Streamlining the Pipeline for Generation of Recombinant Affinity Reagents by Integrating the Affinity Maturation Step
Bibliographic record
Abstract
Often when generating recombinant affinity reagents to a target, one singles out an individual binder, constructs a secondary library of variants, and affinity selects a tighter or more specific binder. To enhance the throughput of this general approach, we have developed a more integrated strategy where the "affinity maturation" step is part of the phage-display pipeline, rather than a follow-on process. In our new schema, we perform two rounds of affinity selection, followed by error-prone PCR on the pools of recovered clones, generation of secondary libraries, and three additional rounds of affinity selection, under conditions of off-rate competition. We demonstrate the utility of this approach by generating low nanomolar fibronectin type III (FN3) monobodies to five human proteins: ubiquitin-conjugating enzyme E2 R1 (CDC34), COP9 signalosome complex subunit 5 (COPS5), mitogen-activated protein kinase kinase 5 (MAP2K5), Splicing factor 3A subunit 1 (SF3A1) and ubiquitin carboxyl-terminal hydrolase 11 (USP11). The affinities of the resulting monobodies are typically in the single-digit nanomolar range. We demonstrate the utility of two binders by pulling down the targets from a spiked lysate of HeLa cells. This integrated approach should be applicable to directed evolution of any phage-displayed affinity reagent scaffold.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".