A “biphasic glycosyltransferase high-throughput screen” identifies novel anthraquinone glycosides in the diversification of phenolic natural products
Bibliographic record
Abstract
The sugar moieties of many glycosylated small molecule natural products are essential for their biological activity. Glycosyltransferases (GTs) are enzymes responsible for installing these sugar moieties on a variety of biomolecules. Many GTs active on natural products are inherently substrate promiscuous and thus serve as useful tools in manipulating natural product glycosylation to generate new combinations of sugar units (glycones) and scaffold molecules (aglycones) in a process called glycodiversification. It is important to have an effective screening tool to detect the activity of promiscuous enzymes and their resulting glycoside products. Toward this aim, we developed a strategy for screening natural product GTs in a high-throughput fashion enabled by rapid isolation and detection of chromophoric or fluorescent glycosylated natural products. This involves a solvent extraction step to isolate the resulting polar glycoside product from the unreacted aglycone acceptor substrate and the detection of the formed glycoside by the innate absorbance or fluorescence of the aglycone moiety. Using our approach, we screened a collection of natural product GTs against a panel of precursors to therapeutically important molecules. Three GTs showed previously unreported promiscuity toward anthraquinones resulting in novel ε-rhodomycinone glycosides. Considering the pharmaceutical value of clinically used anthraquinone glycosides that are biosynthesized from an ε-rhodomycinone precursor, and the significance that the sugar moiety has on the biological activity of these drugs, our results are of particular importance toward the glycodiversification of therapeutics in this class. The GTs identified and the novel compounds they produce show promise toward new biocatalytic tools and therapeutics.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".