Department of Protein Evolution, Max Planck Institute for Developmental Biology, Max-Planck-Ring 5, 72076 Tübingen, Germany.
Department of Informatics, Technical University of Munich, Boltzmannstrasse 3, 85748 Garching, Germany.
Structure. 2019 May 7;27(5):853-865.e5. doi: 10.1016/j.str.2019.03.005. Epub 2019 Mar 28.
The ability of proteins to adopt multiple conformational states is essential to their function, and elucidating the details of such diversity under physiological conditions has been a major challenge. Here we present a generalized method for mapping protein population landscapes by NMR spectroscopy. Experimental NOESY spectra are directly compared with a set of expectation spectra back-calculated across an arbitrary conformational space. Signal decomposition of the experimental spectrum then directly yields the relative populations of local conformational microstates. In this way, averaged descriptions of conformation can be eliminated. As the method quantitatively compares experimental and expectation spectra, it inherently delivers an R factor expressing how well structural models explain the input data. We demonstrate that our method extracts sufficient information from a single 3D NOESY experiment to perform initial model building, refinement, and validation, thus offering a complete de novo structure determination protocol.
蛋白质能够采用多种构象状态的能力对其功能至关重要，阐明生理条件下这种多样性的细节一直是一个主要挑战。在这里，我们提出了一种通过 NMR 光谱法绘制蛋白质群体景观图的通用方法。通过直接比较实验 NOESY 光谱和一组通过任意构象空间回溯计算的期望光谱，可以实现这一点。然后，通过对实验光谱进行信号分解，可以直接得到局部构象微态的相对丰度。通过这种方式，可以消除对构象的平均描述。由于该方法定量比较了实验和预期光谱，因此它内在地提供了一个 R 因子，该因子表示结构模型对输入数据的解释程度。我们证明，我们的方法可以从单个 3D NOESY 实验中提取足够的信息来进行初始模型构建、精修和验证，从而提供了一个完整的从头确定结构的协议。
