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  A probabilistic network model for structural transitions in biomolecules.

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.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0001-54AF-C Version Permalink: http://hdl.handle.net/21.11116/0000-0003-6471-D
Genre: Journal Article

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 Creators:
Habeck, M.1, Author              
Nguyen, T., Author
Affiliations:
1Research Group of Statistical Inverse-Problems in Biophysics, MPI for Biophysical Chemistry, Max Planck Society, ou_1113580              

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Free keywords: Bayesian statistics; conformational change; crosslinking; mass spectrometry; Markov chain Monte Carlo; network model; protein structure; structural modeling
 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.

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Language(s): eng - English
 Dates: 2018-03-092018-06
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1002/prot.25490
 Degree: -

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Title: Proteins: Structure, Function, and Bioinformatics
Source Genre: Journal
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Pages: - Volume / Issue: 86 (6) Sequence Number: - Start / End Page: 634 - 643 Identifier: -