Revealing intermediate states of voltage-gated potassium channels with physics-informed deep learning: application to Kv7.1
Abstract
Introduction Voltage-gated potassium channels translate membrane-voltage changes into pore opening through coordinated conformational rearrangements, yet the intermediate states connecting these events remain difficult to capture. Revealing such hidden intermediates is essential for reconstructing protein motions as continuous, interpretable pathways. We use Kv7.1/KCNQ1 as a stringent demonstration case because its extensive experimental characterization enables testing whether endpoint data can recover the intervening transition. Methods We applied and tuned a general physics-informed deep-learning pipeline to molecular dynamics ensembles of tetrameric Kv7.1. A convolutional autoencoder was trained only on resting and activated endpoint conformations, while an independent intermediate ensemble was withheld. Structural reconstruction was combined with molecular-physics regularization and an electrostatic descriptor of charge displacement across the membrane field. Independent cryogenic electron microscopy structures were introduced after training as benchmarks. Generated conformations were assessed through voltage-sensor progression, contact rearrangements, pore opening, stereochemical quality, and stability in an explicit membrane. The workflow was also applied to KCNQ1-KCNE1 channel-complex ensembles containing either one or two phosphatidylinositol 4,5-bisphosphate molecules per KCNQ1 monomer. Results The model recovered a continuous structural progression and positioned the withheld intermediate ensemble between the endpoints. Experimental structures followed the expected resting-to-intermediate-to-activated sequence. Generated intermediates showed coordinated voltage-sensor displacement, reorganization of stabilizing interactions, and later widening of the activation gate, despite the absence of intermediate structures or pore-radius information during training. In the KCNQ1-KCNE1 complex, the approach distinguished the two lipid-stoichiometry conditions and revealed remodeling of networks linking the voltage sensor, linker, and pore gate. Discussion Physics-informed generative modeling can connect sparse endpoint data to pathway-resolved atomic hypotheses for protein conformational changes, particularly ion-channel gating. The inferred trajectories are not unique kinetic or free-energy pathways, but testable mechanistic models consistent with structural and biophysical information. More broadly, the framework offers a transferable strategy for investigating conformational transitions and modulation in proteins.
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BibTeX
@article{kacher2026revealing,
title = {{Revealing intermediate states of voltage-gated potassium channels with physics-informed deep learning: application to Kv7.1}},
author = {Julia Kacher and Mounir Tarek},
journal = {Frontiers in Biophysics},
year = {2026},
month = aug,
doi = {10.3389/frbis.2026.1877588},
url = {https://hal.science/hal-05789114v1},
note = {CC BY},
}
RIS
TY - JOUR
TI - Revealing intermediate states of voltage-gated potassium channels with physics-informed deep learning: application to Kv7.1
AU - Julia Kacher
AU - Mounir Tarek
PY - 2026
DA - 2026/08/13
JO - Frontiers in Biophysics
DO - 10.3389/frbis.2026.1877588
UR - https://hal.science/hal-05789114v1
AB - Introduction Voltage-gated potassium channels translate membrane-voltage changes into pore opening through coordinated conformational rearrangements, yet the intermediate states connecting these events remain difficult to capture. Revealing such hidden intermediates is essential for reconstructing protein motions as continuous, interpretable pathways. We use Kv7.1/KCNQ1 as a stringent demonstration case because its extensive experimental characterization enables testing whether endpoint data can recover the intervening transition. Methods We applied and tuned a general physics-informed deep-learning pipeline to molecular dynamics ensembles of tetrameric Kv7.1. A convolutional autoencoder was trained only on resting and activated endpoint conformations, while an independent intermediate ensemble was withheld. Structural reconstruction was combined with molecular-physics regularization and an electrostatic descriptor of charge displacement across the membrane field. Independent cryogenic electron microscopy structures were introduced after training as benchmarks. Generated conformations were assessed through voltage-sensor progression, contact rearrangements, pore opening, stereochemical quality, and stability in an explicit membrane. The workflow was also applied to KCNQ1-KCNE1 channel-complex ensembles containing either one or two phosphatidylinositol 4,5-bisphosphate molecules per KCNQ1 monomer. Results The model recovered a continuous structural progression and positioned the withheld intermediate ensemble between the endpoints. Experimental structures followed the expected resting-to-intermediate-to-activated sequence. Generated intermediates showed coordinated voltage-sensor displacement, reorganization of stabilizing interactions, and later widening of the activation gate, despite the absence of intermediate structures or pore-radius information during training. In the KCNQ1-KCNE1 complex, the approach distinguished the two lipid-stoichiometry conditions and revealed remodeling of networks linking the voltage sensor, linker, and pore gate. Discussion Physics-informed generative modeling can connect sparse endpoint data to pathway-resolved atomic hypotheses for protein conformational changes, particularly ion-channel gating. The inferred trajectories are not unique kinetic or free-energy pathways, but testable mechanistic models consistent with structural and biophysical information. More broadly, the framework offers a transferable strategy for investigating conformational transitions and modulation in proteins.
SN - 2813-7183
SN - 2813-7183
ER -