Epitope mapping of a blood–brain barrier crossing antibody targeting the cysteine-rich region of IGF1R using hydrogen-exchange mass spectrometry enabled by electrochemical reduction
Bibliographic record
Abstract
Pathologies of the central nervous system impact a significant portion of our population, and the delivery of therapeutics for effective treatment is challenging. The insulin-like growth factor-1 receptor (IGF1R) has emerged as a target for receptor-mediated transcytosis, a process by which antibodies are shuttled across the blood-brain barrier (BBB). Here, we describe the biophysical characterization of VHH-IR4, a BBB-crossing single-domain antibody (sdAb). Binding was confirmed by isothermal titration calorimetry and an epitope was highlighted by surface plasmon resonance that does not overlap with the IGF-1 binding site or other known BBB-crossing sdAbs. The epitope was mapped with a combination of linear peptide scanning and hydrogen-deuterium exchange mass spectrometry (HDX-MS). IGF1R is large and heavily disulphide bonded, and comprehensive HDX analysis was achieved only through the use of online electrochemical reduction coupled with a multiprotease approach, which identified an epitope for VHH-IR4 within the cysteine-rich region (CRR) of IGF1R spanning residues W244-G265. This is the first report of an sdAb binding the CRR. We show that VHH-IR4 inhibits ligand induced auto-phosphorylation of IGF1R and that this effect is mediated by downstream conformational effects. Our results will guide the selection of antibodies with improved trafficking and optimized IGF1R binding characteristics.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".