DOI: 10.5061/dryad.rn8pk0pn3
title: Exploring voltage-gated sodium channel conformations and protein-protein interactions using AlphaFold2

description: 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.
description: Model generation
 All models in this study were generated using colabfold-batch
 1.5.0 (localcolabfold) (Mirdita et al., 2022) with a subsampled MSA
 (Monteiro da Silva et al., 2024). The execution was performed with the
 following options: <em>colabfold_batch
 --num-models 5 --model-type auto --msa-mode mmseqs2_uniref_env
 \</em> <em>--num-seeds 20
 \</em> <em>--templates  --max-seq 256
 --max-extra-seq 512 \</em>
 <em>--num-recycle 6 --save-recycles nav17alphafull.fasta
 outfiles-6r-256-512-sr</em> The key aspect that
 makes this colabfold execution an MSA subsampled implementation is the
 setting of two parameters: --max-seq and --max-extra-seq. The --max-seq
 parameter defines the maximum number of sequences randomly selected from
 the original generated master MSA. The target sequence is always included.
 Then, the remaining sequences are clustered around the selected sequences
 using Hamming distance. Finally, the cluster centers (the --max-seq
 sequences chosen originally) are used alongside a sample from each cluster
 (as defined by the --max-extra-seq parameter) for the structure prediction
 network. Previous studies have shown that adjusting
 these parameters, particularly by reducing their values, can increase the
 diversity of conformational sampling (del Alamo et al., 2022). In this
 study, we used values of 256 for --max-seq and 512 for --max-extra-seq, as
 these settings enhanced conformational diversity without compromising
 model accuracy in the original study (Monteiro da Silva et al., 2024). A
 total of 100 models were generated for each study case, using 6 recycles.
 Intermediate models were saved at each recycle step, resulting in 7 models
 per generated structure, ranging from recycle 0 to recycle 6, making a
 total of 700 models per test case (see Data Availability).
 Model Analysis
 All models were visually analyzed using UCSF ChimeraX (Goddard et
 al., 2018). Distances were automatically calculated with custom Python
 scripts utilizing the PyRosetta package (Chaudhury et al., 2010). All
 other analyses, calculations, and figure generation were conducted with
 custom Python scripts. Pore analysis of the channels was conducted using
 the MOLEonline website (Pravda et al., 2018).
description: # Exploring voltage-gated sodium channel conformations and protein-protein
 interactions using AlphaFold2
 [https://doi.org/10.5061/dryad.rn8pk0pn3](https://doi.org/10.5061/dryad.rn8pk0pn3) ## Description of the data and file structure This study explores the capability of AlphaFold2 to sample multiple voltage-gated sodium (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 from experimental structures, new states not yet experimentally identified, 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 conformational landscape of the NaV α-subunit. This data set contains all NaV channel models described in a related article. ### Files and variables #### File: hNaV1.1-alphaOnly.tar.gz **Description:** Contains all the generated models for hNaV1.1 with the α-subunit alone (in Protein Data Bank (PDB) format). As 100 models were generated, saving intermediate recycles (from recycle 0 to 6), this results in 700 PDB files. ·       **hNav1.1***unrelaxed***rank_001**_alphafold2_ptm_model_3_seed_015.**r0**.pdb – Top-ranked model at recycle 0. ·       **hNav1.1***unrelaxed***rank_001**_alphafold2_ptm_model_3_seed_015.**r1**.pdb – Top-ranked model at recycle 1. ·       […] ·       **hNav1.1***unrelaxed***rank_001**_alphafold2_ptm_model_3_seed_015.**r6**.pdb – Top-ranked model at recycle 6. ·       […] ·       **hNav1.1***unrelaxed***rank_002**_alphafold2_ptm_model_3_seed_002.**r0**.pdb – Second Top-ranked model at recycle 0. ·       […] ·       **hNav1.1***unrelaxed***rank_100**_alphafold2_ptm_model_5_seed_009.**r6**.pdb – Model ranked last at recycle 6. #### File: hNaV1.1-b1.tar.gz **Description:** Contains all the generated models for hNaV1.1 with the α-subunit and β-1 (in Protein Data Bank (PDB) format). As 100 models were generated, saving intermediate recycles (from recycle 0 to 6), this results in 700 PDB files.             (File names follow the pattern as in hNaV1.1-alphaOnly.tar.gz) #### File: hNaV1.1-b2.tar.gz **Description:** Contains all the generated models for hNaV1.1 with the α-subunit and β-2 (in Protein Data Bank (PDB) format). As 100 models were generated, saving intermediate recycles (from recycle 0 to 6), this results in 700 PDB files.    (File names follow the pattern as in hNaV1.1-alphaOnly.tar.gz) #### File: hNaV1.1-b3.tar.gz **Description:** Contains all the generated models for hNaV1.1 with the α-subunit and β-3 (in Protein Data Bank (PDB) format). As 100 models were generated, saving intermediate recycles (from recycle 0 to 6), this results in 700 PDB files.    (File names follow the pattern as in hNaV1.1-alphaOnly.tar.gz) #### File: hNaV1.1-b4.tar.gz **Description:** Contains all the generated models for hNaV1.1 with the α-subunit and β-4 (in Protein Data Bank (PDB) format). As 100 models were generated, saving intermediate recycles (from recycle 0 to 6), this results in 700 PDB files.    (File names follow the pattern as in hNaV1.1-alphaOnly.tar.gz) #### File: hNaV1.2-alphaOnly.tar.gz **Description:** Contains all the generated models for hNaV1.2 with the α-subunit alone (in Protein Data Bank (PDB) format). As 100 models were generated, saving intermediate recycles (from recycle 0 to 6), this results in 700 PDB files.    (File names follow the pattern as in hNaV1.1-alphaOnly.tar.gz) #### File: hNaV1.2-CaM.tar.gz **Description:** Contains all the generated models for hNaV1.2 with the α-subunit and calmodulin (CaM) (in Protein Data Bank (PDB) format). As 100 models were generated, saving intermediate recycles (from recycle 0 to 6), this results in 700 PDB files.    (File names follow the pattern as in hNaV1.1-alphaOnly.tar.gz) #### File: hNaV1.3-alphaOnly.tar.gz **Description:** Contains all the generated models for hNaV1.3 with the α-subunit alone (in Protein Data Bank (PDB) format). As 100 models were generated, saving intermediate recycles (from recycle 0 to 6), this results in 700 PDB files.    (File names follow the pattern as in hNaV1.1-alphaOnly.tar.gz) #### File: hNaV1.4-alphaOnly.tar.gz **Description:** Contains all the generated models for hNaV1.4 with the α-subunit alone (in Protein Data Bank (PDB) format). As 100 models were generated, saving intermediate recycles (from recycle 0 to 6), this results in 700 PDB files.    (File names follow the pattern as in hNaV1.1-alphaOnly.tar.gz) #### File: hNaV1.5-alphaOnly.tar.gz **Description:** Contains all the generated models for hNaV1.5 with the α-subunit alone (in Protein Data Bank (PDB) format). As 100 models were generated, saving intermediate recycles (from recycle 0 to 6), this results in 700 PDB files.    (File names follow the pattern as in hNaV1.1-alphaOnly.tar.gz) #### File: hNaV1.5-CaM.tar.gz **Description:** Contains all the generated models for hNaV1.5 with the α-subunit and calmodulin (CaM) (in Protein Data Bank (PDB) format). As 100 models were generated, saving intermediate recycles (from recycle 0 to 6), this results in 700 PDB files.    (File names follow the pattern as in hNaV1.1-alphaOnly.tar.gz) #### File: hNaV1.6-alphaOnly.tar.gz **Description:** Contains all the generated models for hNaV1.6 with the α-subunit alone (in Protein Data Bank (PDB) format). As 100 models were generated, saving intermediate recycles (from recycle 0 to 6), this results in 700 PDB files.    (File names follow the pattern as in hNaV1.1-alphaOnly.tar.gz) #### File: hNaV1.7-alphaOnly.tar.gz **Description:** Contains all the generated models for hNaV1.7 with the α-subunit alone (in Protein Data Bank (PDB) format). As 100 models were generated, saving intermediate recycles (from recycle 0 to 6), this results in 700 PDB files.    (File names follow the pattern as in hNaV1.1-alphaOnly.tar.gz) #### File: hNaV1.7-b1.tar.gz **Description:** Contains all the generated models for hNaV1.7 with the α-subunit and β-1 (in Protein Data Bank (PDB) format). As 100 models were generated, saving intermediate recycles (from recycle 0 to 6), this results in 700 PDB files.    (File names follow the pattern as in hNaV1.1-alphaOnly.tar.gz) #### File: hNaV1.7-b2.tar.gz **Description:** Contains all the generated models for hNaV1.7 with the α-subunit and β-2 (in Protein Data Bank (PDB) format). As 100 models were generated, saving intermediate recycles (from recycle 0 to 6), this results in 700 PDB files.    (File names follow the pattern as in hNaV1.1-alphaOnly.tar.gz) #### File: hNaV1.7-b3.tar.gz **Description:** Contains all the generated models for hNaV1.7 with the α-subunit and β-3 (in Protein Data Bank (PDB) format). As 100 models were generated, saving intermediate recycles (from recycle 0 to 6), this results in 700 PDB files.    (File names follow the pattern as in hNaV1.1-alphaOnly.tar.gz) #### File: hNaV1.7-b4.tar.gz **Description:** Contains all the generated models for hNaV1.7 with the α-subunit and β-4 (in Protein Data Bank (PDB) format). As 100 models were generated, saving intermediate recycles (from recycle 0 to 6), this results in 700 PDB files.    (File names follow the pattern as in hNaV1.1-alphaOnly.tar.gz) #### File: hNaV1.8-alphaOnly.tar.gz **Description:** Contains all the generated models for hNaV1.8 with the α-subunit alone (in Protein Data Bank (PDB) format). As 100 models were generated, saving intermediate recycles (from recycle 0 to 6), this results in 700 PDB files.    (File names follow the pattern as in hNaV1.1-alphaOnly.tar.gz) #### File: hNaV1.9-alphaOnly.tar.gz **Description:** Contains all the generated models for hNaV1.9 with the α-subunit alone (in Protein Data Bank (PDB) format). As 100 models were generated, saving intermediate recycles (from recycle 0 to 6), this results in 700 PDB files.    (File names follow the pattern as in hNaV1.1-alphaOnly.tar.gz) #### File: hNaX.tar.gz **Description:** Contains all the generated models for hNaX, a non-voltage-gated NaV-like channel (in Protein Data Bank (PDB) format). As 100 models were generated, saving intermediate recycles (from recycle 0 to 6), this results in 700 PDB files.    (File names follow the pattern as in hNaV1.1-alphaOnly.tar.gz) ## Code/software Each \\*.pdb file can be opened using PyMol software ([https://pymol.org/2/](https://pymol.org/2/)) or UCSF Chimera software [https://www.cgl.ucsf.edu/chimera/.](https://www.cgl.ucsf.edu/chimera/)