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  A probabilistic model for detecting rigid domains in protein structures.

Nguyen, T., & Habeck, M. (2016). A probabilistic model for detecting rigid domains in protein structures. Bioinformatics, 32(17), i710-i717.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002B-AAED-4 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-002B-AAF0-9
Genre: Conference Paper

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2354395.pdf (Publisher version), 921KB
 
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 Creators:
Nguyen, T., Author
Habeck, M.1, Author              
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1Research Group of Statistical Inverse-Problems in Biophysics, MPI for Biophysical Chemistry, Max Planck Society, ou_1113580              

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 Abstract: Motivation: 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. Results: 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.

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Language(s): eng - English
 Dates: 2016-09-17
 Publication Status: Published in print
 Pages: -
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 Rev. Method: Peer
 Identifiers: DOI: 10.1093/bioinformatics/btw442
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Title: 15th European Conference on Computational Biology (ECCB 2016)
Place of Event: The Hague/Netherlands
Start-/End Date: 2016-09-03 - 2016-09-07

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Title: Bioinformatics
Source Genre: Journal
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Pages: - Volume / Issue: 32 (17) Sequence Number: - Start / End Page: i710 - i717 Identifier: -