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Journal Article

A probabilistic network model for structural transitions in biomolecules.

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Habeck,  M.
Research Group of Statistical Inverse-Problems in Biophysics, MPI for Biophysical Chemistry, Max Planck Society;

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2591141_Suppl_5.pdf
(Supplementary material), 193KB

Citation

Habeck, M., & Nguyen, T. (2018). A probabilistic network model for structural transitions in biomolecules. Proteins: Structure, Function, and Bioinformatics, 86(6), 634-643. doi:10.1002/prot.25490.


Cite as: http://hdl.handle.net/21.11116/0000-0001-54AF-C
Abstract
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.