Epitope Screening Using Hydrogen/Deuterium Exchange Mass Spectrometry (HDX-MS): An Optimized Workflow for Accelerated 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 has 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, 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 6 candidate mAbs targeting Pertactin (PRN). Using this approach, five of the six epitopes was unambiguously determined using two HDX mixing timepoints in 24 hours total run time, corresponding to substantial decrease in the instrument time required to map a single epitope using conventional HDX workflows. Conclusion: An accelerated HDX-MS epitope screening workflow was developed. The two-timepoint ‘screening’ workflow mapped all six mAbs and generated high confidence epitopes for five of the six mAbs assayed. The substantial improvement in the rate of data collection can advance HDX-MS for higher throughput investigations supporting the ability to evaluate a broader number of mAb candidates at an earlier stage of vaccine development.
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 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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".