A Single‐Framework Synthetic Antibody Library Containing a Combination of Canonical and Variable Complementarity‐Determining Regions
Bibliographic record
Abstract
Abstract Synthetic antibody libraries have been used to generate antibodies with favorable biophysical and pharmacological properties. Here, we describe the design, construction, and validation of a phage‐displayed antigen‐binding fragment (Fab) library built on a modified trastuzumab framework with four fixed and two diversified complementarity‐determining regions (CDRs). CDRs L1, L2, H1, and H2 were fixed to preserve the most commonly observed “canonical” CDR conformation preferred by the modified trastuzumab Fab framework. The library diversity was engineered within CDRs L3 and H3 by use of custom‐designed trinucleotide phosphoramidite mixes and biased towards human antibody CDR sequences. The library contained ≈7.6 billion unique Fabs, and >95 % of the library correctly encoded both diversified CDR sequences. We used this library to conduct selections against the human epidermal growth factor receptor‐3 extracellular domain (HER3‐ECD) and compared the CDR diversity of the naïve library and the anti‐HER3 selection pool by use of next‐generation sequencing. The most commonly observed CDR combination isolated, named Her3‐3, was overexpressed and purified in Fab and immunoglobulin G (IgG) formats. Fab HER3‐3 bound to HER3‐ECD with a K D value of 2.14 n m and recognized cell‐surface HER3. Although HER3‐3 IgG bound to cell‐surface HER3, it did not inhibit the proliferation of HER3‐positive cells. Near‐infrared imaging showed that Fab HER3‐3 selectively accumulated in a murine HER3‐postive xenograft, thus providing a lead for the development of HER3 imaging probes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".