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To what extent naringenin binding and membrane depolarization shape mitoBK channel gating—A machine learning approach
The large conductance voltage- and Ca(2+)-activated K(+) channels from the inner mitochondrial membrane (mitoBK) are modulated by a number of factors. Among them flavanones, including naringenin (Nar), arise as a promising group of mitoBK channel regulators from a pharmacological point of view. It i...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9342765/ https://www.ncbi.nlm.nih.gov/pubmed/35857767 http://dx.doi.org/10.1371/journal.pcbi.1010315 |
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author | Richter-Laskowska, Monika Trybek, Paulina Bednarczyk, Piotr Wawrzkiewicz-Jałowiecka, Agata |
author_facet | Richter-Laskowska, Monika Trybek, Paulina Bednarczyk, Piotr Wawrzkiewicz-Jałowiecka, Agata |
author_sort | Richter-Laskowska, Monika |
collection | PubMed |
description | The large conductance voltage- and Ca(2+)-activated K(+) channels from the inner mitochondrial membrane (mitoBK) are modulated by a number of factors. Among them flavanones, including naringenin (Nar), arise as a promising group of mitoBK channel regulators from a pharmacological point of view. It is well known that in the presence of Nar the open state probability (p(op)) of mitoBK channels significantly increases. Nevertheless, the molecular mechanism of the mitoBK-Nar interactions remains still unrevealed. It is also not known whether the effects of naringenin administration on conformational dynamics can resemble those which are exerted by the other channel-activating stimuli. In aim to answer this question, we examine whether the dwell-time series of mitoBK channels which were obtained at different voltages and Nar concentrations (yet allowing to reach comparable p(op)s) are discernible by means of artificial intelligence methods, including k-NN and shapelet learning. The obtained results suggest that the structural complexity of the gating dynamics is shaped both by the interaction of channel gate with the voltage sensor (VSD) and the Nar-binding site. For a majority of data one can observe stimulus-specific patterns of channel gating. Shapelet algorithm allows to obtain better prediction accuracy in most cases. Probably, because it takes into account the complexity of local features of a given signal. About 30% of the analyzed time series do not sufficiently differ to unambiguously distinguish them from each other, which can be interpreted in terms of the existence of the common features of mitoBK channel gating regardless of the type of activating stimulus. There exist long-range mutual interactions between VSD and the Nar-coordination site that are responsible for higher levels of Nar-activation (Δp(op)) at deeply depolarized membranes. These intra-sensor interactions are anticipated to have an allosteric nature. |
format | Online Article Text |
id | pubmed-9342765 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-93427652022-08-02 To what extent naringenin binding and membrane depolarization shape mitoBK channel gating—A machine learning approach Richter-Laskowska, Monika Trybek, Paulina Bednarczyk, Piotr Wawrzkiewicz-Jałowiecka, Agata PLoS Comput Biol Research Article The large conductance voltage- and Ca(2+)-activated K(+) channels from the inner mitochondrial membrane (mitoBK) are modulated by a number of factors. Among them flavanones, including naringenin (Nar), arise as a promising group of mitoBK channel regulators from a pharmacological point of view. It is well known that in the presence of Nar the open state probability (p(op)) of mitoBK channels significantly increases. Nevertheless, the molecular mechanism of the mitoBK-Nar interactions remains still unrevealed. It is also not known whether the effects of naringenin administration on conformational dynamics can resemble those which are exerted by the other channel-activating stimuli. In aim to answer this question, we examine whether the dwell-time series of mitoBK channels which were obtained at different voltages and Nar concentrations (yet allowing to reach comparable p(op)s) are discernible by means of artificial intelligence methods, including k-NN and shapelet learning. The obtained results suggest that the structural complexity of the gating dynamics is shaped both by the interaction of channel gate with the voltage sensor (VSD) and the Nar-binding site. For a majority of data one can observe stimulus-specific patterns of channel gating. Shapelet algorithm allows to obtain better prediction accuracy in most cases. Probably, because it takes into account the complexity of local features of a given signal. About 30% of the analyzed time series do not sufficiently differ to unambiguously distinguish them from each other, which can be interpreted in terms of the existence of the common features of mitoBK channel gating regardless of the type of activating stimulus. There exist long-range mutual interactions between VSD and the Nar-coordination site that are responsible for higher levels of Nar-activation (Δp(op)) at deeply depolarized membranes. These intra-sensor interactions are anticipated to have an allosteric nature. Public Library of Science 2022-07-20 /pmc/articles/PMC9342765/ /pubmed/35857767 http://dx.doi.org/10.1371/journal.pcbi.1010315 Text en © 2022 Richter-Laskowska et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Richter-Laskowska, Monika Trybek, Paulina Bednarczyk, Piotr Wawrzkiewicz-Jałowiecka, Agata To what extent naringenin binding and membrane depolarization shape mitoBK channel gating—A machine learning approach |
title | To what extent naringenin binding and membrane depolarization shape mitoBK channel gating—A machine learning approach |
title_full | To what extent naringenin binding and membrane depolarization shape mitoBK channel gating—A machine learning approach |
title_fullStr | To what extent naringenin binding and membrane depolarization shape mitoBK channel gating—A machine learning approach |
title_full_unstemmed | To what extent naringenin binding and membrane depolarization shape mitoBK channel gating—A machine learning approach |
title_short | To what extent naringenin binding and membrane depolarization shape mitoBK channel gating—A machine learning approach |
title_sort | to what extent naringenin binding and membrane depolarization shape mitobk channel gating—a machine learning approach |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9342765/ https://www.ncbi.nlm.nih.gov/pubmed/35857767 http://dx.doi.org/10.1371/journal.pcbi.1010315 |
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