Adiabatic-Bias Molecular Dynamics Simulations Reveal the Impact of Mutations on Muscarinic Antagonist Unbinding Kinetics

Ligand-target dissociation rates (<i>k</i><sub>off</sub>) strongly correlate with efficacy and safety profiles, as well as with the therapeutic effect of drugs. As a prototypical example, muscarinic receptor antagonists used as bronchodilators show similar affinity profiles toward the muscarinic M3 receptors (M3R) and M2 receptors (M2R), whereas their kinetic selectivity toward M3R avoids the adverse effects that a prolonged inhibition of M2R would induce at the cardiac level. Previous studies on the dissociation kinetics of human M3R showed that the residence time and binding affinity of muscarinic antagonists are deeply affected by the presence of specific mutations. The aim of our work was to reproduce the rankings of these experimental kinetic rates through an approach based on the application of adiabatic-bias molecular dynamics (ABMD) simulations using Path Collective Variables (PCVs), PCV-ABMD. Employing this methodology, we simulated the translocation of tiotropium, a long-acting bronchodilator targeting M3R, from the orthosteric site to the extracellular vestibule, without considering the whole unbinding process. The estimated times necessary for translocation displayed a strong correlation with the experimental p<i>k</i><sub>off</sub> values. Moreover, a thorough analysis of protein-ligand contacts provided deeper insights into the mechanism of unbinding of muscarinic antagonists. The newly described PCV-ABMD protocol captured relevant metastable states and offered a reliable approach for the prediction of kinetic selectivity in sets of mutants.

Authors

Institutions

Publication Details

Journal
Journal of Chemical Information and Modeling
Published
2025-06-16
DOI
https://doi.org/10.1021/acs.jcim.5c00601
Citations
3
Primary Topic
Protein Structure and Dynamics
Type
article
Field-Weighted Citation Impact
2.42

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Adiabatic-Bias Molecular Dynamics Simulations Reveal the Impact of Mutations on Muscarinic Antagonist Unbinding Kinetics

Adriana Coricello, Gian Marco Elisi, Giovanni Bottegoni, Maria Musgaard et al.
3 citations
Journal of Chemical Information and Modeling
Protein Structure and Dynamics
2.42
article

Adiabatic-Bias Molecular Dynamics Simulations Reveal the Impact of Mutations on Muscarinic Antagonist Unbinding Kinetics

Adriana Coricello, Gian Marco Elisi, Giovanni Bottegoni, Maria Musgaard, Benjamin G. Tehan, Anna Lisa Chiaravalle
article en
3 citations

Abstract

Ligand-target dissociation rates (<i>k</i><sub>off</sub>) strongly correlate with efficacy and safety profiles, as well as with the therapeutic effect of drugs. As a prototypical example, muscarinic receptor antagonists used as bronchodilators show similar affinity profiles toward the muscarinic M3 receptors (M3R) and M2 receptors (M2R), whereas their kinetic selectivity toward M3R avoids the adverse effects that a prolonged inhibition of M2R would induce at the cardiac level. Previous studies on the dissociation kinetics of human M3R showed that the residence time and binding affinity of muscarinic antagonists are deeply affected by the presence of specific mutations. The aim of our work was to reproduce the rankings of these experimental kinetic rates through an approach based on the application of adiabatic-bias molecular dynamics (ABMD) simulations using Path Collective Variables (PCVs), PCV-ABMD. Employing this methodology, we simulated the translocation of tiotropium, a long-acting bronchodilator targeting M3R, from the orthosteric site to the extracellular vestibule, without considering the whole unbinding process. The estimated times necessary for translocation displayed a strong correlation with the experimental p<i>k</i><sub>off</sub> values. Moreover, a thorough analysis of protein-ligand contacts provided deeper insights into the mechanism of unbinding of muscarinic antagonists. The newly described PCV-ABMD protocol captured relevant metastable states and offered a reliable approach for the prediction of kinetic selectivity in sets of mutants.

Journal of Chemical Information and ModelingVol. 65(13)
University of Urbino (IT), University of Oxford (GB), University of Birmingham (GB)
Ministero dell'Università e della Ricerca
Openalex Percentile: Top 11%
Protein Structure and Dynamics
2.42
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.