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

Voltage-gated sodium (Na V ) channels are vital regulators of electrical activity in excitable cells. Given their importance in physiology, Na V 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 Na V subtypes. Recent advances in cryo-electron microscopy have resolved most human Na V channels, providing valuable insights into their structure and function. However, limitations persist in fully capturing the complex conformational states that underlie Na V channel gating and modulation. This study explores the capability of AlphaFold2 to sample multiple Na V channel conformations and assess AlphaFold Multimer’s accuracy in modeling interactions between the Na V α-subunit and its protein partners, including auxiliary β-subunits and calmodulin. We enhance conformational sampling to explore Na V channel conformations using a subsampled multiple sequence alignment approach and varying the number of recycles. Our results demonstrate that AlphaFold2 models multiple Na V 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 Na V complexes with auxiliary β-subunits and calmodulin with high accuracy, and the presence of protein partners significantly alters both the modeled conformational landscape of the Na V α-subunit and the coupling between its functional states. These findings highlight the potential of deep learning-based methods to expand our understanding of Na V channel structure, gating, and modulation, while also underscoring the limitations of predicted models that remain hypotheses until validated by experimental data. Summary Lopez-Mateos et al.’s study demonstrates AlphaFold2’s potential to sample multiple states of human Na V channels. Additionally, Na V α-subunit interactions with β-subunits and calmodulin reshape Na V α-subunit conformational landscape. This study reveals potential of deep learning methods to model structural diversity of ion channels.