Computational design and immunoinformatics validation of a T cell multi-epitope vaccine targeting glioblastoma stem cells.

Code ↔ Paper

1 match 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 1 match

[1] § Methodology › Modelling, refining, and validation of the tertiary structure of the vaccine construct ↔ colabfold/citations.py, lines 1–60 · score 0.57 · AlphaFold2, protein structure, bioinformatics, acid, interactions, server

Paper

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The authors' code

Python · 160 lines · 7.1 KB · MIT · 1 match

import logging
from pathlib import Path
logger = logging.getLogger(__name__)
citations = {
"Mirdita2021": """@article{Mirdita2022,
author= {Mirdita, Milot and Schütze, Konstantin and Moriwaki, Yoshitaka and Heo, Lim and Ovchinnikov, Sergey and Steinegger, Martin },
doi = {10.1038/s41592-022-01488-1},
journal = {Nature Methods},
title = {{ColabFold: Making Protein folding accessible to all}},
year = {2022},
comment = {ColabFold including MMseqs2 MSA server}
}""",
"Mitchell2019": """@article{Mitchell2019,
author = {Mitchell, Alex L and Almeida, Alexandre and Beracochea, Martin and Boland, Miguel and Burgin, Josephine and Cochrane, Guy and Crusoe, Michael R and Kale, Varsha and Potter, Simon C and Richardson, Lorna J and Sakharova, Ekaterina and Scheremetjew, Maxim and Korobeynikov, Anton and Shlemov, Alex and Kunyavskaya, Olga and Lapidus, Alla and Finn, Robert D},
doi = {10.1093/nar/gkz1035},
journal = {Nucleic Acids Res.},
title = {{MGnify: the microbiome analysis resource in 2020}},
year = {2019},
comment = {MGnify database}
}""",
"Eastman2017": """@article{Eastman2017,
author = {Eastman, Peter and Swails, Jason and Chodera, John D. and McGibbon, Robert T. and Zhao, Yutong and Beauchamp, Kyle A. and Wang, Lee-Ping and Simmonett, Andrew C. and Harrigan, Matthew P. and Stern, Chaya D. and Wiewiora, Rafal P. and Brooks, Bernard R. and Pande, Vijay S.},
doi = {10.1371/journal.pcbi.1005659},
journal = {PLOS Comput. Biol.},
number = {7},
title = {{OpenMM 7: Rapid development of high performance algorithms for molecular dynamics}},
volume = {13},
year = {2017},
comment = {Amber relaxation}
}""",
"Jumper2021": """@article{Jumper2021,
author = {Jumper, John and Evans, Richard and Pritzel, Alexander and Green, Tim and Figurnov, Michael and Ronneberger, Olaf and Tunyasuvunakool, Kathryn and Bates, Russ and {\v{Z}}{\'{i}}dek, Augustin and Potapenko, Anna and Bridgland, Alex and Meyer, Clemens and Kohl, Simon A. A. and Ballard, Andrew J. and Cowie, Andrew and Romera-Paredes, Bernardino and Nikolov, Stanislav and Jain, Rishub and Adler, Jonas and Back, Trevor and Petersen, Stig and Reiman, David and Clancy, Ellen and Zielinski, Michal and Steinegger, Martin and Pacholska, Michalina and Berghammer, Tamas and Bodenstein, Sebastian and Silver, David and Vinyals, Oriol and Senior, Andrew W. and Kavukcuoglu, Koray and Kohli, Pushmeet and Hassabis, Demis},
doi = {10.1038/s41586-021-03819-2},
journal = {Nature},
pmid = {34265844},
title = {{Highly accurate protein structure prediction with AlphaFold.}},
year = {2021},
comment = {AlphaFold2 + BFD Database}
}""",
"Evans2021": """@article{Evans2021,
author = {Evans, Richard and O'Neill, Michael and Pritzel, Alexander and Antropova, Natasha and Senior, Andrew and Green, Tim and Zidek, Augustin and Bates, Russ and Blackwell, Sam and Yim, Jason and Ronneberger, Olaf and Bodenstein, Sebastian and Zielinski, Michal and Bridgland, Alex and Potapenko, Anna and Cowie, Andrew and Tunyasuvunakool, Kathryn and Jain, Rishub and Clancy, Ellen and Kohli, Pushmeet and Jumper, John and Hassabis, Demis},
doi = {10.1101/2021.10.04.463034v1},
journal = {bioRxiv},
title = {{Protein complex prediction with AlphaFold-Multimer}},
year = {2021},
comment = {AlphaFold2-multimer}
}""",
"Mirdita2019": """@article{Mirdita2019,
author = {Mirdita, Milot and Steinegger, Martin and S{\"{o}}ding, Johannes},
doi = {10.1093/bioinformatics/bty1057},
journal = {Bioinformatics},
number = {16},
pages = {2856--2858},
pmid = {30615063},
title = {{MMseqs2 desktop and local web server app for fast, interactive sequence searches}},
volume = {35},
year = {2019},
comment = {MMseqs2 search server}
}""",
"Steinegger2019": """@article{Steinegger2019,
author = {Steinegger, Martin and Meier, Markus and Mirdita, Milot and V{\"{o}}hringer, Harald and Haunsberger, Stephan J. and S{\"{o}}ding, Johannes},
doi = {10.1186/s12859-019-3019-7},
journal = {BMC Bioinform.},
number = {1},
pages = {473},
pmid = {31521110},
title = {{HH-suite3 for fast remote homology detection and deep protein annotation}},
volume = {20},
year = {2019},
comment = {PDB70 database}
}""",
"VanKempen2023": """@article{VanKempen2023,
author = {van Kempen, Michel and Kim, Stephanie S and Tumescheit, Charlotte and Mirdita, Milot and Lee, Jeongjae and Gilchrist, Cameron L M and S{\"{o}}ding, Johannes and Steinegger, Martin},
doi = {10.1038/s41587-023-01773-0},
journal = {Nature Biotechnology},
title = {{Fast and accurate protein structure search with Foldseek}},
year = {2023},
comment = {PDB100 database}
}""",
"Mirdita2017": """@article{Mirdita2017,
author = {Mirdita, Milot and von den Driesch, Lars and Galiez, Clovis and Martin, Maria J. and S{\"{o}}ding, Johannes and Steinegger, Martin},
doi = {10.1093/nar/gkw1081},
journal = {Nucleic Acids Res.},
number = {D1},
pages = {D170--D176},
pmid = {27899574},
title = {{Uniclust databases of clustered and deeply annotated protein sequences and alignments}},
volume = {45},
year = {2017},
comment = {Uniclust30/UniRef30 database}
}""",
"Berman2003": """@misc{Berman2003,
author = {Berman, Helen and Henrick, Kim and Nakamura, Haruki},
booktitle = {Nat. Struct. Biol.},
doi = {10.1038/nsb1203-980},
number = {12},
pages = {980},
pmid = {14634627},
title = {{Announcing the worldwide Protein Data Bank}},
volume = {10},
year = {2003},
comment = {templates downloaded from wwPDB server}
}""",
"Lee2023": """@article{Lee2023,
author = {Lee, Jae-Won and Won, Jong-Hyun and Jeon, Seonggwang and Choo, Yujin and Yeon, Yubin and Oh, Jin-Seon and Kim, Minsoo and Kim, SeonHwa and Joung, InSuk and Jang, Cheongjae and Lee, Sung Jong and Kim, Tae Hyun and Jin, Kyong Hwan and Song, Giltae and Kim, Eun-Sol and Yoo, Jejoong and Paek, Eunok and Noh, Yung-Kyun and Joo, Keehyoung},
title = "{DeepFold: enhancing protein structure prediction through optimized loss functions, improved template features, and re-optimized energy function}",
journal = {Bioinformatics},
volume = {39},
number = {12},
pages = {btad712},
year = {2023},
month = {11},
doi = {10.1093/bioinformatics/btad712},
comment = {DeepFold-v1 Model}
}
""",
}
def write_bibtex(
model: str,
use_msa: bool,
use_env: bool,
use_templates: bool,
use_amber: bool,
result_dir: Path,
bibtex_file: str = "cite.bibtex",
) -> Path:
to_cite = ["Mirdita2021"]
if model == "alphafold2_ptm" or model == "alphafold2":
to_cite += ["Jumper2021"]
if model == "deepfold_v1":
to_cite += ["Lee2023"]
if model.startswith("alphafold2_multimer"):
to_cite += ["Evans2021"]
if use_msa:
to_cite += ["Mirdita2019"]
if use_msa:
to_cite += ["Mirdita2017"]
if use_env:
to_cite += ["Mitchell2019"]
if use_templates:
to_cite += ["VanKempen2023"]
if use_templates:
to_cite += ["Steinegger2019"]
if use_templates:
to_cite += ["Berman2003"]
if use_amber:
to_cite += ["Eastman2017"]
bibtex_file = result_dir.joinpath(bibtex_file)
with bibtex_file.open("w", encoding="utf-8") as writer:
for i in to_cite:
writer.write(citations[i])
writer.write("\n")
logger.info(f"Found {len(to_cite)} citations for tools or databases")
return bibtex_file

citations.py at commit efbf31c, under MIT · at the source

Overview

Authors: Reza Salahlou^1,2, Safar Farajnia^3,2, Nasrin Bargahi², Leila Rahbarnia⁴, Elham Kamalkazemi⁵

Institutions: Tabriz University of Medical Sciences (Iran); Biotechnology Research Center (Iran)

Journal: Scientific reports, volume 16, issue 1, article 22423

Dates: received 29 December 2025; accepted 12 May 2026; published online 18 May 2026

Type: Research article · Language: English

License: CC BY-NC-ND

Identifiers: DOI 10.1038/s41598-026-53415-5 · PMID 42151414 · PMCID PMC13376771 · OpenAlex W7161552716

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: computational modeling (no new data) (modality), human (organism), other condition (population), cellular / molecular (subfield)

Methods: Machine learning, Statistics, Preprocessing

Keywords: Glioblastoma stem cells, Gliostem-MultiVax, Immunoinformatics, T-cell epitopes, Immune simulation, Molecular dynamics, Cancer, Computational biology and bioinformatics, Immunology

MeSH: Brain Neoplasms*, Cancer Vaccines*, Epitopes, T-Lymphocyte*, Glioblastoma*, Neoplastic Stem Cells*, CD4-Positive T-Lymphocytes, CD8-Positive T-Lymphocytes, Humans, Immunoinformatics, Molecular Docking Simulation, Molecular Dynamics Simulation, Protein Subunit Vaccines, Toll-Like Receptor 2 (* major topic)

Topic: vaccines and immunoinformatics approaches (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex

Funding: Drug Applied Research Center (77698)

Citations: not cited yet (Europe PMC); 128 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match 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, “Modelling, refining, and validation of the terti”

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

AlphaFold2.ipynb, Jupyter, 498 lines
AlphaFold3_of3.ipynb, Jupyter, 554 lines
BioEmu.ipynb, Jupyter, 390 lines
Boltz1.ipynb, Jupyter, 258 lines
ColabFold2_preview.ipynb, Jupyter, 666 lines
ESMFold.ipynb, Jupyter, 265 lines
MsaServer/restart-systemd.sh, Shell, 5 lines
MsaServer/setup-and-start-local.sh, Shell, 111 lines
RoseTTAFold.ipynb, Jupyter, 213 lines
RoseTTAFold2.ipynb, Jupyter, 411 lines
batch/AlphaFold2_batch.ipynb, Jupyter, 180 lines
beta/AlphaFold2_advanced.ipynb, Jupyter, 615 lines
beta/AlphaFold2_advanced_beta.ipynb, Jupyter, 7 lines
beta/AlphaFold2_advanced_old.ipynb, Jupyter, 1,021 lines
beta/AlphaFold2_complexes.ipynb, Jupyter, 514 lines
beta/AlphaFold_wJackhmmer.ipynb, Jupyter, 697 lines
beta/Alphafold_single.ipynb, Jupyter, 207 lines
beta/ESMFold.ipynb, Jupyter, 385 lines
beta/ESMFold_advanced.ipynb, Jupyter, 496 lines
beta/ESMFold_api.ipynb, Jupyter, 104 lines
beta/RoseTTAFold.ipynb, Jupyter, 300 lines
beta/RoseTTAFold_install.sh, Shell, 5 lines
beta/RoseTTAFold_run.sh, Shell, 26 lines
beta/alphafold_output_at_each_recycle.ipynb, Jupyter, 152 lines
beta/colabfold.py, Python, 711 lines
beta/colabfold_alphafold.py, Python, 821 lines
beta/convert_256_to_384_rep.ipynb, Jupyter, 39 lines
beta/omegafold.ipynb, Jupyter, 167 lines
beta/omegafold_hacks.ipynb, Jupyter, 206 lines
beta/pairmsa.py, Python, 237 lines
beta/relax_amber.ipynb, Jupyter, 154 lines
colabfold/__init__.py, Python, 1 line
colabfold/alphafold/__init__.py, Python, 1 line
colabfold/alphafold/extra_ptm.py, Python, 435 lines
colabfold/alphafold/ipsae.py, Python, 98 lines
colabfold/alphafold/models.py, Python, 277 lines
colabfold/alphafold/msa.py, Python, 44 lines
colabfold/batch.py, Python, 2,364 lines
colabfold/citations.py, Python, 160 lines, 1 match
colabfold/colabfold.py, Python, 831 lines
colabfold/download.py, Python, 140 lines
colabfold/input.py, Python, 414 lines
colabfold/mmseqs/__init__.py, Python, 1 line
colabfold/mmseqs/merge_and_split_msas.py, Python, 63 lines
colabfold/mmseqs/search.py, Python, 640 lines
colabfold/mmseqs/split_msas.py, Python, 55 lines
colabfold/pdb.py, Python, 69 lines
colabfold/plot.py, Python, 158 lines
colabfold/relax.py, Python, 108 lines
colabfold/utils.py, Python, 406 lines
colabfold_search.sh, Shell, 68 lines
setup_databases.sh, Shell, 218 lines
tests/__init__.py, Python, 1 line
tests/mock.py, Python, 230 lines
tests/reindent_ipynb.py, Python, 8 lines
tests/test_colabfold.py, Python, 510 lines
tests/test_msa.py, Python, 40 lines
tests/test_utils.py, Python, 141 lines
utils/convert_deepfold_weights.py, Python, 9 lines
utils/plot_scores.ipynb, Jupyter, 28 lines
verbose/alphafold_noTemplates_noMD.ipynb, Jupyter, 267 lines
verbose/alphafold_noTemplates_yesMD.ipynb, Jupyter, 323 lines
LICENSE, License, 21 lines
README.md, Text, 293 lines

At the source: colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb

Tracing map

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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;
1 match between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

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Data availability statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

no repository, dataset or request procedure was recognized in it

Read it in the paper: doi.org/10.1038/s41598-026-53415-5.

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 9 keywords, 13 MeSH terms, 1 funder, 125 references.

Cite

This paper

Salahlou, R., Farajnia, S., Bargahi, N., Rahbarnia, L., & Kamalkazemi, E. (2026). Computational design and immunoinformatics validation of a T cell multi-epitope vaccine targeting glioblastoma stem cells. Scientific reports, 16(1), 22423. https://doi.org/10.1038/s41598-026-53415-5

BibTeX

@article{salahlou2026computational, author = {Salahlou, Reza and Farajnia, Safar and Bargahi, Nasrin and Rahbarnia, Leila and Kamalkazemi, Elham}, title = {{Computational design and immunoinformatics validation of a T cell multi-epitope vaccine targeting glioblastoma stem cells}}, journal = {Scientific reports}, year = {2026}, month = may, volume = {16}, number = {1}, pages = {22423}, publisher = {Nature Publishing Group}, issn = {2045-2322}, doi = {10.1038/s41598-026-53415-5}, url = {https://doi.org/10.1038/s41598-026-53415-5}, pmid = {42151414}, pmcid = {PMC13376771} }

RIS

TY - JOUR AU - Salahlou, Reza AU - Farajnia, Safar AU - Bargahi, Nasrin AU - Rahbarnia, Leila AU - Kamalkazemi, Elham TI - Computational design and immunoinformatics validation of a T cell multi-epitope vaccine targeting glioblastoma stem cells T2 - Scientific reports J2 - Sci Rep PY - 2026 DA - 2026/05/18 VL - 16 IS - 1 SP - 22423 SN - 2045-2322 PB - Nature Publishing Group DO - 10.1038/s41598-026-53415-5 UR - https://doi.org/10.1038/s41598-026-53415-5 LA - en ER -

CSL-JSON

{ "id": "10.1038/s41598-026-53415-5", "type": "article-journal", "title": "Computational design and immunoinformatics validation of a T cell multi-epitope vaccine targeting glioblastoma stem cells", "container-title": "Scientific reports", "author": [ { "family": "Salahlou", "given": "Reza" }, { "family": "Farajnia", "given": "Safar" }, { "family": "Bargahi", "given": "Nasrin" }, { "family": "Rahbarnia", "given": "Leila" }, { "family": "Kamalkazemi", "given": "Elham" } ], "container-title-short": "Sci Rep", "volume": "16", "issue": "1", "page": "22423", "DOI": "10.1038/s41598-026-53415-5", "PMID": "42151414", "PMCID": "PMC13376771", "ISSN": "2045-2322", "publisher": "Nature Publishing Group", "URL": "https://doi.org/10.1038/s41598-026-53415-5", "language": "en", "issued": { "date-parts": [ [ 2026, 5, 18 ] ] } }

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