Pour some sugar on it: the expanding world of bacterial protein <i><scp>O</scp></i>‐linked glycosylation
Bibliographic record
Abstract
Protein glycosylation was once considered as an eccentricity of a few bacteria. However in the recent years multiple O-glycosylation mechanisms have been identified in bacterial species from the most diverse genera, including various important human pathogens. This review focuses on summarizing the structural diversity, the various pathways and the physiological roles of this post-translational protein modification. We propose a classification of O-glycosylation based on the requirement of an oligosaccharyltransferase (OTase). OTase-dependent glycosylation utilizes an oligosaccharide synthesized on a lipid carrier that is transferred to proteins en bloc by an OTase. Multiple proteins, including the pilins, are glycosylated using this mechanism. OTase-independent glycosylation refers to the pathway in which glycosyltransferases sequentially add monosaccharides onto the target proteins. This pathway is employed for glycosylation of flagella and autotransporters. Both systems play key roles in pathogenesis. Exploiting glycosylation machineries it is now possible to generate glycoconjugates made of different proteins attached to polysaccharides derived from LPS or capsule biosynthesis. These recombinant glycoproteins can be exploited for vaccines and diagnostics of bacterial infections. Furthermore, O-glycosylation systems are promising targets for antibiotic development. Technological advances in MS and NMR will facilitate the discovery of novel glycosylation systems. Likely, the O-glycosylation pathways we currently know constitute just the tip of the iceberg of a still largely uncharacterized bacterial glycosylation world.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".