<i>In situ</i> antibody phage display yields optimal inhibitors of integrin α11/β1
Bibliographic record
Abstract
Integrins are transmembrane multi-conformation receptors that mediate interactions with the extracellular matrix. In cancer, integrins influence metastasis, proliferation, and survival. Collagen-binding integrin-α11/β1, a marker of aggressive tumors that is involved in stroma-tumor crosstalk, may be an attractive target for anti-cancer therapeutic antibodies. We performed selections with phage-displayed synthetic antibody libraries for binding to either purified integrin-α11/β1 or in situ on live cells. The in-situ strategy yielded many diverse antibodies, and strikingly, most of these antibodies did not recognize purified integrin-α11/β1. Conversely, none of the antibodies selected for binding to purified integrin-α11/β1 were able to efficiently recognize native cell-surface antigen. Most importantly, only the in-situ selection yielded functional antibodies that were able to compete with collagen-I for binding to cell-surface integrin-α11/β1, and thus inhibited cell adhesion. In-depth characterization of a subset of in situ-derived clones as full-length immunoglobulins revealed high affinity cellular binding and inhibitory activities in the single-digit nanomolar range. Moreover, the antibodies showed high selectivity for integrin-α11/β1 with minimal cross-reactivity for close homologs. Taken together, our findings highlight the advantages of in-situ selections for generation of anti-integrin antibodies optimized for recognition and inhibition of native cell-surface proteins, and our work establishes general methods that could be extended to many other membrane proteins.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".