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限制与基本能量泛函相结合，以确保大分子模型以及任何结合的配体在实验 X 射线密度的背景下具有正确的几何形状。这些方法通常缺乏静电、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）来驱动结构细化。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) 方案的大分子精炼自动化方法，该方案在我们的 DivCon Discovery Suite 中实施并“插入” ”到两种主流晶体学软件包：PHENIX 和 BUSTER。该实现能够使用一个或多个区域层，其特征是使用线性缩放、半经验量子力学，然后是系统层，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 Phenix/DivCon 精修方法与用于实验互变异构体/原体状态测定的 XModeScore 方法结合起来，来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的结构对蛋白质：配体结合亲和力预测的影响，使用这些新精炼的结构来显示 QM/MM X 射线精炼结构对我们理解功能的影响（并且我们同样展示了如何使用后处理）细化评分异常值，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具有实用性。