Efficient and Accurate Site-Directed Mutagenesis of Large Plasmids
Bibliographic record
Abstract
Since the development of the polymerase chain reaction (PCR) technique (), its potential use as a tool for site-directed mutagenesis (SDM) has been extensively explored, as illustrated by several chapters in this volume. In particular, the relative ease with which DNA fragments of 2–3 kb can be generated facilitated the development of the highly efficient procedure known as “recombination PCR” (RPCR), the principles of which were first described by Jones and Howard () and were later refined (3,4). Briefly, in this technique, the entire plasmid, containing the cloned gene of interest, is amplified as two overlapping fragments, each generated using a primer pair comprising a nonmutagenic primer directed to vector sequence and a mutagenic primer targeting the mutation site. Following separate amplification of the plasmid in two halves, using both primer sets, the complete plasmid is regenerated by recombination in vivo following cotransformation of the two fragments into competent recA− Escherichia coli cells. Since both DNA strands carry the desired mutation as directed by the mutagenic primers, the recovery of mutant clones should, in theory, approach 100%, although, in practice, mutant yields, ranging from 50 to 100% have been reported ().
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".