Epitope and Paratope Mapping of a SUMO-Remnant Antibody Using Cross-Linking Mass Spectrometry and Molecular Docking
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".