Statistical Inverse Problems in Biophysics, Max Planck Institute for Biophysical Chemistry, Am Fassberg 11, 37077, Göttingen, Germany.
Felix Bernstein Institute for Mathematical Statistics in the Biosciences, Georg August University Göttingen, Goldschmidtstrasse 7, 37077, Göttingen, Germany.
Proteins. 2018 Jun;86(6):634-643. doi: 10.1002/prot.25490. Epub 2018 Mar 24.
Biological macromolecules often undergo large conformational rearrangements during a functional cycle. To simulate these structural transitions with full atomic detail typically demands extensive computational resources. Moreover, it is unclear how to incorporate, in a principled way, additional experimental information that could guide the structural transition. This article develops a probabilistic model for conformational transitions in biomolecules. The model can be viewed as a network of anharmonic springs that break, if the experimental data support the rupture of bonds. Hamiltonian Monte Carlo in internal coordinates is used to infer structural transitions from experimental data, thereby sampling large conformational transitions without distorting the structure. The model is benchmarked on a large set of conformational transitions. Moreover, we demonstrate the use of the probabilistic network model for integrative modeling of macromolecular complexes based on data from crosslinking followed by mass spectrometry.
生物大分子在功能循环中经常经历大的构象重排。为了用全原子细节模拟这些结构转变，通常需要大量的计算资源。此外，目前尚不清楚如何以一种有原则的方式纳入可能指导结构转变的额外实验信息。本文为生物分子中的构象转变开发了一个概率模型。该模型可以看作是一个非谐弹簧网络，如果实验数据支持键的断裂，则这些弹簧会断裂。内部坐标的哈密顿蒙特卡罗用于从实验数据推断结构转变，从而在不扭曲结构的情况下对大的构象转变进行采样。该模型在大量构象转变上进行了基准测试。此外，我们展示了基于交联后质谱数据的大分子复合物的综合建模中概率网络模型的使用。
