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Structural insights enable drug discovery for the neuronal NBCn2 carbonate transporter.

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  1. [1] § Methods › CryoEM sample preparation, data collection and processing ↔ src/alphafold3/model/mmcif_metadata.py, lines 144–232 · score 0.50 · ab initio, classification, templates, models

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Python · 249 lines · 9.5 KB · Apache-2.0 · 1 match

  1. # Copyright 2024 DeepMind Technologies Limited
  2. #
  3. # AlphaFold 3 source code is licensed under the Apache License, Version 2.0
  4. # (the "License"); you may not use this file except in compliance with the
  5. # License. You may obtain a copy of the License at
  6. #
  7. # http://www.apache.org/licenses/LICENSE-2.0
  8. #
  9. # Unless required by applicable law or agreed to in writing, software
  10. # distributed under the License is distributed on an "AS IS" BASIS,
  11. # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  12. # See the License for the specific language governing permissions and
  13. # limitations under the License.
  14. #
  15. # To request access to the AlphaFold 3 model parameters, follow the process set
  16. # out at https://github.com/google-deepmind/alphafold3. You may only use these
  17. # if received directly from Google. Use is subject to terms of use available at
  18. # https://github.com/google-deepmind/alphafold3/blob/main/WEIGHTS_TERMS_OF_USE.md
  20. """Adds mmCIF metadata (to be ModelCIF-conformant) and author and legal info."""
  22. from typing import Final
  24. from alphafold3.structure import mmcif
  25. import numpy as np
  27. _LICENSE_URL: Final[str] = (
  28. 'https://github.com/google-deepmind/alphafold3/blob/main/OUTPUT_TERMS_OF_USE.md'
  29. )
  31. _LICENSE: Final[str] = f"""
  32. Non-commercial use only, by using this file you agree to the terms of use found
  33. at {_LICENSE_URL}.
  34. To request access to the AlphaFold 3 model parameters, follow the process set
  35. out at https://github.com/google-deepmind/alphafold3. You may only use these if
  36. received directly from Google. Use is subject to terms of use available at
  37. https://github.com/google-deepmind/alphafold3/blob/main/WEIGHTS_TERMS_OF_USE.md.
  38. """.strip()
  40. _DISCLAIMER: Final[str] = """\
  41. AlphaFold 3 and its output are not intended for, have not been validated for,
  42. and are not approved for clinical use. They are provided "as-is" without any
  43. warranty of any kind, whether expressed or implied. No warranty is given that
  44. use shall not infringe the rights of any third party.
  45. """.strip()
  47. _MMCIF_PAPER_AUTHORS: Final[tuple[str, ...]] = (
  48. 'Google DeepMind',
  49. 'Isomorphic Labs',
  50. )
  52. # Authors of the mmCIF - we set them to be equal to the authors of the paper.
  53. _MMCIF_AUTHORS: Final[tuple[str, ...]] = _MMCIF_PAPER_AUTHORS
  56. def add_metadata_to_mmcif(
  57. old_cif: mmcif.Mmcif,
  58. *,
  59. version: str,
  60. model_id: bytes,
  61. keep_license: bool = True,
  62. ) -> mmcif.Mmcif:
  63. """Adds metadata to a mmCIF to make it ModelCIF-conformant."""
  64. cif = {}
  66. # ModelCIF conformation dictionary.
  67. cif['_audit_conform.dict_name'] = ['mmcif_ma.dic']
  68. cif['_audit_conform.dict_version'] = ['1.4.5']
  69. cif['_audit_conform.dict_location'] = [
  70. 'https://raw.githubusercontent.com/ihmwg/ModelCIF/master/dist/mmcif_ma.dic'
  71. ]
  73. if keep_license:
  74. cif['_pdbx_data_usage.id'] = ['1', '2']
  75. cif['_pdbx_data_usage.type'] = ['license', 'disclaimer']
  76. cif['_pdbx_data_usage.details'] = [_LICENSE, _DISCLAIMER]
  77. cif['_pdbx_data_usage.url'] = [_LICENSE_URL, '?']
  78. else:
  79. cif['_pdbx_data_usage.id'] = ['1']
  80. cif['_pdbx_data_usage.type'] = ['disclaimer']
  81. cif['_pdbx_data_usage.details'] = [_DISCLAIMER]
  82. cif['_pdbx_data_usage.url'] = ['?']
  84. # Structure author details.
  85. cif['_audit_author.name'] = []
  86. cif['_audit_author.pdbx_ordinal'] = []
  87. for author_index, author_name in enumerate(_MMCIF_AUTHORS, start=1):
  88. cif['_audit_author.name'].append(author_name)
  89. cif['_audit_author.pdbx_ordinal'].append(str(author_index))
  91. # Paper author details.
  92. cif['_citation_author.citation_id'] = []
  93. cif['_citation_author.name'] = []
  94. cif['_citation_author.ordinal'] = []
  95. for author_index, author_name in enumerate(_MMCIF_PAPER_AUTHORS, start=1):
  96. cif['_citation_author.citation_id'].append('primary')
  97. cif['_citation_author.name'].append(author_name)
  98. cif['_citation_author.ordinal'].append(str(author_index))
  100. # Paper citation details.
  101. cif['_citation.id'] = ['primary']
  102. cif['_citation.title'] = [
  103. 'Accurate structure prediction of biomolecular interactions with'
  104. ' AlphaFold 3'
  105. ]
  106. cif['_citation.journal_full'] = ['Nature']
  107. cif['_citation.journal_volume'] = ['630']
  108. cif['_citation.page_first'] = ['493']
  109. cif['_citation.page_last'] = ['500']
  110. cif['_citation.year'] = ['2024']
  111. cif['_citation.journal_id_ASTM'] = ['NATUAS']
  112. cif['_citation.country'] = ['UK']
  113. cif['_citation.journal_id_ISSN'] = ['0028-0836']
  114. cif['_citation.journal_id_CSD'] = ['0006']
  115. cif['_citation.book_publisher'] = ['?']
  116. cif['_citation.pdbx_database_id_PubMed'] = ['38718835']
  117. cif['_citation.pdbx_database_id_DOI'] = ['10.1038/s41586-024-07487-w']
  119. # Type of data in the dataset including data used in the model generation.
  120. cif['_ma_data.id'] = ['1']
  121. cif['_ma_data.name'] = ['Model']
  122. cif['_ma_data.content_type'] = ['model coordinates']
  124. # Description of number of instances for each entity.
  125. cif['_ma_target_entity_instance.asym_id'] = old_cif['_struct_asym.id']
  126. cif['_ma_target_entity_instance.entity_id'] = old_cif[
  127. '_struct_asym.entity_id'
  128. ]
  129. cif['_ma_target_entity_instance.details'] = ['.'] * len(
  130. cif['_ma_target_entity_instance.entity_id']
  131. )
  133. # Details about the target entities.
  134. cif['_ma_target_entity.entity_id'] = cif[
  135. '_ma_target_entity_instance.entity_id'
  136. ]
  137. cif['_ma_target_entity.data_id'] = ['1'] * len(
  138. cif['_ma_target_entity.entity_id']
  139. )
  140. cif['_ma_target_entity.origin'] = ['.'] * len(
  141. cif['_ma_target_entity.entity_id']
  142. )
  144. # Details of the models being deposited.
  145. cif['_ma_model_list.ordinal_id'] = ['1']
  146. cif['_ma_model_list.model_id'] = ['1']
  147. cif['_ma_model_list.model_group_id'] = ['1']
  148. cif['_ma_model_list.model_name'] = ['Top ranked model']
  150. cif['_ma_model_list.model_group_name'] = [
  151. f'AlphaFold-beta-20231127 ({version})'
  152. ]
  153. cif['_ma_model_list.data_id'] = ['1']
  154. cif['_ma_model_list.model_type'] = ['Ab initio model']
  156. # Software used.
  157. cif['_software.pdbx_ordinal'] = ['1']
  158. cif['_software.name'] = ['AlphaFold']
  159. cif['_software.version'] = [
  160. f'AlphaFold-beta-20231127 ({model_id.decode("ascii")})'
  161. ]
  162. cif['_software.type'] = ['package']
  163. cif['_software.description'] = ['Structure prediction']
  164. cif['_software.classification'] = ['other']
  165. cif['_software.date'] = ['?']
  167. # Collection of software into groups.
  168. cif['_ma_software_group.ordinal_id'] = ['1']
  169. cif['_ma_software_group.group_id'] = ['1']
  170. cif['_ma_software_group.software_id'] = ['1']
  172. # Method description to conform with ModelCIF.
  173. cif['_ma_protocol_step.ordinal_id'] = ['1', '2', '3']
  174. cif['_ma_protocol_step.protocol_id'] = ['1', '1', '1']
  175. cif['_ma_protocol_step.step_id'] = ['1', '2', '3']
  176. cif['_ma_protocol_step.method_type'] = [
  177. 'coevolution MSA',
  178. 'template search',
  179. 'modeling',
  180. ]
  182. # Details of the metrics use to assess model confidence.
  183. cif['_ma_qa_metric.id'] = ['1', '2']
  184. cif['_ma_qa_metric.name'] = ['pLDDT', 'pLDDT']
  185. # Accepted values are distance, energy, normalised score, other, zscore.
  186. cif['_ma_qa_metric.type'] = ['pLDDT', 'pLDDT']
  187. cif['_ma_qa_metric.mode'] = ['global', 'local']
  188. cif['_ma_qa_metric.software_group_id'] = ['1', '1']
  190. # Global model confidence pLDDT value.
  191. cif['_ma_qa_metric_global.ordinal_id'] = ['1']
  192. cif['_ma_qa_metric_global.model_id'] = ['1']
  193. cif['_ma_qa_metric_global.metric_id'] = ['1']
  194. # Mean over all atoms, since AlphaFold 3 outputs pLDDT per-atom.
  195. global_plddt = np.mean(
  196. [float(v) for v in old_cif['_atom_site.B_iso_or_equiv']]
  197. )
  198. cif['_ma_qa_metric_global.metric_value'] = [f'{global_plddt:.2f}']
  200. # Local (per residue) model confidence pLDDT value.
  201. cif['_ma_qa_metric_local.ordinal_id'] = []
  202. cif['_ma_qa_metric_local.model_id'] = []
  203. cif['_ma_qa_metric_local.label_asym_id'] = []
  204. cif['_ma_qa_metric_local.label_seq_id'] = []
  205. cif['_ma_qa_metric_local.label_comp_id'] = []
  206. cif['_ma_qa_metric_local.metric_id'] = []
  207. cif['_ma_qa_metric_local.metric_value'] = []
  209. plddt_grouped_by_res = {}
  210. for *res, atom_plddt in zip(
  211. old_cif['_atom_site.label_asym_id'],
  212. old_cif['_atom_site.label_seq_id'],
  213. old_cif['_atom_site.label_comp_id'],
  214. old_cif['_atom_site.B_iso_or_equiv'],
  215. ):
  216. plddt_grouped_by_res.setdefault(tuple(res), []).append(float(atom_plddt))
  218. for ordinal_id, ((chain_id, res_id, res_name), res_plddts) in enumerate(
  219. plddt_grouped_by_res.items(), start=1
  220. ):
  221. res_plddt = np.mean(res_plddts)
  222. cif['_ma_qa_metric_local.ordinal_id'].append(str(ordinal_id))
  223. cif['_ma_qa_metric_local.model_id'].append('1')
  224. cif['_ma_qa_metric_local.label_asym_id'].append(chain_id)
  225. cif['_ma_qa_metric_local.label_seq_id'].append(res_id)
  226. cif['_ma_qa_metric_local.label_comp_id'].append(res_name)
  227. cif['_ma_qa_metric_local.metric_id'].append('2') # See _ma_qa_metric.id.
  228. cif['_ma_qa_metric_local.metric_value'].append(f'{res_plddt:.2f}')
  230. cif['_atom_type.symbol'] = sorted(set(old_cif['_atom_site.type_symbol']))
  232. return old_cif.copy_and_update(cif)
  235. def add_legal_comment(cif: str) -> str:
  236. """Adds legal comment at the top of the mmCIF."""
  237. # fmt: off
  238. # pylint: disable=line-too-long
  239. comment = (
  240. '# By using this file you agree to the legally binding terms of use found at\n'
  241. f'# {_LICENSE_URL}.\n'
  242. '# To request access to the AlphaFold 3 model parameters, follow the process set\n'
  243. '# out at https://github.com/google-deepmind/alphafold3. You may only use these if\n'
  244. '# received directly from Google. Use is subject to terms of use available at\n'
  245. '# https://github.com/google-deepmind/alphafold3/blob/main/WEIGHTS_TERMS_OF_USE.md.'
  246. )
  247. # pylint: enable=line-too-long
  248. # fmt: on
  249. return f'{comment}\n{cif}'

mmcif_metadata.py at commit a66cc52, under Apache-2.0 · at the source

Overview

Authors: Shifan Yang1,2,3, Yihan Zhao1,2,3,4, Sezen Vatansever1,2,3, Gregory Zilberg4,5, Michael J. Capper4, Jinglong Zhang1,2, Joshua Stamos4,5,6, Keino Hutchinson4, Audrey L. Warren4, Alexander C. Stone1,2,3, Anwar Abbassi1,2,3, Eric Purisic4,5, Lap Ho1,2, Aiqun Li1,2, Jinye Dai4,5, Avner Schlessinger1,4,7, Bin Zhang1,2,3,4,5,8, Daniel Wacker1,4,5
ORCID iDs: Shifan Yang, Michael J. Capper, Jinglong Zhang, Joshua Stamos, Audrey L. Warren, Alexander C. Stone, Aiqun Li, Avner Schlessinger, Daniel Wacker
  1. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place,New York, NY USA
  2. Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place,New York, NY USA
  3. Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai,New York, New York USA
  4. Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai,New York, New York USA
  5. Department of Neuroscience, Icahn School of Medicine at Mount Sinai,New York, New York USA
  6. Present Address: The School of Theoretical and Applied Science, Ramapo College of New Jersey,Mahwah, New Jersey USA
  7. AI Small Molecule Drug Discovery Center, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York New York, USA
  8. Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai,New York, New York USA
Institutions: Icahn School of Medicine at Mount Sinai (United States); Ramapo College (United States)
Journal: Nature communications, volume 17, issue 1, article 8923
Dates: received 9 May 2025; accepted 18 June 2026; published online 22 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-75444-4 · PMID 42637710 · PMCID PMC13503941 · OpenAlex W7170047222
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: histology / microscopy (modality), human (organism), mouse (organism), cellular / molecular (subfield)
Methods: Statistics, fMRI & imaging
Keywords: Cryoelectron microscopy, Virtual screening, Computational biophysics, Transporters
MeSH: Drug Discovery*, Neurons*, Sodium-Bicarbonate Symporters*, Animals, Brain, Cryoelectron Microscopy, Humans, Mice, Molecular Docking Simulation (* major topic)
Topic: Ion Transport and Channel Regulation (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: NINDS (R01NS145483)
Citations: not cited yet (Europe PMC); 85 references in the paper

Abstract

NBCn2 (SLC4A10), a member of the SLC4 solute carrier (SLC) family, is a sodium-dependent (bi)carbonate transporter that regulates acid extrusion in various brain regions. Mutations in NBCn2 cause severe neurodevelopmental disorders in humans, and knock out studies suggest that its role in regulating neuronal excitability could hold therapeutic potential for seizure disorders such as epilepsy. Despite its physiological importance, NBCn2’s molecular mechanisms remain largely unknown, and there is limited availability of tool compounds to further probe its role in health and disease. Combining cryoEM with computational docking and simulation studies, we herein elucidate NBCn2’s molecular architecture and substrate binding mechanisms on the atomic scale. Via structure-based drug discovery we further identify a compound series that inhibits NBCn2-mediated transport, and characterize its inhibitory mechanisms via cryoEM. Lastly, we showcase the potential of this compound series to template useful probes by demonstrating pharmacological activity both in primary culture as well as brain slices.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

google-deepmind/alphafold3

License: Apache-2.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: a66cc5226d5fbcf7b08af4495af6fd7262178e38, 21 September 2026
Languages: Python (90), C++ (19), C/C++ (18), Shell (3)
Size: 203 files, 130 scripts
Software Heritage: archived
Found in: the text, “Ion modeling using AlphaFold3”
Holds: README, license file, environment (pyproject.toml, uv.lock, docker/Dockerfile), tests, continuous integration, documentation
Not found: CITATION.cff
Tools: NumPy (40 files), JAX (30 files), RDKit (6 files), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
132 files
At the source: github.com/google-deepmind/alphafold3

knk9596/SLC4A10_Modeling

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 5ecad490b567366c00db877078b9c9a743dc9554, 1 September 2026
Languages: Python (4)
Size: 5 files, 4 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (3 files), Matplotlib (2 files), Biopython (1 file), MDAnalysis (1 file), pandas (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
5 files
At the source: github.com/knk9596/SLC4A10_Modeling

Code availability

Analysis scripts for MD simulations and AlphaFold3 modeling are available on GitHub (https://github.com/knk9596/SLC4A10_Modeling) and Zenodo (10.5281/zenodo.20278709).

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 134 scripts, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data availability

Density maps and structure coordinates have been deposited in the Electron Microscopy Data Bank (EMDB) and the PDB. A structure of NBCn2-Apo has been deposited under EMD-48304 (https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-48304) and PDB-9MIX (https://doi.org/10.2210/pdb9MIX/pdb), a structure of NBCn2-NaHCO3 has been deposited under EMD-48318 (https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-48318) and PDB-9MJO (https://doi.org/10.2210/pdb9MJO/pdb), and a structure of NBCn2-Cmpd38J has been deposited under EMD-48320 (https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-48320) and PDB-9MK6 (https://doi.org/10.2210/pdb9MK6/pdb). Our study further includes a comparison with previous structures PDB-7RTM (https://doi.org/10.2210/pdb7RTM/pdb) and PDB-7TY7 (https://doi.org/10.2210/pdb7TY7/pdb). Source data are provided with this paper. Loop modeling, SILCS fragment map and MD simulation data are available on Zenodo (10.5281/zenodo.14983143). AlphaFold3 modeling data are available on Zenodo (10.5281/zenodo.20278709). Source data are provided in this paper.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 18 authors, 4 keywords, 9 MeSH terms, 1 funder, 82 references.

Cite

This paper

Yang, S., Zhao, Y., Vatansever, S., Zilberg, G., Capper, M. J., Zhang, J., Stamos, J., Hutchinson, K., Warren, A. L., Stone, A. C., Abbassi, A., Purisic, E., Ho, L., Li, A., Dai, J., Schlessinger, A., Zhang, B., & Wacker, D. (2026). Structural insights enable drug discovery for the neuronal NBCn2 carbonate transporter. Nature communications, 17(1), 8923. https://doi.org/10.1038/s41467-026-75444-4

BibTeX

@article{yang2026structural,
author = {Yang, Shifan and Zhao, Yihan and Vatansever, Sezen and Zilberg, Gregory and Capper, Michael J. and Zhang, Jinglong and Stamos, Joshua and Hutchinson, Keino and Warren, Audrey L. and Stone, Alexander C. and Abbassi, Anwar and Purisic, Eric and Ho, Lap and Li, Aiqun and Dai, Jinye and Schlessinger, Avner and Zhang, Bin and Wacker, Daniel},
title = {{Structural insights enable drug discovery for the neuronal NBCn2 carbonate transporter}},
journal = {Nature communications},
year = {2026},
month = jul,
volume = {17},
number = {1},
pages = {8923},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-75444-4},
url = {https://doi.org/10.1038/s41467-026-75444-4},
pmid = {42637710},
pmcid = {PMC13503941}
}

RIS

TY - JOUR
AU - Yang, Shifan
AU - Zhao, Yihan
AU - Vatansever, Sezen
AU - Zilberg, Gregory
AU - Capper, Michael J.
AU - Zhang, Jinglong
AU - Stamos, Joshua
AU - Hutchinson, Keino
AU - Warren, Audrey L.
AU - Stone, Alexander C.
AU - Abbassi, Anwar
AU - Purisic, Eric
AU - Ho, Lap
AU - Li, Aiqun
AU - Dai, Jinye
AU - Schlessinger, Avner
AU - Zhang, Bin
AU - Wacker, Daniel
TI - Structural insights enable drug discovery for the neuronal NBCn2 carbonate transporter
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/07/22
VL - 17
IS - 1
SP - 8923
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-75444-4
UR - https://doi.org/10.1038/s41467-026-75444-4
LA - en
ER -

CSL-JSON

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"PMCID": "PMC13503941",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-75444-4",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
22
]
]
}
}

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