P3 site-directed mutagenesis: An efficient method based on primer pairs with 3-prime overhangs
Bibliographic record
Abstract
Site-directed mutagenesis is a fundamental tool indispensable for protein and plasmid engineering. An important technological question is how to achieve the efficiency at the ideal level of 100%. Based on complementary primer pairs, the QuickChange method has been widely used, but it requires significant improvements due to its low efficiency and frequent unwanted mutations. An alternative and innovative strategy is to utilize primer pairs with 3-prime overhangs, but this approach has not been fully developed. As the first step towards reaching the efficiency of 100%, we have optimized this pproach systematically and evaluated the resulting method extensively with >100 mutations on 12 mammalian expression vectors, ranging from 7.0-13.4 kb in size and encoding ten epigenetic regulators with links to cancer and neurodevelopmental disorders. We have also tested the new method with two expression vectors for the SARS-COV-2 spike protein. Compared to the QuickChange method, the success rate has increased substantially, with an average efficiency of >50%, with some at or close to 100%, and requiring much less time for engineering various mutations. Therefore, in this study, we have developed a new site-directed mutagenesis method for efficient, versatile and economical generation of various mutations. Importantly, the extensive experience from this study also sheds light on how to develop ideal mutagenesis methods with the efficiency at or close to 100% for a wide spectrum of plasmids.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".