Scalable High Throughput Selection From Phage-displayed Synthetic Antibody Libraries
Bibliographic record
Abstract
The demand for antibodies that fulfill the needs of both basic and clinical research applications is high and will dramatically increase in the future. However, it is apparent that traditional monoclonal technologies are not alone up to this task. This has led to the development of alternate methods to satisfy the demand for high quality and renewable affinity reagents to all accessible elements of the proteome. Toward this end, high throughput methods for conducting selections from phage-displayed synthetic antibody libraries have been devised for applications involving diverse antigens and optimized for rapid throughput and success. Herein, a protocol is described in detail that illustrates with video demonstration the parallel selection of Fab-phage clones from high diversity libraries against hundreds of targets using either a manual 96 channel liquid handler or automated robotics system. Using this protocol, a single user can generate hundreds of antigens, select antibodies to them in parallel and validate antibody binding within 6-8 weeks. Highlighted are: i) a viable antigen format, ii) pre-selection antigen characterization, iii) critical steps that influence the selection of specific and high affinity clones, and iv) ways of monitoring selection effectiveness and early stage antibody clone characterization. With this approach, we have obtained synthetic antibody fragments (Fabs) to many target classes including single-pass membrane receptors, secreted protein hormones, and multi-domain intracellular proteins. These fragments are readily converted to full-length antibodies and have been validated to exhibit high affinity and specificity. Further, they have been demonstrated to be functional in a variety of standard immunoassays including Western blotting, ELISA, cellular immunofluorescence, immunoprecipitation and related assays. This methodology will accelerate antibody discovery and ultimately bring us closer to realizing the goal of generating renewable, high quality antibodies to the proteome.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".