New Insights on Structure and Function of Sialyltransferases
Bibliographic record
Abstract
Sialic acids are a unique posttranslational modification at the terminus of glycoproteins and -lipids. Proteins modified with oligomers of sialic acid add a repellent charge to cell surfaces, which is a crucial feature in cell migration and axonal growth during early brain development. Varied expression levels of sialic acid are linked to tumor malignancy in neuroblastoma, schizophrenia, autism and bipolar disorder but the lack thereof is linked to impaired neuronal development. On the other hand, overexpression of sialic acid oligomers in Schwann cells promotes the peripheral regeneration of lesioned nerves and improves the ability of Schwann cells to migrate into damaged tissue and to remyelinate central nervous system axons. In order to understand the molecular mechanisms of sialylation, our project focuses on the structural characterization of enzymes of the mammalian and bacterial glycosyltransferase families 29 and 42. The proteins of interest were expressed in insect cells and structural studies were undertaken by x-ray crystallography. Kinetics, SEC MALS and glycan array data will shed light on mechanism of catalysis and acceptor specificity. Altogether, the results of this study will promote further understanding of the structure-function relationship of sialyltransferases.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".