Global analysis of prokaryotic tRNA-derived cyclodipeptide biosynthesis
Bibliographic record
Abstract
BACKGROUND: Among naturally occurring small molecules, tRNA-derived cyclodipeptides are a class that have attracted attention for their diverse and desirable biological activities. However, no tools are available to link cyclodipeptide synthases identified within prokaryotic genome sequences to their chemical products. Consequently, it is unclear how many genetically encoded cyclodipeptides represent novel products, and which producing organisms should be targeted for discovery. RESULTS: We developed a pipeline for identification and classification of cyclodipeptide biosynthetic gene clusters and prediction of aminoacyl-tRNA substrates and complete chemical structures. We leveraged this tool to conduct a global analysis of tRNA-derived cyclodipeptide biosynthesis in 93,107 prokaryotic genomes, and compared predicted cyclodipeptides to known cyclodipeptide synthase products and all known chemically characterized cyclodipeptides. By integrating predicted chemical structures and gene cluster architectures, we created a unified map of known and unknown genetically encoded cyclodipeptides. CONCLUSIONS: Our analysis suggests that sizeable regions of the chemical space encoded within sequenced prokaryotic genomes remain unexplored. Our map of the landscape of genetically encoded cyclodipeptides provides candidates for targeted discovery of novel compounds. The integration of our pipeline into a user-friendly web application provides a resource for further discovery of cyclodipeptides in newly sequenced prokaryotic genomes.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".