A High Through-put Platform for Recombinant Antibodies to Folded Proteins
Bibliographic record
Abstract
Antibodies are key reagents in biology and medicine, but commercial sources are rarely recombinant and thus do not provide a permanent and renewable resource. Here, we describe an industrialized platform to generate antigens and validated recombinant antibodies for 346 transcription factors (TFs) and 211 epigenetic antigens. We describe an optimized automated phage display and antigen expression pipeline that in aggregate produced about 3000 sequenced Fragment antigen-binding domain that had high affinity (typically EC50<20 nm), high stability (Tm∼80 °C), good expression in E. coli (∼5 mg/L), and ability to bind antigen in complex cell lysates. We evaluated a subset of Fabs generated to homologous SCAN domains for binding specificities. These Fragment antigen-binding domains were monospecific to their target SCAN antigen except in rare cases where they cross-reacted with a few highly related antigens. Remarkably, immunofluorescence experiments in six cell lines for 270 of the TF antigens, each having multiple antibodies, show that ∼70% stain predominantly in the cytosol and ∼20% stain in the nucleus which reinforces the dominant role that translocation plays in TF biology. These cloned antibody reagents are being made available to the academic community through our web site recombinant-antibodies.org to allow a more system-wide analysis of TF and chromatin biology. We believe these platforms, infrastructure, and automated approaches will facilitate the next generation of renewable antibody reagents to the human proteome in the coming decade.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.009 |
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".