Kinetic and thermodynamic insights into sodium ion translocation through the μ-opioid receptor from molecular dynamics and machine learning analysis
Xiaohu Hu,
Yibo Wang,
Amanda Hunkele,
Davide Provasi,
Gavril W Pasternak and
Marta Filizola
PLOS Computational Biology, 2019, vol. 15, issue 1, 1-19
Abstract:
The differential modulation of agonist and antagonist binding to opioid receptors (ORs) by sodium (Na+) has been known for decades. To shed light on the molecular determinants, thermodynamics, and kinetics of Na+ translocation through the μ-OR (MOR), we used a multi-ensemble Markov model framework combining equilibrium and non-equilibrium atomistic molecular dynamics simulations of Na+ binding to MOR active or inactive crystal structures embedded in an explicit lipid bilayer. We identify an energetically favorable, continuous ion pathway through the MOR active conformation only, and provide, for the first time: i) estimates of the energy differences and required timescales of Na+ translocation in inactive and active MORs, ii) estimates of Na+-induced changes to agonist binding validated by radioligand measurements, and iii) testable hypotheses of molecular determinants and correlated motions involved in this translocation, which are likely to play a key role in MOR signaling.Author summary: Notwithstanding years of research supporting the notion that μ-opioid receptor (MOR) function can be modulated by sodium ions (Na+), a complete understanding of Na+ translocation through the receptor and its effect on ligand binding at MOR requires additional information. Here, we use computer simulations to elucidate the energetics involved in sodium binding at inactive and active MOR, the timescales of sodium translocation through these receptor conformations, and the molecular determinants involved in this process.
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006689 (text/html)
https://journals.plos.org/ploscompbiol/article/fil ... 06689&type=printable (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1006689
DOI: 10.1371/journal.pcbi.1006689
Access Statistics for this article
More articles in PLOS Computational Biology from Public Library of Science
Bibliographic data for series maintained by ploscompbiol ().