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Area of Science:

  • Structural biology
  • Computational biophysics
  • Pharmacology

Background:

  • Voltage-gated sodium (NaV) channels are crucial for electrical signaling in cells.
  • Developing subtype-selective NaV channel drugs is difficult due to conserved structures.
  • Cryo-electron microscopy has advanced NaV channel structure determination but has limitations in capturing dynamic states.

Purpose of the Study:

  • To evaluate AlphaFold2's ability to predict diverse NaV channel conformations.
  • To assess AlphaFold Multimer's accuracy in modeling NaV channel complexes with accessory proteins.
  • To explore how protein interactions influence NaV channel conformational dynamics.

Main Methods:

  • Utilized AlphaFold2 for conformational sampling of NaV channels with enhanced techniques.
  • Employed subsampled multiple sequence alignments and varied recycles for improved sampling.
  • Applied correlation and clustering analyses to understand domain movements and state ensembles.
  • Modeled NaV α-subunit interactions with β-subunits and calmodulin using AlphaFold Multimer.

Main Results:

  • AlphaFold2 successfully modeled known, novel, and intermediate NaV channel conformations.
  • Predicted structures revealed coordinated domain movements and recurring conformational states.
  • AlphaFold Multimer accurately modeled NaV channel complexes with β-subunits and calmodulin.
  • Protein partners significantly modulated the NaV α-subunit's conformational landscape and state coupling.

Conclusions:

  • Deep learning methods show promise for understanding NaV channel structure, gating, and modulation.
  • Predicted models offer valuable hypotheses but require experimental validation.
  • Computational approaches can expand insights into the complex dynamics of ion channels.