Suppressing allostery in epitope mapping experiments using millisecond hydrogen / deuterium exchange mass spectrometry
Bibliographic record
Abstract
Localization of the interface between the candidate antibody and its antigen target, commonly known as epitope mapping, is a critical component of the development of therapeutic monoclonal antibodies. With the recent availability of commercial automated systems, hydrogen / deuterium eXchange (HDX) is rapidly becoming the tool for mapping epitopes preferred by researchers in both industry and academia. However, this approach has a significant drawback in that it can be confounded by 'allosteric' structural and dynamic changes that result from the interaction, but occur far from the point(s) of contact. Here, we introduce a 'kinetic' millisecond HDX workflow that suppresses allosteric effects in epitope mapping experiments. The approach employs a previously introduced microfluidic apparatus that enables millisecond HDX labeling times with on-chip pepsin digestion and electrospray ionization. The 'kinetic' workflow also differs from conventional HDX-based epitope mapping in that the antibody is introduced to the antigen at the onset of HDX labeling. Using myoglobin / anti-myoglobin as a model system, we demonstrate that at short 'kinetic' workflow labeling times (i.e., 200 ms), the HDX signal is already fully developed at the 'true' epitope, but is still largely below the significance threshold at allosteric sites. Identification of the 'true' epitope is supported by computational docking predictions and allostery modeling using the rigidity transmission allostery algorithm.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".