Genome mining of tailoring enzymes from biosynthetic gene clusters for synthetic biology: A case study with fungal methyltransferases
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Harnessing the potential of tailoring enzymes within fungal natural product (NP) biosynthetic gene clusters (BGCs) can significantly enhance NP diversity and production efficiency via artificially constructed microbial cell factories. To achieve this, an efficient genome mining method is crucial, especially since the functions of many putative enzymes in databases are unknown. As a test case, we aimed to identify methyltransferases (MTs) that modify a polyketide substrate without a known cognate MT. 16,748 putative MTs were annotated in 101,321 fungal BGCs and grouped into orthologous families. Three methods were explored to prioritize suitable enzymes. Among these, the machine learning method proved superior, with 11 out of 15 tested MTs successfully methylating the test substrate. This demonstrates the effectiveness of machine learning to mine tailoring enzymes that modify selected compounds, aiding synthetic biology in optimizing NP biosynthesis and facilitating the production of "unnatural products" for pharmaceutical or other bioindustrial applications.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it