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

Voltage-gated sodium (NaV) channels are vital regulators of electrical
activity in excitable cells, playing critical roles in generating and
propagating action potentials. 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 family members. Recent
advances in cryo-electron microscopy have resolved nearly all 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 Multimer’s accuracy in modeling
interactions between the NaV α-subunit and its protein partners, including
auxiliary β-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.
Furthermore, AlphaFold Multimer models NaV complexes with auxiliary
β-subunits and calmodulin with high accuracy, and the presence of protein
partners significantly alters the modeled conformational landscape of the
NaV α-subunit. 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.