title: A probabilistic model for detecting rigid domains in protein structures.
abstract: 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
  
  
  
  : mhabeck@gwdg.de.
authors: Felix Bernstein Institute for Mathematical Statistics in the Biosciences, University of Göttingen.
authors: Thach
authors: T
authors: Nguyen
authors: Felix Bernstein Institute for Mathematical Statistics in the Biosciences, University of Göttingen Max Planck Institute for Biophysical Chemistry, Göttingen 37077, Germany.
authors: Michael
authors: M
authors: Habeck
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D015233:Models, Statistical; D009010:Monte Carlo Method; D011487:Protein Conformation; D011506:Proteins
chemical_list: D011506:Proteins