Conventional protein:ligand crystallographic refinement uses stereochemistry restraints coupled with a rudimentary energy functional to ensure the correct geometry of the model of the macromolecule—along with any bound ligand(s)—within the context of the experimental, X-ray density. These methods generally lack explicit terms for electrostatics, polarization, dispersion, hydrogen bonds, and other key interactions, and instead they use pre-determined parameters (e.g. bond lengths, angles, and torsions) to drive structural refinement. In order to address this deficiency and obtain a more complete and ultimately more accurate structure, we have developed an automated approach for macromolecular refinement based on a two layer, QM/MM (ONIOM) scheme as implemented within our DivCon Discovery Suite and "plugged in" to two mainstream crystallographic packages: PHENIX and BUSTER. This implementation is able to use one or more region layer(s), which is(are) characterized using linear-scaling, semi-empirical quantum mechanics, followed by a system layer which includes the balance of the model and which is described using a molecular mechanics functional. In this work, we applied our Phenix/DivCon refinement method—coupled with our XModeScore method for experimental tautomer/protomer state determination—to the characterization of structure sets relevant to structure-based drug design (SBDD). We then use these newly refined structures to show the impact of QM/MM X-ray refined structure on our understanding of function by exploring the influence of these improved structures on protein:ligand binding affinity prediction (and we likewise show how we use post-refinement scoring outliers to inform subsequent X-ray crystallographic efforts). Through this endeavor, we demonstrate a computational chemistry ↔ structural biology (X-ray crystallography) "feedback loop" which has utility in industrial and academic pharmaceutical research as well as other allied fields.

传统的蛋白质：配体晶体学精修使用立体化学约束和基本的能量泛函来确保大分子模型的正确几何形状 - 以及任何结合的配体 - 在实验的 X 射线密度范围内。这些方法通常缺乏关于静电、极化、色散、氢键和其他关键相互作用的明确术语，而是使用预先确定的参数（例如键长、角度和扭转）来驱动结构改进。为了解决这一缺陷并获得更完整且最终更准确的结构，我们开发了一种基于两层 QM/MM (ONIOM) 方案的大分子细化自动化方法，该方案在我们的 DivCon Discovery Suite 中实施并“插入” 到两个主流的晶体学包：PHENIX 和 BUSTER。此实现能够使用一个或多个区域层，其特征是使用线性标度、半经验量子力学，然后是一个系统层，其中包括模型的平衡，并使用分子力学功能。在这项工作中，我们将我们的 Phenix/DivCon 细化方法与我们用于实验互变异构体/原体状态确定的 XModeScore 方法相结合，用于表征与基于结构的药物设计 (SBDD) 相关的结构集。然后，我们通过探索这些改进结构对蛋白质的影响，使用这些新改进的结构来展示 QM/MM X 射线改进结构对我们对功能理解的影响：配体结合亲和力预测（我们同样展示了我们如何使用改进后的评分异常值来为后续的 X 射线晶体学工作提供信息）。通过这一努力，我们展示了一种计算化学↔ 结构生物学（X 射线晶体学）“反馈回路”，它在工业和学术药物研究以及其他相关领域中具有实用性。