Proline Hydroxylation in Cell Wall Proteins: Is It Yet Possible to Define Rules?
Bibliographic record
Abstract
Cell wall proteins (CWPs) play critical and dynamic roles in plant cell walls by contributing to developmental processes and response to environmental cues. Since the CWPs go through the secretion pathway, most of them undergo post-translational modifications (PTMs) which can modify their biological activity. Glycosylation is one of the major PTMs of CWPs and refers to N-glycosylation, O-glycosylation and glypiation. Each of these PTMs occurs in different amino acid contexts which are not all well defined. This article deals with the hydroxylation of Pro residues which is a prerequisite for O-glycosylation of CWPs on hydroxyproline (Hyp) residues. The location of Hyp residues is well described in several structural CWPs, but yet rarely described in other CWPs. In this article, it is studied in detail in five Arabidopsis thaliana proteins using mass spectrometry (MS) data: one of them (At4g38770, AtPRP4) is a structural CWP containing 32.5% of Pro residues arranged in typical motifs, the others are either rich (27-28%, At1g31580 and At2g10940) or poor (6-8%, At1g09750 and At3g08030) in Pro residues. The known rules of Pro hydroxylation allowed a good prediction of Hyp location in AtPRP4. However, they could not be applied to the other proteins whatever their Pro content. In addition, variability of the Pro hydroxylation patterns was observed within some amino acid motifs in all the proteins and new patterns of Pro hydroxylation are described. Altogether, this work shows that Hyp residues are present in more protein families than initially described, and that Pro hydroxylation patterns could be different in each of them. This work paves the way for completing the existing Pro hydroxylation code.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".