Deep-mutational scanning libraries using tiled-region exchange mutagenesis
Bibliographic record
Abstract
ABSTRACT The analysis of gene function frequently requires the generation of mutants. Deep-mutational scanning (DMS) has emerged as a powerful tool to decipher important functional residues within genes and proteins. However, methods for performing DMS tend to be complex or laborious. Here, we introduce Tiled-Region Exchange (T-REx) Mutagenesis, which is a multiplexed modification of the EMPIRIC mutagenesis approach. Self-encoded removal fragments are cloned in parallel in non-overlapping gene locations and pooled. In a one-pot reaction, oligonucleotides are then swapped with their corresponding self-encoded removal fragments in bulk using a single Golden Gate reaction. To aid in downstream phenotyping, the library is then fused with unique DNA barcodes using the Bxb1 recombinase. We demonstrate this approach and its optimizations, to show that it is both easy to perform and efficient. This method offers simple and expedient means to create comprehensive mutagenesis libraries. ARTICLE SUMMARY Researchers in the field of molecular genetics frequently investigate protein function via mutations. Here, we present a rapid deep-mutational scanning methodology called Tiled-Region Exchange Mutagenesis that can comprehensively create mutant libraries. The approach is a multiplexed version of the established EMPIRIC mutagenesis approach and retains its simplicity. We show optimizations of the in vitro reactions to reduce cost and improve robustness. This method will be useful to researchers seeking to perform deep-mutational scanning on any gene of interest.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 0.001 |
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".