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  Probing the energy landscape of activation gating of the bacterial potassium channel KcsA.

Lindner, T., de Groot, B. L., & Stary-Weinzinger, A. (2013). Probing the energy landscape of activation gating of the bacterial potassium channel KcsA. PLoS Computational Biology, 9(5): e1003058. doi:10.1371/journal.pcbi.1003058.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-FCF5-F Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0027-CA55-9
Genre: Journal Article

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Lindner, T., Author
de Groot, B. L.1, Author              
Stary-Weinzinger, A., Author
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1Research Group of Computational Biomolecular Dynamics, MPI for biophysical chemistry, Max Planck Society, ou_578573              

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 Abstract: The bacterial potassium channel KcsA, which has been crystallized in several conformations, offers an ideal model to investigate activation gating of ion channels. In this study, essential dynamics simulations are applied to obtain insights into the transition pathways and the energy profile of KcsA pore gating. In agreement with previous hypotheses, our simulations reveal a two phasic activation gating process. In the first phase, local structural rearrangements in TM2 are observed leading to an intermediate channel conformation, followed by large structural rearrangements leading to full opening of KcsA. Conformational changes of a highly conserved phenylalanine, F114, at the bundle crossing region are crucial for the transition from a closed to an intermediate state. 3.9 mu s umbrella sampling calculations reveal that there are two well-defined energy barriers dividing closed, intermediate, and open channel states. In agreement with mutational studies, the closed state was found to be energetically more favorable compared to the open state. Further, the simulations provide new insights into the dynamical coupling effects of F103 between the activation gate and the selectivity filter. Investigations on individual subunits support cooperativity of subunits during activation gating.

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Language(s): eng - English
 Dates: 2013-05-02
 Publication Status: Published online
 Pages: 9
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 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1371/journal.pcbi.1003058
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Title: PLoS Computational Biology
Source Genre: Journal
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Pages: - Volume / Issue: 9 (5) Sequence Number: e1003058 Start / End Page: - Identifier: -