NMR relaxation dispersion measurements report on conformational changes occurring on the μs-ms timescale. Chemical shift information derived from relaxation dispersion can be used to generate structural models of weakly populated alternative conformational states. Current methods to obtain such models rely on determining the signs of chemical shift changes between the conformational states, which are difficult to obtain in many situations. Here we use a "sample and select" method to generate relevant structural models of alternative conformations of the C-terminal associated region of E. coli DHFR, using only unsigned chemical shift changes for backbone amides and carbonyls (1H, 15N, and 13C'). We find that CS-Rosetta sampling with unsigned chemical shift changes generates a diversity of structures that are sufficient to characterize a minor conformational state of the C-terminal region of DHFR. The excited state differs from the ground state by a change in secondary structure, consistent with previous predictions from chemical shift hypersurfaces, and validated by the X-ray structure of a partially humanized mutant of E. coli DHFR (N23PP/G51PEKN). The results demonstrate that the combination of fragment modeling with sparse chemical shift data can determine the structure of an alternative conformation of DHFR sampled on the μs-ms timescale. Such methods will be useful for characterizing alternative states, which can potentially be used for in silico drug screening, as well as contributing to understanding the role of minor states in biology and molecular evolution.

中文翻译：

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使用来自 NMR 弛豫色散的稀疏化学位移数据对隐藏结构进行建模

NMR 弛豫色散测量报告了在 μs-ms 时间尺度上发生的构象变化。来自弛豫色散的化学位移信息可用于生成弱填充的替代构象状态的结构模型。目前获得此类模型的方法依赖于确定构象状态之间的化学位移变化的迹象，这在许多情况下很难获得。在这里，我们使用“样本和选择”方法生成大肠杆菌DHFR C 末端相关区域的替代构象的相关结构模型，仅使用骨架酰胺和羰基（1H、15N 和 13C'）的无符号化学位移变化）。我们发现具有无符号化学位移变化的 CS-Rosetta 采样产生了足以表征 DHFR C 末端区域的次要构象状态的结构多样性。激发态与基态的不同之处在于二级结构的变化，这与先前对化学位移超表面的预测一致，并通过大肠杆菌DHFR (N23PP/G51PEKN) 的部分人源化突变体的 X 射线结构进行了验证。结果表明，片段建模与稀疏化学位移数据的结合可以确定在 μs-ms 时间尺度上采样的 DHFR 的替代构象的结构。这些方法将有助于表征替代状态，这可能用于计算机药物筛选，