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Structural and mechanistic insights into gating and allosteric modulation of GluN1-GluN3A NMDA receptors.

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The authors' code

Python · 291 lines · 8.5 KB · CC-BY-4.0

  1. #!/opt/anaconda3/bin/python3
  3. import streamlit as st
  4. import glob
  5. import os
  6. import read_lif
  7. import numpy as np
  8. import cv2
  9. import matplotlib.pyplot as plt
  10. import math
  11. import plotly.graph_objects as go
  12. import SessionState
  13. from plotly.offline import plot
  14. from skimage.measure import label, regionprops
  15. from skimage import data
  16. from skimage import color
  17. from skimage.morphology import extrema
  18. from skimage import exposure
  19. from scipy import spatial
  21. def bytescale(data, in_min, in_max):
  22. data = np.clip(data, in_min, in_max)
  23. data = (data - float(in_min)) / float(in_max - in_min)
  24. return np.array(data * 255, dtype=np.uint8)
  27. def pixel_list_by_radius(radius):
  28. pixel_list = []
  29. max_pix = math.ceil(radius)
  30. for x in range(max_pix+1):
  31. for y in range(max_pix+1):
  32. if math.sqrt(x*x+y*y) < radius:
  33. pixel_list.append((x,y))
  34. if x > 0:
  35. pixel_list.append((-x,y))
  36. if y > 0:
  37. pixel_list.append((x,-y))
  38. if x >0 and y > 0:
  39. pixel_list.append((-x,-y))
  40. return(pixel_list)
  43. @st.cache
  44. def load_zero_frame(filename,sel_series,sel_channel):
  45. reader = read_lif.Reader(filename)
  46. series = reader.getSeries()
  47. image = series[sel_series].getFrame2D(T=0, channel = sel_channel, dtype=np.uint16)
  48. image_b = bytescale(image,np.min(image),np.max(image))
  49. return image_b
  51. @st.cache
  52. def load_final_frame(filename,sel_series,sel_channel):
  53. reader = read_lif.Reader(filename)
  54. series = reader.getSeries()
  55. num_timepoints = series[sel_series].getNbFrames()
  56. image = series[sel_series].getFrame2D(T=num_timepoints-1, channel = sel_channel, dtype=np.uint16)
  57. image_b = bytescale(image,np.min(image),np.max(image))
  58. return image_b
  60. @st.cache
  61. def load_time_series(filename,sel_series,sel_channel):
  62. reader = read_lif.Reader(filename)
  63. series = reader.getSeries()
  64. num_timepoints = series[sel_series].getNbFrames()
  65. shape = series[sel_series].get2DShape()
  66. data = np.zeros((shape[0],shape[1],num_timepoints))
  67. for tp in range(num_timepoints):
  68. im = series[sel_series].getFrame2D(T=tp, channel=sel_channel,dtype=np.uint16)
  69. data[:,:,tp] = im
  70. return data
  72. @st.cache
  73. def get_peaks_positions(blur, percent):
  74. h_maxima = extrema.h_maxima(blur, percent)
  75. label_h_maxima = label(h_maxima)
  77. return [(x.centroid[1], x.centroid[0]) for x in regionprops(label_h_maxima)]
  82. st.sidebar.title("Input selection")
  83. filename = st.sidebar.selectbox(
  84. 'Input File',
  85. glob.glob("*.lif"))
  87. reader = read_lif.Reader(filename)
  88. series = reader.getSeriesHeaders()
  90. sel_series_i = st.sidebar.selectbox(
  91. 'Series',
  92. list(range(len(series))),format_func=lambda x:series[x].getName())
  94. channels = series[sel_series_i].getChannels()
  96. sel_channel_i = st.sidebar.selectbox(
  97. 'Channel',
  98. list(range(len(channels))),format_func=lambda x:channels[x].getAttribute('ChannelTag'))
  100. #Load timepoint 0 of desired image
  101. image_b = load_zero_frame(filename,sel_series_i,sel_channel_i)
  102. image_b_final = load_final_frame(filename,sel_series_i,sel_channel_i)
  104. # Blur image
  105. st.sidebar.title("Peak picking parameters")
  106. blurring = st.sidebar.slider("Blurring", min_value=1, max_value=21, value=5)
  107. blur = cv2.GaussianBlur(image_b,(blurring,blurring),0)
  108. blur_final = cv2.GaussianBlur(image_b_final,(blurring,blurring),0)
  109. #st.image(blur)
  112. # Threshold image based on percentage
  113. percent = st.sidebar.slider("H-height", min_value=1., max_value=100., value=10.,step=0.1,format="%.3f")
  114. percent_final = st.sidebar.slider("Exclude based on final H-height", min_value=1., max_value=100., value=10.,step=0.1,format="%.3f")
  117. coor_list_start = get_peaks_positions(blur, percent)
  118. coor_list_final = get_peaks_positions(blur_final, percent_final)
  119. #st.write(len(coor_list_start))
  120. #st.write(len(coor_list_final))
  122. final_tree = spatial.KDTree(coor_list_final)
  124. coor_list = [ x for x in coor_list_start if final_tree.query(x,distance_upper_bound=3)[1] == len(coor_list_final)]
  126. #st.write(len(coor_list))
  128. skip = st.button('skip')
  129. step1 = st.button('1 step')
  130. step2 = st.button('2 step')
  131. step3 = st.button('3 step')
  132. step4 = st.button('4 step')
  133. step5 = st.button('5 step')
  135. ss = SessionState.get(position=1, count0=0, count1=0, count2=0, count3=0, count4=0, count5=0)
  136. #ss1 = SessionState.get(count=1)
  137. widget = st.empty()
  139. #ss1 = SessionState.get(count1=1)
  141. if skip:
  142. ss.position = ss.position + 1
  143. ss.count0 = ss.count0 + 1
  144. if step1:
  145. ss.position = ss.position + 1
  146. ss.count1 = ss.count1 + 1
  147. if step2:
  148. ss.position = ss.position + 1
  149. ss.count2 = ss.count2 + 1
  150. if step3:
  151. ss.position = ss.position + 1
  152. ss.count3 = ss.count3 + 1
  153. if step4:
  154. ss.position = ss.position + 1
  155. ss.count4 = ss.count4 + 1
  156. if step5:
  157. ss.position = ss.position + 1
  158. ss.count5 = ss.count5 + 1
  159. st.write('skip=',ss.count0,'1 step=',ss.count1, '2 step=',ss.count2, '3 step=',ss.count3, '4 step=',ss.count4, '5 step=',ss.count5)
  160. max=len(coor_list)
  161. ss.position = widget.slider('Peak', 0, max, ss.position)
  162. #ss1.count1 = st.write("", ss1.count1)
  164. #selected_keypoint_i = st.slider("Select peak", min_value=0, max_value=len(coor_list), value=0,step=1)
  165. selected_keypoint_i=ss.position
  167. st.sidebar.title("Trace calculation parameters")
  168. extraction_radius = st.sidebar.slider("Extraction radius", min_value=0.0, max_value=20.0, value=3.0,step=0.1)
  170. time_series_images = load_time_series(filename,sel_series_i,sel_channel_i)
  173. coord = (int(round(coor_list[selected_keypoint_i][0])),int(round(coor_list[selected_keypoint_i][1])))
  174. #st.write(coord)
  175. pixel_list = pixel_list_by_radius(extraction_radius)
  177. trace = []
  178. for tp in range(time_series_images.shape[2]):
  179. summation = 0
  180. for pix in pixel_list:
  181. ex_pix = (coord[0] + pix[0], coord[1] + pix[1])
  182. summation += time_series_images[ex_pix[1],ex_pix[0],tp]
  183. trace.append(summation)
  185. st.line_chart(trace)
  187. min_v = np.min(time_series_images)
  188. max_v = np.max(time_series_images)
  190. plot_points = np.linspace(0,time_series_images.shape[2]-1,num = 10,dtype=int)
  192. image_to = np.zeros((30,31*10))
  193. for i, timepoint in enumerate(plot_points):
  194. image = bytescale(time_series_images[coord[1]-15:coord[1]+15,coord[0]-15:coord[0]+15,timepoint],min_v,max_v)
  195. image_to[0:30,31*i:31 * (i+1) -1] = image
  197. #plt.imshow(image_to)
  198. #st.pyplot()
  199. plt.clf()
  200. st.write(coor_list[selected_keypoint_i])
  202. # Show image with picked peaks
  204. show_image=st.button('SHOW IMAGE')
  205. if show_image:
  206. # Create figure
  207. fig = go.Figure()
  209. # Constants
  210. img_width = blur.shape[0]
  211. img_height = blur.shape[1]
  212. scale_factor = 1.0
  214. # Add invisible scatter trace.
  215. # This trace is added to help the autoresize logic work.
  216. fig.add_trace(
  217. go.Scatter(
  218. x=[0, img_width * scale_factor],
  219. y=[0, img_height * scale_factor],
  220. mode="markers",
  221. marker_opacity=0
  222. )
  223. )
  225. # Configure axes
  226. fig.update_xaxes(
  227. visible=False,
  228. range=[0, img_width * scale_factor]
  229. )
  231. fig.update_yaxes(
  232. visible=False,
  233. range=[0, img_height * scale_factor],
  234. # the scaleanchor attribute ensures that the aspect ratio stays constant
  235. scaleanchor="x"
  236. )
  238. fig.add_trace(
  239. go.Heatmap(
  240. z=blur,
  241. showscale=False,
  242. colorscale='Greys',
  243. reversescale=True
  244. )
  245. )
  246. # Add image
  247. #fig.update_layout(
  248. # images=[go.layout.Image(
  249. # x=0,
  250. # sizex=img_width * scale_factor,
  251. # y=img_height * scale_factor,
  252. # sizey=img_height * scale_factor,
  253. # xref="x",
  254. # yref="y",
  255. # opacity=1.0,
  256. # layer="below",
  257. # sizing="stretch",
  258. # source=blur)]
  259. #)
  261. # Configure other layout
  262. fig.update_layout(
  263. width=img_width * scale_factor,
  264. height=img_height * scale_factor,
  265. margin={"l": 0, "r": 0, "t": 0, "b": 0},
  266. )
  268. shapes = [ go.layout.Shape(
  269. type="rect",
  270. xref="x",
  271. yref="y",
  272. x0=point[0]-2,
  273. y0=point[1]-2,
  274. x1=point[0]+2,
  275. y1=point[1]+2,
  276. line=dict(
  277. color="yellow",
  278. width=3,
  279. )
  280. ) for point in coor_list]
  281. shapes[selected_keypoint_i]["line"]["color"] = "red"
  282. fig.update_layout(
  283. shapes=shapes)
  284. # Disable the autosize on double click because it adds unwanted margins around the image
  285. # More detail: https://plot.ly/python/configuration-options/
  286. st.plotly_chart(fig)
  287. #fig.show(config={'doubleClick': 'reset'})

BleachProcess.py, under CC-BY-4.0 · at the source

Overview

Authors: Junhoe Kim1, Avery J Benton2, James S Lotti2, Nirvan Rouzbeh2, Kasper B Hansen2, Eric Gouaux1,3
ORCID iDs: Junhoe Kim, Avery J Benton, James S Lotti, Kasper B Hansen, Eric Gouaux
  1. Vollum Institute, Oregon Health and Science University, Portland, OR USA
  2. Center for Structural and Functional Neuroscience, Center for Biomolecular Structure and Dynamics, Division of Biological and Biomedical Sciences, University of Montana, Missoula, MT USA
  3. Howard Hughes Medical Institute, Oregon Health and Science University, Portland, OR USA
Institutions: Oregon Health & Science University (United States); Vollum Institute (United States); University of Montana (United States); Howard Hughes Medical Institute (United States)
Journal: Nature structural & molecular biology, volume 33, issue 9, pages 1299-1310
Dates: received 12 December 2025; accepted 23 July 2026; published online 28 August 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41594-026-01866-9 · PMID 42665662 · PMCID PMC13561882 · OpenAlex W7204557669
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: histology / microscopy (modality), human (organism), cellular / molecular (subfield)
Methods: Connectivity, Preprocessing, Evoked potentials, fMRI & imaging
Keywords: Electron microscopy, Structural biology, Ligand-gated ion channels, Ion channels
MeSH: Receptors, N-Methyl-D-Aspartate*, Allosteric Regulation, Animals, Cryoelectron Microscopy, Glycine, Humans, Ion Channel Gating, Models, Molecular, Protein Conformation (* major topic)
Topic: Neuroscience and Neuropharmacology Research (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: NIGMS NIH HHS (P30 GM140963, R24 GM154185); NINDS NIH HHS (R01 NS116055, R01 NS097536, R01 NS038631); NIH HHS (S10 OD034224)
Citations: not cited yet (Europe PMC); 82 references in the paper

Abstract

N-methyl-d-aspartate receptors (NMDARs) mediate excitatory signaling essential for synaptic plasticity and memory. Unlike GluN2-containing NMDARs, GluN3-containing receptors are activated solely by glycine and exhibit profound desensitization and paradoxical potentiation by GluN1-selective antagonists, including CGP-78608 (CGP). Although GluN3A-containing NMDARs regulate synapse pruning and excitotoxicity, and are associated with schizophrenia, autism and stroke, their native stoichiometry and gating mechanism are poorly defined. Here, using single-molecule pull-down analysis, we show that native GluN3A-containing receptors are diheteromeric assemblies. Cryogenic-electron microscopy analysis of GluN1–GluN3A receptors in antagonist-bound, preactive, active and desensitized states, augmented by electrophysiology and pharmacology experiments, show how glycine activates the receptor solely via GluN3A-dependent conformational changes, opening the gate with two-fold symmetry, and induces a roughly four-fold symmetric desensitized state. CGP-bound GluN1 restricts GluN3A rotation, promoting glycine-induced activation by blocking desensitization. These findings illuminate how CGP potentiates GluN1–GluN3A receptor activity, place the receptor gating mechanism on a structural foundation and define the molecular basis for pharmacological modulation.

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

Repository

Its files are read in the Code ↔ Paper reader above.

Zenodo 8161179

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: Python (2)
Size: 2 files, 2 scripts
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (1 file), NumPy (1 file), OpenCV (1 file), Plotly (1 file), scikit-image (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
2 files
At the source:

Code availability

Custom code used for analyzing single-molecule photobleaching trajectories in this study is available via Zenodo at https://doi.org/10.5281/zenodo.8161179 (ref. 82).

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 2 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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 Statement

Data and materials can be obtained from the corresponding authors on request and are available from the following repositories: cryo-EM maps and coordinates have been deposited in the Electron Microscopy Data Bank and in the PDB under accession codes EMD-70399 (http://www.ebi.ac.uk/pdbe/entry/emdb/EMD-70399) and 6OEH (http://doi.org/10.2210/pdb6OEH/pdb) (antagonist-bound LBD–TMD), EMD-70433 (http://www.ebi.ac.uk/pdbe/entry/emdb/EMD-70433) and 9OFI (http://doi.org/10.2210/pdb9OFI/pdb) (antagonist-bound LBD), EMD-70446 (http://www.ebi.ac.uk/pdbe/entry/emdb/EMD-70446) and 9OFY (http://doi.org/10.2210/pdb9OFY/pdb) (antagonist-bound TMD), EMD-70402 (http://www.ebi.ac.uk/pdbe/entry/emdb/EMD-70402) and 9OEM (http://doi.org/10.2210/pdb9OEM/pdb) (preactive; class 1), EMD-70403 (http://www.ebi.ac.uk/pdbe/entry/emdb/EMD-70403) and 9OEN (http://doi.org/10.2210/pdb9OEN/pdb) (active; class 2), EMD-70404 (http://www.ebi.ac.uk/pdbe/entry/emdb/EMD-70404) (active 2; class 3), EMD-70405 (http://www.ebi.ac.uk/pdbe/entry/emdb/EMD-70405) and 9OEO (http://doi.org/10.2210/pdb9OEO/pdb) (desensitized LBD–TMD), EMD-70437 (http://www.ebi.ac.uk/pdbe/entry/emdb/EMD-70437) and 9OFM (http://doi.org/10.2210/pdb9OFM/pdb) (desensitized LBD) and EMD-70447 (http://www.ebi.ac.uk/pdbe/entry/emdb/EMD-70447) and 9OFZ (http://doi.org/10.2210/pdb9OFZ/pdb) (desensitized TMD), respectively. Source data are provided with this paper.

Custom code used for analyzing single-molecule photobleaching trajectories in this study is available via Zenodo at https://doi.org/10.5281/zenodo.8161179 (ref. 82).

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 2, 28 September 2026

  • Publisher: n/a → Nature Portfolio

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 4 keywords, 9 MeSH terms, 3 funders, 82 references.

Cite

This paper

Kim, J., Benton, A. J., Lotti, J. S., Rouzbeh, N., Hansen, K. B., & Gouaux, E. (2026). Structural and mechanistic insights into gating and allosteric modulation of GluN1-GluN3A NMDA receptors. Nature structural & molecular biology, 33(9), 1299-1310. https://doi.org/10.1038/s41594-026-01866-9

BibTeX

@article{kim2026structural,
author = {Kim, Junhoe and Benton, Avery J and Lotti, James S and Rouzbeh, Nirvan and Hansen, Kasper B and Gouaux, Eric},
title = {{Structural and mechanistic insights into gating and allosteric modulation of GluN1-GluN3A NMDA receptors}},
journal = {Nature structural \& molecular biology},
year = {2026},
month = aug,
volume = {33},
number = {9},
pages = {1299--1310},
publisher = {Nature Portfolio},
issn = {1545-9993},
doi = {10.1038/s41594-026-01866-9},
url = {https://doi.org/10.1038/s41594-026-01866-9},
pmid = {42665662},
pmcid = {PMC13561882}
}

RIS

TY - JOUR
AU - Kim, Junhoe
AU - Benton, Avery J
AU - Lotti, James S
AU - Rouzbeh, Nirvan
AU - Hansen, Kasper B
AU - Gouaux, Eric
TI - Structural and mechanistic insights into gating and allosteric modulation of GluN1-GluN3A NMDA receptors
T2 - Nature structural & molecular biology
J2 - Nat Struct Mol Biol
PY - 2026
DA - 2026/08/28
VL - 33
IS - 9
SP - 1299
EP - 1310
SN - 1545-9993
PB - Nature Portfolio
DO - 10.1038/s41594-026-01866-9
UR - https://doi.org/10.1038/s41594-026-01866-9
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41594-026-01866-9",
"type": "article-journal",
"title": "Structural and mechanistic insights into gating and allosteric modulation of GluN1-GluN3A NMDA receptors",
"container-title": "Nature structural & molecular biology",
"author": [
{
"family": "Kim",
"given": "Junhoe"
},
{
"family": "Benton",
"given": "Avery J"
},
{
"family": "Lotti",
"given": "James S"
},
{
"family": "Rouzbeh",
"given": "Nirvan"
},
{
"family": "Hansen",
"given": "Kasper B"
},
{
"family": "Gouaux",
"given": "Eric"
}
],
"container-title-short": "Nat Struct Mol Biol",
"volume": "33",
"issue": "9",
"page": "1299-1310",
"DOI": "10.1038/s41594-026-01866-9",
"PMID": "42665662",
"PMCID": "PMC13561882",
"ISSN": "1545-9993",
"publisher": "Nature Portfolio",
"URL": "https://doi.org/10.1038/s41594-026-01866-9",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
28
]
]
}
}

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