Department of Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany.
Elife. 2019 Mar 4;8:e43542. doi: 10.7554/eLife.43542.
We present a correlation-driven molecular dynamics (CDMD) method for automated refinement of atomistic models into cryo-electron microscopy (cryo-EM) maps at resolutions ranging from near-atomic to subnanometer. It utilizes a chemically accurate force field and thermodynamic sampling to improve the real-space correlation between the modeled structure and the cryo-EM map. Our framework employs a gradual increase in resolution and map-model agreement as well as simulated annealing, and allows fully automated refinement without manual intervention or any additional rotamer- and backbone-specific restraints. Using multiple challenging systems covering a wide range of map resolutions, system sizes, starting model geometries and distances from the target state, we assess the quality of generated models in terms of both model accuracy and potential of overfitting. To provide an objective comparison, we apply several well-established methods across all examples and demonstrate that CDMD performs best in most cases.
我们提出了一种关联驱动的分子动力学（CDMD）方法，用于将原子模型自动精修到从近原子分辨率到亚纳米分辨率的冷冻电子显微镜（cryo-EM）映射中。它利用化学上准确的力场和热力学采样来提高模型结构与 cryo-EM 映射之间的实空间相关性。我们的框架采用逐渐提高分辨率和映射-模型一致性以及模拟退火的方法，并允许完全自动化的精修，而无需人工干预或任何其他的构象和主链特异性限制。使用多个具有挑战性的系统，涵盖广泛的映射分辨率、系统大小、起始模型几何形状和与目标状态的距离，我们根据模型的准确性和过度拟合的潜力来评估生成模型的质量。为了提供客观的比较，我们在所有示例中应用了几种成熟的方法，并证明 CDMD 在大多数情况下表现最佳。
