Department of Biochemistry, Stanford University, Stanford, CA, United States; Department of Chemistry, Stanford University, Stanford, CA, United States.
Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, United States.
Methods Enzymol. 2023;688:223-254. doi: 10.1016/bs.mie.2023.06.009. Epub 2023 Jul 27.
Conformational ensembles underlie all protein functions. Thus, acquiring atomic-level ensemble models that accurately represent conformational heterogeneity is vital to deepen our understanding of how proteins work. Modeling ensemble information from X-ray diffraction data has been challenging, as traditional cryo-crystallography restricts conformational variability while minimizing radiation damage. Recent advances have enabled the collection of high quality diffraction data at ambient temperatures, revealing innate conformational heterogeneity and temperature-driven changes. Here, we used diffraction datasets for Proteinase K collected at temperatures ranging from 313 to 363 K to provide a tutorial for the refinement of multiconformer ensemble models. Integrating automated sampling and refinement tools with manual adjustments, we obtained multiconformer models that describe alternative backbone and sidechain conformations, their relative occupancies, and interconnections between conformers. Our models revealed extensive and diverse conformational changes across temperature, including increased bound peptide ligand occupancies, different Ca binding site configurations and altered rotameric distributions. These insights emphasize the value and need for multiconformer model refinement to extract ensemble information from diffraction data and to understand ensemble-function relationships.
构象集合是所有蛋白质功能的基础。因此，获得能够准确表示构象异质性的原子水平集合模型对于加深我们对蛋白质如何工作的理解至关重要。从 X 射线衍射数据中建模集合信息一直具有挑战性，因为传统的低温结晶学限制了构象可变性，同时最大限度地减少了辐射损伤。最近的进展使得能够在环境温度下收集高质量的衍射数据，揭示了固有构象异质性和温度驱动的变化。在这里，我们使用了从 313 到 363 K 温度下收集的蛋白酶 K 的衍射数据集，为多构象集合模型的精修提供了一个教程。我们通过自动采样和精修工具与手动调整相结合，获得了描述替代骨架和侧链构象、它们的相对占有率以及构象之间连接的多构象模型。我们的模型揭示了广泛多样的构象变化，包括结合肽配体占有率的增加、不同的 Ca 结合位点构型和改变的构象分布。这些见解强调了从衍射数据中提取集合信息和理解集合-功能关系的多构象模型精修的价值和必要性。
