Quantum refinement for improvement of metalloenzyme crystal structures

项目摘要 铁金属蛋白是一大类酶，参与人体必需的许多化学物质。 健康。在许多这样的蛋白质中，铁硫键在两种蛋白质之间的相互作用中发挥着重要的功能作用。 铁的活性中心和一个含硫的底物，以及作为电子转移中心的[Fe-S]团簇。 这些酶参与抗生素的生物合成、硫代谢和细胞呼吸。 因此，了解这些酶在生理相关条件下的催化机制 对于回答与健康相关的关键问题至关重要。最近，X射线自由电子激光器(XFELs)已经 出现了一种在室温下收集酶晶体的时间分辨X射线衍射数据的方法 而不会造成辐射损害。这使得实时跟踪催化反应成为可能。 生理条件，提供了对机制的重要洞察。然而，一个重要的问题是 XFEL X射线衍射数据的分析仍有待解决。求精是解决结构问题的重要步骤 根据从X射线衍射仪获得的电子密度图得到的蛋白质。标准晶体细化 程序使用立体化学约束来帮助结构溶液，并确保最终结构是 化学上合理。这些限制对于金属-配体键来说往往不准确，这可能会使 精细化结果，特别是对于中分辨率(~1.6-2.4？)的结构。此外，这些 立体化学约束不能准确地反映金属周围电子密度的潜在变化。 地点。电荷分布的这种微妙变化可能会导致机械上重要的结构变化； 因此，更准确地包括这些影响的精炼是必不可少的，特别是对于高共价部分 如Fe-S键和团簇。量子力学(QM)计算可以更准确地描述 共价金属-配体键和团簇的电子密度，从而为标准提供了一个有吸引力的补充 完善程序。在这项提案中，我将开发和校准一种量子精化方法，用于 作为模块集成到结晶学结构分析软件平台PHENIX中。该方法将 用密度泛函理论(DFT)对金属活性中心及其周围配体进行计算 获得晶体细化过程中使用的梯度，并将以模型铁为基准- 硫磺蛋白。然后我将把这种方法应用于两种金属蛋白的时间分辨XFEL X射线衍射数据， 收集异青霉素N合成酶(IPNS)和耐氧膜结合型[Ni-Fe]氢酶(MBH) 在它们的氧气反应过程中。这些结构的量子精细化将结合电子技术中的微妙变化 催化过程中的结构对结构的变化，提供更准确的结构并阐明其机理 异青霉素N在IPNS中的生物合成及其在MBH中耐O2的机制。这方面的发展 该方法对进一步了解金属酶的作用机制具有重要意义。

Project Summary Iron metalloproteins are a large class of enzymes that are involved in many chemistries essential to human health. In many of these proteins, iron sulfur bonds play an important functional role, both in interactions between the iron active site and a sulfur-containing substrate and in [Fe-S] clusters that serve as electron transfer centers. Such enzymes are involved in the biosynthesis of antibiotics, sulfur metabolism, and cellular respiration. Understanding these enzymes’ mechanisms of catalysis under physiologically relevant conditions is thus essential for answering critical health-related questions. Recently, X-ray free electron lasers (XFELs) have emerged as a way to collect time-resolved X-ray diffraction (XRD) data on enzyme crystals at room temperature without causing radiation damage. This has made it possible to follow catalytic reactions in real time under physiological conditions, providing significant insight into mechanism. However, an important issue with the analysis of XFEL XRD data still needs to be resolved. Refinement is an important step in solving the structure of a protein based on the electron density map obtained from XRD. Standard crystallographic refinement procedures use stereochemical restraints to aid in the structure solution and ensure that the final structure is chemically reasonable. These restraints are often not accurate for metal-ligand bonds, which can bias the refinement results, especially for structures solved to medium resolution (~1.6-2.4 Å). Further, these stereochemical restraints do not accurately reflect potential changes in the electron density around the metal site. Such subtle changes in charge distribution can lead to structural changes that are mechanistically important; thus, refinement that more accurately includes these effects is essential, especially for highly covalent moieties such as Fe-S bonds and clusters. Quantum mechanical (QM) calculations can more accurately describe the electron density of covalent metal-ligand bonds and clusters, thus providing an attractive supplement to standard refinement procedures. In this proposal, I will be developing and calibrating a quantum refinement method for integration as a module into the crystallography structure analysis software platform PHENIX. The method will use density functional theory (DFT) calculations on the metal active site and immediately surrounding ligands to obtain the gradient used in the crystallographic refinement procedure, and will be benchmarked on model iron- sulfur proteins. I will then apply the method to time-resolved XFEL XRD data on two metalloproteins, isopeninicillin N synthase (IPNS) and the O2-tolerant membrane-bound [Ni-Fe] hydrogenase (MBH) collected during their O2 reactions. Quantum refinement of these structures will couple the subtle changes in electronic structure during catalysis to changes in structure, giving more accurate structures and elucidating the mechanism of isopenicillin N biosynthesis in IPNS and the mechanism of O2-tolerance in MBH. The development of this method will have important implications for understanding further metalloenzyme mechanisms in the future.