CellectSeq: In silico discovery of antibodies targeting integral membrane proteins combining in situ selections and next-generation sequencing
Bibliographic record
Abstract
Synthetic antibody (Ab) technologies are efficient and cost-effective platforms for the generation of monoclonal Abs against human antigens. Yet, they typically depend on purified proteins, which exclude integral membrane proteins that require the lipid bilayers to support their native structure and function. Here, we present an Ab discovery strategy, termed CellectSeq, for targeting integral membrane proteins on native cells in complex environment. As proof of concept, we targeted three transmembrane proteins linked to cancer, tetraspanin CD151, carbonic anhydrase 9, and integrin-α11. First, we performed in situ cell-based selections to enrich phage-displayed synthetic Ab pools for antigen-specific binders. Then, we designed next-generation sequencing procedures to explore Ab diversities and abundances. Finally, we developed motif-based scoring and sequencing error-filtering algorithms for the comprehensive interrogation of next-generation sequencing pools to identify Abs with high diversities and specificities, even at extremely low abundances, which are very difficult to identify using manual sampling or sequence abundances.
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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 | 0.001 |
| 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".