Synthesis and Characterization of the Arylomycin Lipoglycopeptide Antibiotics and the Crystallographic Analysis of Their Complex with Signal Peptidase
Bibliographic record
Abstract
Glycosylation of natural products, including antibiotics, often plays an important role in determining their physical properties and their biological activity, and thus their potential as drug candidates. The arylomycin class of antibiotics inhibits bacterial type I signal peptidase and is comprised of three related series of natural products with a lipopeptide tail attached to a core macrocycle. Previously, we reported the total synthesis of several A series derivatives, which have unmodified core macrocycles, as well as B series derivatives, which have a nitrated macrocycle. We now report the synthesis and biological evaluation of lipoglycopeptide arylomycin variants whose macrocycles are glycosylated with a deoxy-α-mannose substituent, and also in some cases hydroxylated. The synthesis of the derivatives bearing each possible deoxy-α-mannose enantiomer allowed us to assign the absolute stereochemistry of the sugar in the natural product and also to show that while glycosylation does not alter antibacterial activity, it does appear to improve solubility. Crystallographic structural studies of a lipoglycopeptide arylomycin bound to its signal peptidase target reveal the molecular interactions that underlie inhibition and also that the mannose is directed away from the binding site into solvent which suggests that other modifications may be made at the same position to further increase solubility and thus reduce protein binding and possibly optimize the pharmacokinetics of the scaffold.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
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 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".