A probabilistic model for detecting rigid domains in protein structures

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. Availability and Implementation: The Python source code for protein ensemble analysis is available at: https://github.com/thachnguyen/motion_detection Contact: mhabeck@gwdg.de
Source: Bioinformatics - Category: Bioinformatics Authors: Tags: PROTEINS Source Type: research