Automated High-Throughput Synthesis of Artificial Glycopeptides. Small-Molecule Probes for Chemical Glycobiology
Bibliographic record
Abstract
A fully automated method for the synthesis of artificial glycopeptides having two (similar or different) carbon-linked glycosyl moieties on a dipeptide scaffold has been developed. By use of this approach that combines the diversity of peptide/pseudopeptide and glycosides, different glycoside moieties can be incorporated onto the peptide/pseudopeptide backbone in a highly controlled manner. The approach utilizes a stepwise reductive amination with glycoside aldehyde derivatives (model 1) or (ii) glycoside reductive amination followed by glycoside amide bond formation (model 2). Further, an automated method has been utilized in the high-throughput library synthesis of 4 x 96 artificial glycopeptides. These libraries were tested as chemical probes/inhibitors of enzyme systems that convert a glucose moiety into rhamnose prior to incorporation of the rhamnose unit and the conversion of UDP-galactopyranose to UDP-galactofuranose via UDP-galactopyranose mutase enzyme during the biosynthesis of the mycobacterium cell wall.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".