Hydrogen-deuterium exchange mass spectrometry reveals three unique binding responses of mAbs directed to the catalytic domain of hCAIX
Bibliographic record
Abstract
Human carbonic anhydrase (hCAIX), an extracellular enzyme that catalyzes the reversible hydration of CO2, is often overexpressed in solid tumors. This enzyme is instrumental in maintaining the survival of cancer cells in a hypoxic and acidic tumor microenvironment. Absent in most normal tissues, hCAIX is a promising therapeutic target for detection and treatment of solid tumors. Screening of a library of anti-hCAIX monoclonal antibodies (mAbs) previously identified three therapeutic candidates (mAb c2C7, m4A2 and m9B6) with distinct biophysical and functional characteristics. Selective binding to the catalytic domain was confirmed by yeast surface display and isothermal calorimetry, and deeper insight into the dynamic binding profiles of these mAbs upon binding were highlighted by bottom-up hydrogen-deuterium exchange mass spectrometry (HDX-MS). Here, a conformational and allosterically silent epitope was identified for the antibody-drug conjugate candidate c2C7. Unique binding profiles are described for both inhibitory antibodies, m4A2 and m9B6. M4A2 reduces the ability of the enzyme to hydrate CO2 by steric gating at the entrance of the catalytic cavity. Conversely, m9B6 disrupts the secondary structure that is necessary for substrate binding and hydration. The synergy of these two inhibitory mechanisms is demonstrated in in vitro activity assays and HDX-MS. Finally, the ability of m4A2 to modulate extracellular pH and intracellular metabolism is reported. By highlighting three unique modes by which hCAIX can be targeted, this study demonstrates both the utility of HDX-MS as an important tool in the characterization of anti-cancer biotherapeutics, and the underlying value of CAIX as a therapeutic target.
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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 | 0.001 |
| 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".