Microtubules in the axon are GDP bound but adopt a stable GTP-like expanded state.

Code ↔ Paper

2 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 2 matches

[1] § Methods › Single-particle cryo-EM image processing ↔ mask3D_cyl.py, lines 9 to 39 · score 0.63 · outer diameter, inner diameter, cyl, helical, mrc, command · Report a mismatch
[2] § Methods › Single-particle cryo-EM image processing ↔ MTSeamSym.py, lines 97 to 186 · score 0.58 · neighboring, seam, max, outer, inner, rotation · Report a mismatch

Paper

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The paper is loaded when this pane is shown.

The paper is loaded by your browser, with JavaScript: read it at doi.org or on Europe PMC.

The authors' code

Python · 107 lines · 3.2 KB · no license · 1 match

#!/usr/bin/env python
import optparse
import os,sys
from EMAN2 import *
import math
#===============
def setupParserOptions():
parser = optparse.OptionParser()
parser.set_usage("""%prog -f helical.mrc --DO --DI --DZ --boxsize --apix
to apply a soft mask to both ends of a 3D helical volume (z direction),
together with a soft inner and outer mask (x-y plane)""")
parser.add_option("-f", dest="mrc", type="string", metavar="FILE",
help="3D helical.mrc file to be masked. If not specified, will only output mask3D.mrc")
parser.add_option("--DO", dest="DO", type="int", metavar="INT",
help="outer diameter in x-y plane, in angstrom")
parser.add_option("--DI", dest="DI", type="int", metavar="INT", default=0,
help="inner diameter in x-y plane, in angstrom, default=0")
parser.add_option("--DZ", dest="DZ", type="float", metavar="FLOAT",
help="diameter in z direction, if >1, in angstrom; if <1, in percentage")
parser.add_option("--boxsize", dest="boxsize", type="int", metavar="INT",
help="boxsize")
parser.add_option("--apix", dest="apix", type="float", metavar="FLOAT",
help="apix")
options,args = parser.parse_args()
if len(args) > 0:
parser.error("Unknown commandline options: " +str(args))
if len(sys.argv) < 2:
parser.print_help()
parser.error("no options defined")
params = {}
for i in parser.option_list:
if isinstance(i.dest, str):
params[i.dest] = getattr(options, i.dest)
return params
# Generate 2D slices to be inserted into mask3D volume
def createMask2D(params):
from itertools import product
apix = params['apix']
RO = float(params['DO'])/apix/2
RI = float(params['DI'])/apix/2
if params['boxsize']:
nx = int(params['boxsize'])
else:
vol = EMData(params['mrc'])
nx = vol.get_xsize()
falloff_r = 6 # use steeper falloff
mask2D = EMData(nx,nx)
mask2D.to_one()
for x,y in product(range(nx),range(nx)):
dx = abs(x-nx/2)
dy = abs(y-nx/2)
r2 = dx**2+dy**2
if r2 > RO*RO:
wt1 = 0.5*(1 + math.cos(math.pi*min(1,(math.sqrt(r2)-RO)/falloff_r)))
mask2D.set(x,y,wt1)
elif r2 < RI*RI:
wt2 = 0.5*(1 + math.cos(math.pi*min(1,(RI-math.sqrt(r2))/falloff_r)))
mask2D.set(x,y,wt2)
#mask2D.write_image('mask.mrc')
return mask2D
def createMask3D(params,mask2D):
apix = params['apix']
RO = float(params['DO'])/apix/2
RI = float(params['DI'])/apix/2
nx = mask2D.get_xsize()
if params['DZ'] > 1.0:
RZ = float(params['DZ'])/apix/2
else:
RZ = (nx/2*params['DZ'])
mask3D = EMData(nx,nx,nx)
falloff_z = 6.0
# now apply soft mask
for z in range(nx):
img = EMData(nx,nx)
img = mask2D.copy()
# here "img = mask2D" won't work !!
dz = abs(z-nx/2)
if dz > RZ:
wt3 = 0.5*(1 + math.cos(math.pi*min(1,(dz-RZ)/falloff_z)))
img.mult(wt3)
#img.write_image("test_%d.mrc"%z)
mask3D.insert_clip(img,(0,0,z))
name = 'mask3D_%d-%dx%d_apix%.2f.mrc'%(params['DO'],params['DI'],math.ceil(RZ*apix*2),apix)
mask3D.write_image(name)
return mask3D,name
#===============
if __name__ == "__main__":
params = setupParserOptions()
mask2D = createMask2D(params)
mask3D,name = createMask3D(params,mask2D)
if params['mrc']:
vol = EMData(params['mrc'])
vol.mult(mask3D)
vol.write_image('%s_mask.mrc'%(params["mrc"][:-4]))
#os.system("bhead -origin 0,0,0 -recalculate -sampling %.3f %s %s"%(params['apix'],name,name))

mask3D_cyl.py at commit 159aa8b, no license · at the source

Overview

Authors: Elena A. Zehr¹, Shufeng Sun¹, Stephanie L. Sarbanes¹, Antonina Roll-Mecak^1,2

ORCID iDs: Antonina Roll-Mecak

Institutions: National Institute of Neurological Disorders and Stroke (United States); National Heart, Lung, and Blood Institute (United States)

Journal: Nature structural & molecular biology, volume 33, issue 4, pages 631-640

Dates: received 2 July 2025; accepted 4 March 2026; published online 8 April 2026; in print 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1038/s41594-026-01787-7 · PMID 41951886 · PMCID PMC13095656 · OpenAlex W7151889715

Open access: hybrid, a free copy (OpenAlex)

Status: code verified (how the code is found and verified)

Categories: histology / microscopy (modality), human (organism), cellular / molecular (subfield)

Methods: Connectivity

Keywords: Cryoelectron microscopy, Microtubules

MeSH: Axons*, Guanosine Diphosphate*, Guanosine Triphosphate*, Microtubules*, Cryoelectron Microscopy, Humans, Induced Pluripotent Stem Cells, Neurogenesis, Neurons, Tubulin (* major topic)

Topic: Microtubule and mitosis dynamics (Cell Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex

Citations: cited by 8 papers (Europe PMC); 89 references in the paper

Abstract

Microtubules scaffold cells, supporting signaling and cargo transport. They assemble from GTP–tubulin, which hydrolyzes to GDP–tubulin during polymerization. GTP–microtubule lattices are stable; GDP lattices depolymerize rapidly. In vitro, hydrolysis triggers lattice compaction. Lattice spacing regulates motors and microtubule-associated proteins; however, the conformation of tubulin in microtubules in cells is unknown. Here, we present the atomic-resolution cryo-electron microscopy structure of human microtubules in situ, in the axons of human cortical neurons derived from induced pluripotent stem cells (iPS cells). Our 2.7-Å-resolution reconstruction delineates bound water molecules and reveals that axonal microtubules adopt an expanded GTP-like lattice, despite being GDP bound. Using cryo-electron tomography and power spectrum analysis, we find that, unlike in axons, microtubules in undifferentiated iPS cells are compacted. Therefore, lattice expansion is part of neuronal differentiation. Our work provides molecular insights into neurogenesis and has implications for understanding microtubule stability and effector recruitment in neurons.

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 2 matches between paragraphs and lines of code.

rui--zhang/Microtubule

License: none: the authors keep all their rights

State: the link answers, verified on 29 September 2026

Evidence: files inventoried

Commit: 159aa8b8a8a62a6684ccc8a88cefb457bd2fbadf, 13 June 2025

Languages: Python (25)

Size: 32 files, 25 scripts

Software Heritage: not archived

Found in: the text, “Single-particle cryo-EM image processing”

Holds: README

Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: NumPy (15 files), Matplotlib (4 files)

Availability: 1 check, the latest on 29 September 2026: the link answers

29 September 2026: the link answers

26 files

MTSeamSym.py, Python, 477 lines, 1 match
MTSuperPtcl.py, Python, 224 lines
MTapix2_dimer.py, Python, 196 lines
MTavg_break_v9.py, Python, 342 lines
MTllcalc.py, Python, 173 lines
MTllplot.py, Python, 171 lines
MTparGetPhilist.py, Python, 197 lines
MTparGreedy_new3.py, Python, 256 lines
MTparRotPF.py, Python, 111 lines
MTparSamePhi2.py, Python, 369 lines
MTparShift40A.py, Python, 94 lines
MTparUniPhi.py, Python, 359 lines
MTpar_selDpres.py, Python, 90 lines
MTrotPF_v9.py, Python, 125 lines
MTseam_calDPRES.py, Python, 321 lines
MTshift40A_v9.py, Python, 93 lines
MTss_calPRES.py, Python, 327 lines
MTss_plotDPRES_MT.py, Python, 185 lines
fre2relion_conv_v2_rel31_cs_cutoff_NEW2.py, Python, 121 lines
freali-runpar-v9.py, Python, 42 lines
mask3D_cyl.py, Python, 107 lines, 1 match
mtFSC.py, Python, 223 lines
mtSeamSym.py, Python, 477 lines
plot_histo.py, Python, 68 lines
relion2freali_rel3.1_cs_MT.py, Python, 111 lines
README.md, Text, 5 lines

At the source: github.com/rui--zhang/Microtubule

RollmecakLab/Layer-Line-Analysis

License: none: the authors keep all their rights

State: the link answers, verified on 29 September 2026

Evidence: files inventoried

Commit: e24db88aa95bd28849f30bcc67bc30f2a8979a5a, 19 April 2026

Languages: Python (3)

Size: 9 files, 3 scripts

Software Heritage: not archived

Found in: the text, “Tomography image processing and layer-line analy”

Holds: README

Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: NumPy (2 files), Matplotlib (1 file), Pillow (1 file), scikit-learn (1 file)

Availability: 1 check, the latest on 29 September 2026: the link answers

29 September 2026: the link answers

4 files

em2mrc.py, Python, 35 lines
fit_line.py, Python, 44 lines
sum_FFTs.py, Python, 44 lines
README.md, Text, 22 lines

At the source: github.com/RollmecakLab/Layer-Line-Analysis

bioinformatics.ccr.cancer.gov/btep/classes

License: none: the authors keep all their rights

State: the link answers, verified on 29 September 2026

Evidence: the link answers

Software Heritage: not checked

Found in: the text, “Tomography image processing and layer-line analy”

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)

29 September 2026: the link answers (HTTP 200)

At the source: bioinformatics.ccr.cancer.gov/btep/classes/chirp-chatbot-for-intramural-research-program

Code availability

Scripts used for power spectrum analysis are feely available on GitHub (https://github.com/RollmecakLab/Layer-Line-Analysis).

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:

3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
28 scripts, each with its path and the digest of its content;
2 matches 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

ebi.ac.uk/pdbe/emdb, at EMBL-EBI; found in “Data availability”
ebi.ac.uk/pdbe/entry, at EMBL-EBI; found in “Data availability”
figshare:29546309, at figshare; found in DataCite

Data availability

Cryo-EM maps and atomic models were deposited to the EM Data Bank and PDB under accession numbers EMD-70956 (http://www.ebi.ac.uk/pdbe/entry/emdb/EMD-70956) and 9OX7 (https://doi.org/10.2210/pdb9OX7/pdb), respectively, with accompanying raw half-maps. Tomograms were deposited to the EM Public Image Archive under the following accession codes: tomograms of axons, EMPIAR-13239 (https://www.ebi.ac.uk/pdbe/emdb/empiar/entry/13239/); tomograms of iPS cells, EMPIAR-13240 (https://www.ebi.ac.uk/pdbe/emdb/empiar/entry/13240/); tomograms of in vitro assembled GDP-bound porcine brain microtubules, EMPIAR-13241 (https://www.ebi.ac.uk/pdbe/emdb/empiar/entry/13241/) and EMPIAR-13242 (https://www.ebi.ac.uk/pdbe/emdb/empiar/entry/13242/); tomograms of in vitro assembled GMPCPP-bound porcine microtubules, EMPIAR-13243 (https://www.ebi.ac.uk/pdbe/emdb/empiar/entry/13243/) and EMPIAR-13244 (https://www.ebi.ac.uk/pdbe/emdb/empiar/entry/13244/). Data and materials can be obtained from the corresponding authors upon request. Source data are provided with 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, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 2 keywords, 10 MeSH terms, 89 references.

Cite

This paper

Zehr, E. A., Sun, S., Sarbanes, S. L., & Roll-Mecak, A. (2026). Microtubules in the axon are GDP bound but adopt a stable GTP-like expanded state. Nature structural & molecular biology, 33(4), 631-640. https://doi.org/10.1038/s41594-026-01787-7

BibTeX

@article{zehr2026microtubules, author = {Zehr, Elena A. and Sun, Shufeng and Sarbanes, Stephanie L. and Roll-Mecak, Antonina}, title = {{Microtubules in the axon are GDP bound but adopt a stable GTP-like expanded state}}, journal = {Nature structural \& molecular biology}, year = {2026}, month = apr, volume = {33}, number = {4}, pages = {631--640}, publisher = {Nature Portfolio}, issn = {1545-9993}, doi = {10.1038/s41594-026-01787-7}, url = {https://doi.org/10.1038/s41594-026-01787-7}, pmid = {41951886}, pmcid = {PMC13095656} }

RIS

TY - JOUR AU - Zehr, Elena A. AU - Sun, Shufeng AU - Sarbanes, Stephanie L. AU - Roll-Mecak, Antonina TI - Microtubules in the axon are GDP bound but adopt a stable GTP-like expanded state T2 - Nature structural & molecular biology J2 - Nat Struct Mol Biol PY - 2026 DA - 2026/04/08 VL - 33 IS - 4 SP - 631 EP - 640 SN - 1545-9993 PB - Nature Portfolio DO - 10.1038/s41594-026-01787-7 UR - https://doi.org/10.1038/s41594-026-01787-7 LA - en ER -

CSL-JSON

{ "id": "10.1038/s41594-026-01787-7", "type": "article-journal", "title": "Microtubules in the axon are GDP bound but adopt a stable GTP-like expanded state", "container-title": "Nature structural & molecular biology", "author": [ { "family": "Zehr", "given": "Elena A." }, { "family": "Sun", "given": "Shufeng" }, { "family": "Sarbanes", "given": "Stephanie L." }, { "family": "Roll-Mecak", "given": "Antonina" } ], "container-title-short": "Nat Struct Mol Biol", "volume": "33", "issue": "4", "page": "631-640", "DOI": "10.1038/s41594-026-01787-7", "PMID": "41951886", "PMCID": "PMC13095656", "ISSN": "1545-9993", "publisher": "Nature Portfolio", "URL": "https://doi.org/10.1038/s41594-026-01787-7", "language": "en", "issued": { "date-parts": [ [ 2026, 4, 8 ] ] } }

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Microtubules in the axon are GDP bound but adopt a stable GTP-like expanded state.

Code ↔ Paper

Paper

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Overview

Abstract

Repositories

rui--zhang/Microtubule

RollmecakLab/Layer-Line-Analysis

bioinformatics.ccr.cancer.gov/btep/classes

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Data

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