Determination of Protein Monoclonal–Antibody Epitopes by a Combination of Structural Proteomics Methods
Bibliographic record
Abstract
Structural proteomics techniques are useful for the determination of protein interaction interfaces. Each technique provides orthogonal structural information on the structure and the location of protein interaction sites. Here, we have characterized a monoclonal antibody epitope for a protein antigen by a combination of differential photoreactive surface modification (SM), cross-linking (CL), differential hydrogen-deuterium exchange (HDX), and epitope extraction/excision. We found that experimental data from different approaches agree with each other in determining the epitope of the monoclonal antibody on the protein antigens using the HIV-1 p24-mAb E complex as an illustrative example. A combination of these multiple structural proteomics approaches results in a detailed picture of the interaction of the proteins and increases confidence in the determination of the final structure of the protein interaction interface. Data are available via ProteomeXchange with identifier PXD040902.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".