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Inferring functional units in ion channel pores via relative entropy

Schmidt, Michael ; Schroeder, Indra ; Bauer, Daniel ; Thiel, Gerhard ; Hamacher, Kay (2024)
Inferring functional units in ion channel pores via relative entropy.
In: European Biophysics Journal, 2021, 50 (1)
doi: 10.26083/tuprints-00023470
Article, Secondary publication, Publisher's Version

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Item Type: Article
Type of entry: Secondary publication
Title: Inferring functional units in ion channel pores via relative entropy
Language: English
Date: 3 September 2024
Place of Publication: Darmstadt
Year of primary publication: 2021
Place of primary publication: Berlin ; Heidelberg ; New York
Publisher: Springer International Publishing
Journal or Publication Title: European Biophysics Journal
Volume of the journal: 50
Issue Number: 1
DOI: 10.26083/tuprints-00023470
Corresponding Links:
Origin: Secondary publication DeepGreen
Abstract:

Coarse-grained protein models approximate the first-principle physical potentials. Among those modeling approaches, the relative entropy framework yields promising and physically sound results, in which a mapping from the target protein structure and dynamics to a model is defined and subsequently adjusted by an entropy minimization of the model parameters. Minimization of the relative entropy is equivalent to maximization of the likelihood of reproduction of (configurational ensemble) observations by the model. In this study, we extend the relative entropy minimization procedure beyond parameter fitting by a second optimization level, which identifies the optimal mapping to a (dimension-reduced) topology. We consider anisotropic network models of a diverse set of ion channels and assess our findings by comparison to experimental results.

Uncontrolled Keywords: Potassium channel (pores), Mechanical coupling relative entropy, Anisotropic network model
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-234702
Classification DDC: 500 Science and mathematics > 530 Physics
500 Science and mathematics > 570 Life sciences, biology
Divisions: 10 Department of Biology > Biology of Algae and Protozoa
10 Department of Biology > Computational Biology and Simulation
Date Deposited: 03 Sep 2024 13:51
Last Modified: 02 Oct 2024 14:01
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/23470
PPN: 521851521
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