density data. Enhanced sampling is employed to explore the binding pocket rearrangements. To provide a model that can accurately describe the conformational dynamics of the chemically diverse set of small-molecule drugs inside MDFF, we use QM/MM and neural-network potential (NNP)/MM models of protein-ligand complexes, where the ligand is represented using the QM or NNP model, and the protein is represented using established molecular mechanical force fields (e.g., CHARMM). This pipeline offers structures commensurate to or better than recently submitted high-resolution cryo-EM or X-ray models, even when given medium to low-resolution data as input. The use of the NNPs makes the algorithm more robust to the choice of search models, offering a radius of convergence of 6.5 Å for ligand structure determination. The quality of the predicted structures was also judged by density functional theory calculations of ligand strain energy. This strain potential energy is found to systematically decrease with better fitting to density and improved ligand coordination, indicating correct binding interactions. A computationally inexpensive protocol for computing strain energy is reported as part of the model analysis protocol that monitors both the ligand fit as well as model quality.
尽管分辨率有了显著提高，但冷冻电子显微镜（Cryo-EM）在确定蛋白质-药物复合物结构方面的潜力仍未实现。要确定准确的结构和结合配体的配位，需要将模型同时拟合到密度包络中，对配体几何形状进行详尽的采样，最重要的是，同时对侧链进行重排以优化结合能变化。在本文中，我们提出了一种灵活拟合的管道，其中分子动力学柔性拟合（MDFF）用于从 3 到 5Å 电子密度数据中精修蛋白质-配体复合物的结构。增强采样用于探索结合口袋的重排。为了提供一个能够准确描述 MDFF 中小分子药物化学多样性构象动力学的模型，我们使用蛋白质-配体复合物的量子力学/分子力学（QM/MM）和神经网络势（NNP）/分子力学（MM）模型，其中配体使用 QM 或 NNP 模型表示，蛋白质使用已建立的分子力学力场（例如 CHARMM）表示。即使输入中等至低分辨率的数据，该管道也能提供与最近提交的高分辨率冷冻电镜或 X 射线模型相当或更好的结构。NNP 的使用使算法对搜索模型的选择更具鲁棒性，为配体结构确定提供了 6.5Å 的收敛半径。预测结构的质量也通过配体应变能的密度泛函理论计算来判断。发现该应变势能与更好的密度拟合和改善的配体配位呈系统下降趋势，表明存在正确的结合相互作用。作为模型分析协议的一部分，报告了一种计算应变能的计算成本低廉的协议，该协议同时监测配体拟合和模型质量。
