Nguyen Thach, Habeck Michael
Felix Bernstein Institute for Mathematical Statistics in the Biosciences, University of Göttingen.
Felix Bernstein Institute for Mathematical Statistics in the Biosciences, University of Göttingen Max Planck Institute for Biophysical Chemistry, Göttingen 37077, Germany.
Bioinformatics. 2016 Sep 1;32(17):i710-i717. doi: 10.1093/bioinformatics/btw442.
Large-scale conformational changes in proteins are implicated in many important biological functions. These structural transitions can often be rationalized in terms of relative movements of rigid domains. There is a need for objective and automated methods that identify rigid domains in sets of protein structures showing alternative conformational states.
We present a probabilistic model for detecting rigid-body movements in protein structures. Our model aims to approximate alternative conformational states by a few structural parts that are rigidly transformed under the action of a rotation and a translation. By using Bayesian inference and Markov chain Monte Carlo sampling, we estimate all parameters of the model, including a segmentation of the protein into rigid domains, the structures of the domains themselves, and the rigid transformations that generate the observed structures. We find that our Gibbs sampling algorithm can also estimate the optimal number of rigid domains with high efficiency and accuracy. We assess the power of our method on several thousand entries of the DynDom database and discuss applications to various complex biomolecular systems.
The Python source code for protein ensemble analysis is available at: https://github.com/thachnguyen/motion_detection
蛋白质中的大规模构象变化与许多重要的生物学功能相关。这些结构转变通常可以根据刚性结构域的相对运动来解释。需要客观且自动化的方法来识别处于不同构象状态的蛋白质结构集中的刚性结构域。
我们提出了一种用于检测蛋白质结构中刚体运动的概率模型。我们的模型旨在通过在旋转和平移作用下进行刚性变换的几个结构部分来近似不同的构象状态。通过使用贝叶斯推理和马尔可夫链蒙特卡罗采样，我们估计模型的所有参数，包括将蛋白质分割成刚性结构域、结构域本身的结构以及产生观测结构的刚性变换。我们发现我们的吉布斯采样算法还能高效且准确地估计刚性结构域的最佳数量。我们在DynDom数据库的数千个条目中评估了我们方法的能力，并讨论了其在各种复杂生物分子系统中的应用。
蛋白质集合分析的Python源代码可在以下网址获取：https://github.com/thachnguyen/motion_detection
