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Record W4407683025 · doi:10.1021/acs.jproteome.4c00717

Epitope and Paratope Mapping of a SUMO-Remnant Antibody Using Cross-Linking Mass Spectrometry and Molecular Docking

2025· article· en· W4407683025 on OpenAlexafffund
Simon Comtois‐Marotte, Éric Bonneil, Chongyang Li, Matthew J. Smith, Pierre Thibault

Bibliographic record

VenueJournal of Proteome Research · 2025
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsUniversité de MontréalInstitute for Research in Immunology and Cancer
FundersNatural Sciences and Engineering Research Council of CanadaFonds de Recherche du Québec - SantéGenome Canada
KeywordsParatopeChemistryEpitopeMass spectrometryComputational biologyDocking (animal)Epitope mappingAntibodyBiologyChromatographyGeneticsMedicine

Abstract

fetched live from OpenAlex

The small ubiquitin-like modifier (SUMO) is an important post-translational modification that regulates the function of various proteins essential for DNA damage repair, genome integrity, and cell homeostasis. To identify protein SUMOylation effectively, an enrichment step is necessary, often requiring exogenous gene expression in cells and immunoaffinity purification of SUMO-remnant peptides following tryptic digestion. Previously, an antibody was developed to enrich tryptic peptides containing the remnant NQTGG on the receptor lysine, although the specifics of the structural interaction motif remained unclear. This study integrates de novo sequencing, intact mass spectrometry, cross-linking mass spectrometry, and molecular docking to elucidate the structural interaction motifs of a SUMO-remnant antibody. Additional cross-linking experiments were performed using SUMOylated peptides and high-field asymmetric waveform ion mobility spectrometry (FAIMS) to enhance the sensitivity and confirm interactions at the paratope interface. This study establishes a robust framework for characterizing antibody–antigen interactions, offering valuable insights into the structural basis of SUMO-remnant peptide recognition.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.030
Threshold uncertainty score0.517

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.051
GPT teacher head0.423
Teacher spread0.372 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2025
Admission routes2
Has abstractyes

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