Efficiency and fidelity of T3 DNA ligase in ligase-catalysed oligonucleotide polymerisations
Bibliographic record
Abstract
Ligase-catalyzed oligonucleotide polymerisations (LOOPER) can readily generate libraries of diversely-modified nucleic acid polymers, which can be subjected to iterative rounds of in vitro selection to evolve functional activity. While there exist several different DNA ligases, T4 DNA ligase has most often been used for the process. Recently, T3 DNA ligase was shown to be effective in LOOPER; however, little is known about the fidelity and efficiency of this enzyme in LOOPER. In this paper we evaluate the efficiency of T3 DNA ligase and T4 DNA ligase for various codon lengths and compositions within the context of polymerisation fidelity and yield. We find that T3 DNA ligase exhibits high efficiency and fidelity with short codon lengths, but struggles with longer and more complex codon libraries, while T4 DNA ligase exhibits the opposite trend. Interestingly, T3 DNA ligase is unable to accommodate modifications at the 8-position of adenosine when integrated into short codons, which will create challenges in expanding the available codon set for the process. The limitations and strengths of the two ligases are further discussed within the context of LOOPER.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".