Learning generative models of molecular dynamics

BMC Genomics

Proceedings

Christopher J Langmead¹  Hetunandan Kamisetty²  Narges Sharif Razavian³ 

[1] Computer Science Department, Carnegie Mellon University, 15213, Pittsburgh, PA, USA;Lane Center for Computational Biology, Carnegie Mellon University, 15213, Pittsburgh, PA, USA;Department of Biochemistry, University of Washington, 98195, Seattle, WA, USA;Language Technologies Institute, Carnegie Mellon University, 15213, Pittsburgh, PA, USA;

关键词: Markov Random Fields;    Correlation Network;    Differential Entropy;    Precision Matrix;    Gaussian Graphical Model;   

DOI  :  10.1186/1471-2164-13-S1-S5

来源: Springer

【 摘 要 】

We introduce three algorithms for learning generative models of molecular structures from molecular dynamics simulations. The first algorithm learns a Bayesian-optimal undirected probabilistic model over user-specified covariates (e.g., fluctuations, distances, angles, etc). L1 reg-ularization is used to ensure sparse models and thus reduce the risk of over-fitting the data. The topology of the resulting model reveals important couplings between different parts of the protein, thus aiding in the analysis of molecular motions. The generative nature of the model makes it well-suited to making predictions about the global effects of local structural changes (e.g., the binding of an allosteric regulator). Additionally, the model can be used to sample new conformations. The second algorithm learns a time-varying graphical model where the topology and parameters change smoothly along the trajectory, revealing the conformational sub-states. The last algorithm learns a Markov Chain over undirected graphical models which can be used to study and simulate kinetics. We demonstrate our algorithms on multiple molecular dynamics trajectories.

【 授权许可】

Unknown   
© Razavian et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.