EXPERT expands prime editing efficiency and range of large fragment edits
Bibliographic record
Abstract
Prime editing systems (PEs) hold great promise in modern biotechnology. However, their editing range is limited as PEs can only modify the downstream sequences of the pegRNA nick. Here, we report the development of the extended prime editor system (EXPERT) to overcome this limitation by using an extended pegRNA (ext-pegRNA) with modified 3' extension, and an additional sgRNA (ups-sgRNA) targeting the upstream region of the ext-pegRNA. We demonstrate that EXPERT can efficiently perform editing on both sides of the ext-pegRNA nick, a task that is unattainable by canonical PEs. EXPERT exhibits prominent capacity in replacing sequences up to 88 base pairs and inserting sequences up to 100 base pairs within the upstream region of the ext-pegRNA nick. Compared to canonical PEs such as PE2, the utilization of the EXPERT strategy significantly enhances the editing efficiency for large fragment edits with an average improvement of 3.12-fold, up to 122.1 times higher. Safety wise, the use of ups-sgRNA does not increase the rates of undesirable insertions and deletions (indels), as the two nicks are on the same strand. Moreover, we do not observe increased off-target editing rates genome-wide. Our work introduces EXPERT as a PE tool with significant potential in life sciences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".