Structure-based mutagenic analysis of mechanism and substrate specificity in mammalian glycosyltransferases: Porcine ST3Gal-I
Bibliographic record
Abstract
Sialyltransferases (STs) play essential roles in signaling and in the cellular recognition processes of mammalian cells by selectively installing cell-surface sialic acids in an appropriate manner both temporally and organ-specifically. The availability of the first three-dimensional structure of a mammalian (GT29) sialyltransferase has, for the first time, allowed quantitative structure/function analyses to be performed, thereby providing reliable insights into the roles of key active site amino acids. Kinetic analyses of mutants of ST3Gal-I, in conjunction with structural studies, have confirmed the mechanistic roles of His302 and His319 as general acid and base catalysts, respectively, and have quantitated other interactions with the cytosine monophosphate-N-acetyl β-neuraminic acid donor substrate. The contributions of side chains that provide key interactions with the acceptor substrate, defining its specificity, have also been quantitated. Particularly important transition-state interactions of 2.5 and 2.7 kcal mol(-1) are found between the acceptor axial 4-hydroxyl and the conserved side chains of Gln108 and Tyr269, respectively. These results provide a basis for the engineering of mammalian STs to accommodate non-natural substrate analogs that should prove valuable as chemical biological probes of sialyltransferase function.
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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".