P3a site-specific and cassette mutagenesis for seamless protein, RNA and plasmid engineering
Bibliographic record
Abstract
Site-directed mutagenesis is a basic molecular tool required for protein, RNA and plasmid engineering. For mutagenesis methods, an ideal goal is to reach the efficiency of 100%. Towards this goal, we have recently taken the first step by adopting an innovative strategy via primer pairs with 3-prime overhangs and developing P3 site-directed mutagenesis, with an average efficiency of ~50%. As the second step towards the ideal goal, we report here P3a site-directed mutagenesis with an efficiency reaching ~100%. We systematically evaluated this new method by engineering >100 point mutations and small deletions (or insertions) on >20 mammalian expression vectors encoding various epigenetic regulators and the spike protein of SARS-CoV-2. As all known mutagenesis methods are limited to point mutations and small deletions/insertions (up to a dozen nucleotides), a technical problem is how to carry out cassette mutagenesis for replacement, deletion or insertion of large DNA fragments. The high efficiency of P3a mutagenesis and the handshaking feature of primer pairs with 3-prime overhangs inspired us to adapt this new method for seamless cassette mutagenesis, including highly efficient epitope tagging and untagging, deletion of small or large DNA fragments (up to 5 kb) and insertion of gene fragments (up to ~0.4 kb), LoxP sites and sequences encoding degrons, sgRNA and tigRNA. Thus, this new site-specific and cassette mutagenesis method is highly efficient, fast and versatile, likely resulting in its wide use for typical biomedical research, as well as for engineering and refining synthetic or mutant proteins from AI-assisted design.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".