OSCR

Cargo Recognition of Nesprin-2 by the Dynein Adapter Bicaudal D2 for a Nuclear Positioning Pathway That Is Important for Brain Development.

Code ↔ Paper

3 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 3 matches
  1. [1] § Materials and Methods › Structure Predictions ↔ beta/RoseTTAFold.ipynb, lines 1–125 · score 0.57 · ColabFold, Google Colab, structure predictions, ID, Notebook, template
  2. [2] § Materials and Methods › Structure Predictions ↔ colabfold/citations.py, lines 1–60 · score 0.57 · ColabFold, structure predictions, AlphaFold, server, molecules, sequence
  3. [3] § Results › A Structural Model of the Minimal Nesprin-2/BicD2 Complex with a PAE Score in the High Confidence Range Was Obtained Using AlphaFold ↔ beta/AlphaFold2_advanced.ipynb, lines 231–299 · score 0.54 · ranking models, pLDDT, predicted structure, metric, alpha, residue

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Jupyter notebook · 300 lines · 11 KB · MIT · 1 match

  1. # %% [markdown]
  2. # <a href="https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/beta/RoseTTAFold.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
  4. # %% [markdown]
  5. # # RoseTTAFold w/ PyRosetta
  6. #
  7. # **Limitations**
  8. # - This notebook disables a few aspects (templates) of the full rosettafold pipeline.
  9. # - For best resuls use the [full pipeline](https://github.com/RosettaCommons/RoseTTAFold) or [Robetta webserver](https://robetta.bakerlab.org/)!
  10. # - For a typical Google-Colab session, with a `16G-GPU`, the max total length is **700 residues**. Sometimes a `12G-GPU` is assigned, in which case the max length is lower.
  11. #
  12. # For other related notebooks see [ColabFold](https://github.com/sokrypton/ColabFold)
  14. # %%
  15. #@title ##Install and import libraries
  16. if "PAPERMILL_INPUT_PATH" not in dir():
  17. #@markdown This step will take 2+ mins (6min PyRosetta, 2min RoseTTAFold)
  18. use_pyrosetta = False #@param {type:"boolean"}
  19. #@markdown - Use PyRosetta for structure prediction. To do so you'll need to get a [PyRosetta License](https://els2.comotion.uw.edu/product/pyrosetta) (free for academic use).
  20. #@markdown Once you obtain license, enter the username and password below.
  21. username = '' #@param {type:"string"}
  22. username.strip().lower()
  23. password = '' #@param {type:"string"}
  25. import os
  26. import subprocess
  27. import hashlib
  29. import sys
  30. from IPython.utils import io
  31. try:
  32. from google.colab import files
  33. except:
  34. from IPython.core.magic import register_line_magic
  35. @register_line_magic
  36. def tensorflow_version(line):
  37. pass
  38. pass
  40. if use_pyrosetta:
  41. # Thanks Matteo Ferla for password check
  42. hashed_username = hashlib.sha256(username.encode()).hexdigest()
  43. hashed_password = hashlib.sha256(password.encode()).hexdigest()
  44. expected_hashed_username = 'cf6f296b8145262b22721e52e2edec13ce57af8c6fc990c8ae1a4aa3e50ae40e'
  45. expected_hashed_password = '45066dd976d8bf0c05dc8dd4d58727945c3437e6eb361ba9870097968db7a0da'
  46. msg = 'Error: username or password is incorrect.'
  47. assert hashed_username == expected_hashed_username, msg
  48. assert hashed_password == expected_hashed_password, msg
  50. dist = subprocess.check_output(['lsb_release', '-is']).strip()
  51. if dist == "Ubuntu":
  52. dist = "ubuntu"
  53. else:
  54. dist = "linux"
  55. pyrosetta_path = f"PyRosetta4.Release.python37.{dist}.release-306"
  56. if not os.path.isdir(pyrosetta_path):
  57. print("installing pyRosetta - 6 mins")
  58. with io.capture_output() as captured:
  59. !wget --show-progress --user {username} --password {password} https://graylab.jhu.edu/download/PyRosetta4/archive/release/PyRosetta4.Release.python37.{dist}/{pyrosetta_path}.tar.bz2 -O - | tar -xj
  60. !pip install -e {pyrosetta_path}/setup/
  62. if not os.path.isdir("RoseTTAFold"):
  63. print("installing RoseTTAFold - 2 mins")
  64. with io.capture_output() as captured:
  65. # extra functionality
  66. !wget -qnc https://raw.githubusercontent.com/sokrypton/ColabFold/main/beta/colabfold.py
  68. # download model
  69. !git clone https://github.com/RosettaCommons/RoseTTAFold.git
  70. !wget -qnc https://raw.githubusercontent.com/sokrypton/ColabFold/main/beta/RoseTTAFold__network__Refine_module.patch
  71. !patch -u RoseTTAFold/network/Refine_module.py -i RoseTTAFold__network__Refine_module.patch
  73. # download model params
  74. !wget -qnc https://files.ipd.uw.edu/pub/RoseTTAFold/weights.tar.gz
  75. !tar -xf weights.tar.gz
  76. !rm weights.tar.gz
  78. # download scwrl4 (for adding sidechains)
  79. # http://dunbrack.fccc.edu/SCWRL3.php
  80. # Thanks Roland Dunbrack!
  81. !wget -qnc https://files.ipd.uw.edu/krypton/TrRosetta/scwrl4.zip
  82. !unzip -qqo scwrl4.zip
  84. # install libraries
  85. !pip install -q dgl-cu113 -f https://data.dgl.ai/wheels/repo.html
  86. !pip install -q torch-scatter -f https://pytorch-geometric.com/whl/torch-1.11.0+cu113.html
  87. !pip install -q torch-sparse -f https://pytorch-geometric.com/whl/torch-1.11.0+cu113.html
  88. !pip install -q torch-geometric
  89. !pip install -q py3Dmol
  91. !wget https://openstructure.org/static/lddt-linux.zip -O lddt.zip
  92. !unzip -d ./RoseTTAFold/lddt -j lddt.zip
  94. with io.capture_output() as captured:
  95. sys.path.append('./RoseTTAFold/network')
  96. import predict_e2e, predict_pyRosetta
  97. from parsers import parse_a3m
  99. import colabfold as cf
  100. import py3Dmol
  101. import numpy as np
  102. import matplotlib.pyplot as plt
  104. def get_bfactor(pdb_filename):
  105. bfac = []
  106. for line in open(pdb_filename,"r"):
  107. if line[:4] == "ATOM":
  108. bfac.append(float(line[60:66]))
  109. return np.array(bfac)
  111. def set_bfactor(pdb_filename, bfac):
  112. I = open(pdb_filename,"r").readlines()
  113. O = open(pdb_filename,"w")
  114. for line in I:
  115. if line[0:6] == "ATOM ":
  116. seq_id = int(line[22:26].strip()) - 1
  117. O.write(f"{line[:60]}{bfac[seq_id]:6.2f}{line[66:]}")
  118. O.close()
  120. def do_scwrl(inputs, outputs, exe="./scwrl4/Scwrl4"):
  121. subprocess.run([exe,"-i",inputs,"-o",outputs,"-h"],
  122. stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
  123. bfact = get_bfactor(inputs)
  124. set_bfactor(outputs, bfact)
  125. return bfact
  127. # %%
  128. #@markdown ##Input Sequence
  129. if "PAPERMILL_INPUT_PATH" not in dir():
  130. sequence = "PIAQIHILEGRSDEQKETLIREVSEAISRSLDAPLTSVRVIITEMAKGHFGIGGELASK" #@param {type:"string"}
  131. jobname = "test" #@param {type:"string"}
  133. sequence = sequence.translate(str.maketrans('', '', ' \n\t')).upper()
  134. jobname = jobname+"_"+cf.get_hash(sequence)[:5]
  136. # %%
  137. #@title Search against genetic databases
  138. if "PAPERMILL_INPUT_PATH" not in dir():
  139. #@markdown ---
  140. msa_method = "mmseqs2" #@param ["mmseqs2","single_sequence","custom_a3m"]
  141. #@markdown - `mmseqs2` - FAST method from [ColabFold](https://github.com/sokrypton/ColabFold)
  142. #@markdown - `single_sequence` - use single sequence input (not recommended, unless a *denovo* design and you dont expect to find any homologous sequences)
  143. #@markdown - `custom_a3m` Upload custom MSA (a3m format)
  145. # tmp directory
  146. prefix = cf.get_hash(sequence)
  147. os.makedirs('tmp', exist_ok=True)
  148. prefix = os.path.join('tmp',prefix)
  150. os.makedirs(jobname, exist_ok=True)
  153. if msa_method == "mmseqs2":
  154. if "PAPERMILL_INPUT_PATH" not in dir():
  155. host_url="https://a3m.mmseqs.com"
  156. a3m_lines = cf.run_mmseqs2(sequence, prefix, filter=True, host_url=host_url)
  157. with open(f"{jobname}/msa.a3m","w") as a3m:
  158. a3m.write(a3m_lines)
  160. elif msa_method == "single_sequence":
  161. with open(f"{jobname}/msa.a3m","w") as a3m:
  162. a3m.write(f">{jobname}\n{sequence}\n")
  164. elif msa_method == "custom_a3m":
  165. if "PAPERMILL_INPUT_PATH" not in dir():
  166. print("upload custom a3m")
  167. msa_dict = files.upload()
  168. lines = msa_dict[list(msa_dict.keys())[0]].decode().splitlines()
  169. a3m_lines = []
  170. for line in lines:
  171. line = line.replace("\x00","")
  172. if len(line) > 0 and not line.startswith('#'):
  173. a3m_lines.append(line)
  174. with open(f"{jobname}/msa.a3m","w") as a3m:
  175. a3m.write("\n".join(a3m_lines))
  176. else:
  177. import shutil
  178. shutil.copy(a3m_file, f"{jobname}/msa.a3m")
  180. msa_all = parse_a3m(f"{jobname}/msa.a3m")
  181. msa_arr = np.unique(msa_all,axis=0)
  182. total_msa_size = len(msa_arr)
  183. if msa_method == "mmseqs2":
  184. print(f'\n{total_msa_size} Sequences Found in Total (after filtering)\n')
  185. else:
  186. print(f'\n{total_msa_size} Sequences Found in Total\n')
  188. if total_msa_size > 1:
  189. plt.figure(figsize=(8,5),dpi=100)
  190. plt.title("Sequence coverage")
  191. seqid = (msa_all[0] == msa_arr).mean(-1)
  192. seqid_sort = seqid.argsort()
  193. non_gaps = (msa_arr != 20).astype(float)
  194. non_gaps[non_gaps == 0] = np.nan
  195. plt.imshow(non_gaps[seqid_sort]*seqid[seqid_sort,None],
  196. interpolation='nearest', aspect='auto',
  197. cmap="rainbow_r", vmin=0, vmax=1, origin='lower',
  198. extent=(0, msa_arr.shape[1], 0, msa_arr.shape[0]))
  199. plt.plot((msa_arr != 20).sum(0), color='black')
  200. plt.xlim(0,msa_arr.shape[1])
  201. plt.ylim(0,msa_arr.shape[0])
  202. plt.colorbar(label="Sequence identity to query",)
  203. plt.xlabel("Positions")
  204. plt.ylabel("Sequences")
  205. plt.savefig(f"{jobname}/msa_coverage.png", bbox_inches = 'tight')
  206. plt.show()
  208. # %%
  209. %tensorflow_version 1.x
  210. #@title ## Run RoseTTAFold
  211. # load model
  212. if "rosettafold" not in dir():
  213. if use_pyrosetta:
  214. rosettafold = predict_pyRosetta.Predictor(model_dir="weights")
  215. else:
  216. rosettafold = predict_e2e.Predictor(model_dir="weights")
  218. # make prediction using model
  219. if use_pyrosetta:
  220. if not os.path.isfile(f"{jobname}/pred.npz"):
  221. print("running RoseTTAFold")
  222. rosettafold.predict(f"{jobname}/msa.a3m",f"{jobname}/pred")
  223. else:
  224. if not os.path.isfile(f"{jobname}/pred.pdb"):
  225. print("running RoseTTAFold")
  226. rosettafold.predict(f"{jobname}/msa.a3m",f"{jobname}/pred")
  228. if not os.path.isfile(f"{jobname}/pred.fasta"):
  229. with open(f"{jobname}/pred.fasta","w") as out:
  230. out.write(f">pred\n{sequence}\n")
  232. if use_pyrosetta:
  233. CPU = max(1, len(os.sched_getaffinity(0)))
  234. pyrosetta_script = "./RoseTTAFold/folding/RosettaTR.py"
  235. if not os.path.isfile(f"{jobname}/model/model_1.crderr.pdb"):
  236. print("running PyRosetta")
  237. with open(f"{jobname}/job_list","w") as job_list:
  238. for m in [0,1,2]:
  239. for p in [0.05,0.15,0.25,0.35,0.45]:
  240. pdb_out = f"{jobname}/model_{m}_{p}.pdb"
  241. opt = f"--roll -r 3 -pd {p} -m {m} -sg 7,3"
  242. if not os.path.isfile(pdb_out):
  243. job_list.write(f"python -u {pyrosetta_script} {opt} {jobname}/pred.npz {jobname}/pred.fasta {pdb_out}\n")
  244. os.system(f"cat {jobname}/job_list | tr '\\n' '\\0' | xargs -0 -L 1 -P {CPU} -I % sh -c '%'")
  246. print("ranking models using DAN")
  247. dan_script = "./RoseTTAFold/DAN-msa/ErrorPredictorMSA.py"
  248. dan_pick_script = "./RoseTTAFold/DAN-msa/pick_final_models.div.py"
  249. os.system(f"python {dan_script} --roll -p {CPU} {jobname}/pred.npz {jobname} {jobname}")
  250. os.system(f"python {dan_pick_script} {jobname} {jobname}/model {CPU}")
  252. plddt = get_bfactor(f"{jobname}/model/model_1.crderr.pdb")
  254. else:
  255. # pack sidechains using Scwrl4
  256. plddt = do_scwrl(f"{jobname}/pred.pdb",f"{jobname}/pred.scwrl.pdb")
  258. print(f"Predicted LDDT: {plddt.mean()}")
  259. plt.figure(figsize=(8,5),dpi=100)
  260. plt.plot(plddt)
  261. plt.xlabel("positions")
  262. plt.ylabel("plddt")
  263. plt.ylim(0,1)
  264. plt.savefig(f"{jobname}/plddt.png", bbox_inches = 'tight')
  265. plt.show()
  267. # %%
  268. #@title Display 3D structure {run: "auto"}
  269. color = "lDDT" #@param ["chain", "lDDT", "rainbow"]
  270. show_sidechains = False #@param {type:"boolean"}
  271. show_mainchains = False #@param {type:"boolean"}
  272. if use_pyrosetta:
  273. cf.show_pdb(f"{jobname}/model/model_1.crderr.pdb", show_sidechains, show_mainchains, color, chains=1, vmin=0.5, vmax=0.9).show()
  274. else:
  275. cf.show_pdb(f"{jobname}/pred.scwrl.pdb", show_sidechains, show_mainchains, color, chains=1, vmin=0.5, vmax=0.9).show()
  277. if color == "lDDT": cf.plot_plddt_legend().show()
  279. # %%
  280. #@title Download prediction
  282. #@markdown Once this cell has been executed, a zip-archive with
  283. #@markdown the obtained prediction will be automatically downloaded
  284. #@markdown to your computer.
  286. # add settings file
  287. settings_path = f"{jobname}/settings.txt"
  288. with open(settings_path, "w") as text_file:
  289. text_file.write(f"method=RoseTTAFold\n")
  290. text_file.write(f"sequence={sequence}\n")
  291. text_file.write(f"msa_method={msa_method}\n")
  292. text_file.write(f"use_templates=False\n")
  294. # --- Download the predictions ---
  295. !zip -q -r {jobname}.zip {jobname}
  296. try:
  297. files.download(f'{jobname}.zip')
  298. except:
  299. pass

RoseTTAFold.ipynb at commit efbf31c, under MIT · at the source

Overview

Authors: Estrella D Rodriguez Castro1, Sivasankar Putta1, M Yusuf Ali2, Jose M Garcia Martin1, Xiaoxin Zhao1, Samantha Sylvain1, Kathleen M Trybus2, Sozanne R Solmaz1
ORCID iDs: Sozanne R Solmaz
  1. Department of Chemistry, Binghamton University, PO Box 6000, Binghamton, New York 13902, United States
  2. Department of Molecular Physiology and Biophysics, University of Vermont, Burlington, Vermont 05405, United States
Institutions: Binghamton University (United States); University of Vermont (United States)
Journal: Biochemistry, volume 65, issue 6, pages 717-731
Dates: received 24 September 2025; accepted 26 January 2026; published online 2 March 2026; in print March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1021/acs.biochem.5c00596 · PMID 41770881 · PMCID PMC13001094 · OpenAlex W7133240202
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), cellular / molecular (subfield)
Methods: Statistics, Evoked potentials, Graphs
MeSH: Brain*, Cell Nucleus*, Microfilament Proteins*, Microtubule-Associated Proteins*, Nerve Tissue Proteins*, Nuclear Proteins*, Animals, Binding Sites, Dyneins, Humans, Models, Molecular, Protein Binding (* major topic)
Topic: Nuclear Structure and Function (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: National Institute of General Medical Sciences (R01 GM144578, R35 GM136288, 1R01GM125853-02S1, 3R35GM130207-01S1); NIGMS NIH HHS (R01 GM144578, R35 GM136288); NINDS NIH HHS (R03 NS126811); National Institute of Neurological Disorders and Stroke (R03 NS126811-01A1)
Citations: not cited yet (Europe PMC); 73 references in the paper

Abstract

Nesprin-2 and its paralog Nesprin-1 are subunits of LINC complexes that are essential for brain development. To position the nucleus for neuronal migration, Nesprin-2 interacts with the motors kinesin-1 and dynein, which are recruited by the adapter Bicaudal D2 (BicD2), but the molecular details of these interactions are elusive. Here, structural models of minimal Nesprin-2/BicD2 complexes with 1:2 and 2:2 stoichiometry were predicted using AlphaFold and experimentally validated by mutagenesis, binding assays, and single-molecule biophysical studies. The core of the binding site is formed by spectrin repeats of Nesprin-2, which form an α-helical bundle with BicD2 that is structurally distinct from the Rab6/BicD2 and Nup358/BicD2 complexes. Such structural differences could fine-tune the motility of associated dynein and kinesin-1 motors for these transport pathways. Furthermore, the Nesprin-2 fragment interacts with full-length BicD2 and activates dynein/dynactin/BicD2 complexes for processive motility, suggesting that no additional components are required to reconstitute this transport pathway. Interestingly, either one or two Nesprin-2 molecules can bind to a BicD2 dimer and activate BicD2/dynein/dynactin complexes for processive motion, resulting in similar speed and run lengths. The BicD2/dynein binding site is spatially close but does not overlap with the kinesin-1 recruitment site, thus both motors may interact with Nesprin-2 simultaneously. Several mutations of Nesprin-1 and 2 that cause Emery–Dreifuss muscular dystrophy are found in the motor-recruiting domain and may alter interactions with kinesin-1 and BicD2/dynein, consistent with the abnormally positioned nuclei found in patients with this disease.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

sokrypton/colabfold

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: efbf31c37cedb38cd09c69c1b991910a9866480e, 19 September 2026
Languages: Python (29), Jupyter (27), Shell (6)
Size: 122 files, 62 scripts
Software Heritage: not archived
Found in: the text, “Structure Predictions”
Holds: README, license file, environment (Dockerfile, poetry.lock, pyproject.toml), tests, continuous integration, 27 notebooks
Not found: CITATION.cff, documentation
Tools: NumPy (32 files), Matplotlib (24 files), JAX (16 files), PyTorch (8 files), Biopython (7 files), TensorFlow (5 files), SciPy (4 files), PyTorch Lightning (3 files), pandas (3 files), scikit-learn (3 files), PyTorch Geometric (1 file), RDKit (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
64 files
At the source: colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb

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;
  • 62 scripts, each with its path and the digest of its content;
  • 3 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

No dataset and no data link were found in the paper.

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 12 MeSH terms, 4 funders, 72 references.

Cite

This paper

Rodriguez Castro, E. D., Putta, S., Ali, M. Y., Garcia Martin, J. M., Zhao, X., Sylvain, S., Trybus, K. M., & Solmaz, S. R. (2026). Cargo Recognition of Nesprin-2 by the Dynein Adapter Bicaudal D2 for a Nuclear Positioning Pathway That Is Important for Brain Development. Biochemistry, 65(6), 717-731. https://doi.org/10.1021/acs.biochem.5c00596

BibTeX

@article{rodriguezcastro2026cargo,
author = {Rodriguez Castro, Estrella D and Putta, Sivasankar and Ali, M Yusuf and Garcia Martin, Jose M and Zhao, Xiaoxin and Sylvain, Samantha and Trybus, Kathleen M and Solmaz, Sozanne R},
title = {{Cargo Recognition of Nesprin-2 by the Dynein Adapter Bicaudal D2 for a Nuclear Positioning Pathway That Is Important for Brain Development}},
journal = {Biochemistry},
year = {2026},
month = mar,
volume = {65},
number = {6},
pages = {717--731},
publisher = {American Chemical Society},
issn = {0006-2960},
doi = {10.1021/acs.biochem.5c00596},
url = {https://doi.org/10.1021/acs.biochem.5c00596},
pmid = {41770881},
pmcid = {PMC13001094}
}

RIS

TY - JOUR
AU - Rodriguez Castro, Estrella D
AU - Putta, Sivasankar
AU - Ali, M Yusuf
AU - Garcia Martin, Jose M
AU - Zhao, Xiaoxin
AU - Sylvain, Samantha
AU - Trybus, Kathleen M
AU - Solmaz, Sozanne R
TI - Cargo Recognition of Nesprin-2 by the Dynein Adapter Bicaudal D2 for a Nuclear Positioning Pathway That Is Important for Brain Development
T2 - Biochemistry
J2 - Biochemistry
PY - 2026
DA - 2026/03/02
VL - 65
IS - 6
SP - 717
EP - 731
SN - 0006-2960
PB - American Chemical Society
DO - 10.1021/acs.biochem.5c00596
UR - https://doi.org/10.1021/acs.biochem.5c00596
LA - en
ER -

CSL-JSON

{
"id": "10.1021/acs.biochem.5c00596",
"type": "article-journal",
"title": "Cargo Recognition of Nesprin-2 by the Dynein Adapter Bicaudal D2 for a Nuclear Positioning Pathway That Is Important for Brain Development",
"container-title": "Biochemistry",
"author": [
{
"family": "Rodriguez Castro",
"given": "Estrella D"
},
{
"family": "Putta",
"given": "Sivasankar"
},
{
"family": "Ali",
"given": "M Yusuf"
},
{
"family": "Garcia Martin",
"given": "Jose M"
},
{
"family": "Zhao",
"given": "Xiaoxin"
},
{
"family": "Sylvain",
"given": "Samantha"
},
{
"family": "Trybus",
"given": "Kathleen M"
},
{
"family": "Solmaz",
"given": "Sozanne R"
}
],
"container-title-short": "Biochemistry",
"volume": "65",
"issue": "6",
"page": "717-731",
"DOI": "10.1021/acs.biochem.5c00596",
"PMID": "41770881",
"PMCID": "PMC13001094",
"ISSN": "0006-2960",
"publisher": "American Chemical Society",
"URL": "https://doi.org/10.1021/acs.biochem.5c00596",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
2
]
]
}
}

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