Bibliographic record
Abstract
Bacterial glycosyltransferases (GT) often synthesize the same glycan linkages as mammalian GT; yet, they usually have very little sequence identity. Nevertheless, enzymatic properties, folding, substrate specificities, and catalytic mechanisms of these enzyme proteins may have significant similarity. Thus, bacterial GT can be utilized for the enzymatic synthesis of both bacterial and mammalian types of complex glycan structures. A comparison is made here between mammalian and bacterial enzymes that synthesize epitopes found in mammalian glycoproteins, and those found in the O antigens of Gram-negative bacteria. These epitopes include Thomsen-Friedenreich (TF or T) antigen, blood group O, A, and B, type 1 and 2 chains, Lewis antigens, sialylated and fucosylated structures, and polysialic acids. Many different approaches can be taken to investigate the substrate binding and catalytic mechanisms of GT, including crystal structure analyses, mutations, comparison of amino acid sequences, NMR, and mass spectrometry. Knowledge of the protein structures and functions helps to design GT for specific glycan synthesis and to develop inhibitors. The goals are to develop new strategies to reduce bacterial virulence and to synthesize vaccines and other biologically active glycan structures.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".