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Record W4381950620 · doi:10.1016/j.isci.2023.107205

Methionine oxidation selectively enhances T cell reactivity against a melanoma antigen

2023· article· en· W4381950620 on OpenAlexfundno aff
Gabriela Chirițoiu, Cristian V. A. Munteanu, Teodor Asvadur Şulea, Laurenţiu Spiridon, Andrei‐José Petrescu, Camilla Jandus, Pedro Romero, Ştefana M. Petrescu

Bibliographic record

VenueiScience · 2023
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsnot available
FundersOntario Ministry of Research, Innovation and ScienceUnitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si InovariiAcademia RomânaSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungCorporation for National and Community Service
KeywordsAntigenicityMethionine sulfoxideChemistryMethioninePeptideImmunosurveillanceAntigenBiochemistryMelanomaAmino acidCellBiologyCancer researchImmunology

Abstract

fetched live from OpenAlex

The impact of the peptide amino acids side-chain modifications on the immunological recognition has been scarcely explored. We investigate here the effect of methionine oxidation on the antigenicity of the melanoma immunodominant peptide 369-YMDGTMSQV-377 (YMD). Using CD8 + T cell activation assays, we found that the antigenicity of the sulfoxide form is higher when compared to the YMD peptide. This is consistent with free energy computations performed on HLA-A∗02:01/YMD/TCR complex showing that this is lowered upon oxidation, paired with a steep increase in order at atomic level. Oxidized YMD forms were identified at the melanoma cell surface by LC-MS/MS analysis. These results demonstrate that methionine oxidation in the antigenic peptides may generate altered peptide ligands with increased antigenicity, and that this oxidation may occur in vivo , opening up the possibility that high-affinity CD8 + T cells might be naturally primed in the course of melanoma progression, as a result of immunosurveillance.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.265
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2023
Admission routes1
Has abstractyes

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