A proposal for the naming of N-glycosylation pathway components in Archaea
Bibliographic record
Abstract
To the Editor, In 1976, the long-held opinion that N-glycosylation was a trait restricted to eukaryotes was overturned when the surface-layer glycoprotein from the archaeon Halobacterium salinarum was shown to undergo such post-translational modification (Mescher and Strominger, 1976). Largely based on genomic studies, it is now believed that N-glycosylation is a common process in Archaea (Kaminski et al., 2013). Analysis of the limited number of characterized archaeal N-linked glycan reveals diversity in composition unparalleled in either eukaryal or bacterial N-glycosylation (Schwarz and Aebi, 2011; Eichler, 2013). Such variety is indicative of N-glycosylation in Archaea being largely mediated by different, species-specific pathways. Current biochemical descriptions of archaeal N-glycosylation systems support this claim (Jarrell et al., 2010; Eichler, 2013; Meyer and Albers, 2013). As such, it is essential that workers in the field adhere to a common nomenclature for the genes and proteins involved in the archaeal version of this post-translational modification. Indeed, by adopting an agreed upon naming system, some of the ambiguity plaguing the field of bacterial protein glycosylation might be avoided. For instance, although PglB, PglC, PglD, PglE and PglF all contribute to N-glycosylation in Campylobacter jejuni and O-glycosylation in Neisseria gonorrhoeae, proteins bearing the same name serve very different roles in each system (Linton et al., 2005; Aas et al., 2007).
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".