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Markov state models of proton- and pore-dependent activation in a pentameric ligand-gated ion channel
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).ORCID iD: 0000-0003-3224-4547
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).ORCID iD: 0000-0003-2049-3378
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).ORCID iD: 0000-0002-2734-2794
Number of Authors: 42021 (English)In: eLIFE, E-ISSN 2050-084X, Vol. 10, article id e68369Article in journal (Refereed) Published
Abstract [en]

Ligand-gated ion channels conduct currents in response to chemical stimuli, mediating electrochemical signaling in neurons and other excitable cells. For many channels, the details of gating remain unclear, partly due to limited structural data and simulation timescales. Here, we used enhanced sampling to simulate the pH-gated channel GLIC, and construct Markov state models (MSMs) of gating. Consistent with new functional recordings, we report in oocytes, our analysis revealed differential effects of protonation and mutation on free-energy wells. Clustering of closed- versus open-like states enabled estimation of open probabilities and transition rates, while higher-order clustering affirmed conformational trends in gating. Furthermore, our models uncovered state- and protonation-dependent symmetrization. This demonstrates the applicability of MSMs to map energetic and conformational transitions between ion-channel functional states, and how they reproduce shifts upon activation or mutation, with implications for modeling neuronal function and developing state-selective drugs.

Place, publisher, year, edition, pages
2021. Vol. 10, article id e68369
Keywords [en]
ligand-gated ion channel, gating, allosteric modulation, molecular dynamics, electrophysiology, markov state model, Xenopus
National Category
Biological Sciences
Identifiers
URN: urn:nbn:se:su:diva-200936DOI: 10.7554/eLife.68369ISI: 000726402600001PubMedID: 34652272Scopus ID: 2-s2.0-85118207788OAI: oai:DiVA.org:su-200936DiVA, id: diva2:1631774
Available from: 2022-01-25 Created: 2022-01-25 Last updated: 2022-05-30Bibliographically approved

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Heusser, Stephanie A.Howard, Rebecca J.Lindahl, Erik

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