Glycosyltransferases as versatile tools to study the biology of glycans
Bibliographic record
Abstract
All cells are decorated with complex carbohydrate structures called glycans that serve as ligands for glycan-binding proteins (GBPs) to mediate a wide range of biological processes. Understanding the specific functions of glycans is key to advancing an understanding of human health and disease. However, the lack of convenient and accessible tools to study glycan-based interactions has been a defining challenge in glycobiology. Thus, the development of chemical and biochemical strategies to address these limitations has been a rapidly growing area of research. In this review, we describe the use of glycosyltransferases (GTs) as versatile tools to facilitate a greater understanding of the biological roles of glycans. We highlight key examples of how GTs have streamlined the preparation of well-defined complex glycan structures through chemoenzymatic synthesis, with an emphasis on synthetic strategies allowing for site- and branch-specific display of glyco-epitopes. We also describe how GTs have facilitated expansion of glyco-engineering strategies, on both glycoproteins and cell surfaces. Coupled with advancements in bioorthogonal chemistry, GTs have enabled selective glyco-epitope editing of glycoproteins and cells, selective glycan subclass labeling, and the introduction of novel biomolecule functionalities onto cells, including defined oligosaccharides, antibodies, and other proteins. Collectively, these approaches have contributed great insight into the fundamental biological roles of glycans and are enabling their application in drug development and cellular therapies, leaving the field poised for rapid expansion.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".