To what extent naringenin binding and membrane depolarization shape mitoBK channel gating-A machine learning approach.
The large conductance voltage- and Ca2+-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 we...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2022-07-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1010315 |
| _version_ | 1811214617156780032 |
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| author | Monika Richter-Laskowska Paulina Trybek Piotr Bednarczyk Agata Wawrzkiewicz-Jałowiecka |
| author_facet | Monika Richter-Laskowska Paulina Trybek Piotr Bednarczyk Agata Wawrzkiewicz-Jałowiecka |
| author_sort | Monika Richter-Laskowska |
| collection | DOAJ |
| description | The large conductance voltage- and Ca2+-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 (pop) 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 pops) 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 (Δpop) at deeply depolarized membranes. These intra-sensor interactions are anticipated to have an allosteric nature. |
| first_indexed | 2024-04-12T06:07:39Z |
| format | Article |
| id | doaj.art-cdea1e4f509d4160adc5982526c5854c |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-12T06:07:39Z |
| publishDate | 2022-07-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-cdea1e4f509d4160adc5982526c5854c2022-12-22T03:44:50ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582022-07-01187e101031510.1371/journal.pcbi.1010315To what extent naringenin binding and membrane depolarization shape mitoBK channel gating-A machine learning approach.Monika Richter-LaskowskaPaulina TrybekPiotr BednarczykAgata Wawrzkiewicz-JałowieckaThe large conductance voltage- and Ca2+-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 (pop) 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 pops) 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 (Δpop) at deeply depolarized membranes. These intra-sensor interactions are anticipated to have an allosteric nature.https://doi.org/10.1371/journal.pcbi.1010315 |
| spellingShingle | Monika Richter-Laskowska Paulina Trybek Piotr Bednarczyk Agata Wawrzkiewicz-Jałowiecka To what extent naringenin binding and membrane depolarization shape mitoBK channel gating-A machine learning approach. PLoS Computational Biology |
| 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 |
| url | https://doi.org/10.1371/journal.pcbi.1010315 |
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