Catalytic mechanism of the inverting N-acetylglucosaminyltransferase I: DFT quantum mechanical model of the reaction pathway and determination of the transition state structure
Bibliographic record
Abstract
The complex N-glycan structures on glycoproteins play important roles in cell adhesion and recognition events in metazoan organisms. A critical step in the biosynthetic pathway leading from high mannose to these complex structures includes the transfer of N-acetylglucosamine (GlcNAc) to a mannose residue by the inverting N-acetylglucosaminyltransferase I (GnT-I). The catalytic mechanism of this enzymatic reaction is explored herein using DFT quantum chemical methods. The computational model used to follow the reaction is based on the X-ray crystallographic structure of GnT-I and contains 127 atoms that represent fragments of residues critical for the substrate binding and catalysis. The mechanism of the catalytic reaction was monitored by means of a 2D potential energy map calculated as a function of predefined reaction coordinates at the B3LYP/6-31G** level. This potential energy surface revealed one transition state associated with a reaction pathway following a concerted mechanism. The reaction barrier was estimated, and the structure of the transition state was characterized at the B3LYP/6-311++G**// B3LYP/6-31G** level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".