Epitope screening using Hydrogen/Deuterium Exchange Mass Spectrometry (HDX‐MS): An accelerated workflow for evaluation of lead monoclonal antibodies
Bibliographic record
Abstract
BACKGROUND: Epitope mapping is an increasingly important aspect of biotherapeutic and vaccine development. Recent advances in therapeutic antibody design and production have enabled candidate mAbs to be identified at a rapidly increasing rate, resulting in a significant bottleneck in the characterization of "structural" epitopes, that are challenging to determine using existing high throughput epitope mapping tools. Here, a Hydrogen/Deuterium Exchange Mass Spectrometry (HDX-MS) epitope screening workflow was introduced that is well suited for accelerated characterization of epitopes with a common antigen. MAIN METHODS AND MAJOR RESULTS: The method is demonstrated on set of six candidate mAbs targeting Pertactin (PRN). Using this approach, five of the six epitopes were unambiguously determined using two HDX mixing timepoints in 24 h total run time, which is equivalent to the instrument time required to map a single epitope using the conventional workflow. CONCLUSION: An accelerated HDX-MS epitope screening workflow was developed. The "screening" workflow successfully characterized five (out of six attempted) novel epitopes on the PRN antigen; information that can be used to support vaccine antigenicity assays.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".