Exploring voltage-gated sodium channel conformations and protein-protein interactions using AlphaFold2

Abstract

Voltage-gated sodium (NaV) channels are vital regulators of electrical activity in excitable cells. Given their importance in physiology, NaV channels are key therapeutic targets for treating numerous conditions, yet developing subtype-selective drugs remains challenging due to the high sequence and structural conservation among NaV subtypes. Recent advances in cryo-electron microscopy have resolved most human NaV channels, providing valuable insights into their structure and function. However, limitations persist in fully capturing the complex conformational states that underlie NaV channel gating and modulation. This study explores the capability of AlphaFold2 to sample multiple NaV channel conformations and assess AlphaFold Multimers accuracy in modeling interactions between the NaV -subunit and its protein partners, including auxiliary {beta}-subunits and calmodulin. We enhance conformational sampling to explore NaV channel conformations using a subsampled multiple sequence alignment approach and varying the number of recycles. Our results demonstrate that AlphaFold2 models multiple NaV channel conformations, including those observed in experimental structures, states that have not been described experimentally, and potential intermediate states. Correlation and clustering analyses uncover coordinated domain behavior and recurrent state ensembles. Furthermore, AlphaFold Multimer models NaV complexes with auxiliary {beta}-subunits and calmodulin with high accuracy, and the presence of protein partners significantly alters both the modeled conformational landscape of the NaV -subunit and the coupling between its functional states. These findings highlight the potential of deep learning-based methods to expand our understanding of NaV channel structure, gating, and modulation, while also underscoring the limitations of predicted models that remain hypotheses until validated by experimental data. SummaryLopez-Mateos et al.s study demonstrates AlphaFold2s potential to sample multiple states of human NaV channels. Additionally, NaV -subunit interactions with {beta}-subunits and calmodulin reshape NaV -subunit conformational landscape. This study reveals potential of deep learning methods to model structural diversity of ion channels.