Peculiar Phosphonate Modifications of Velvet Worm Slime Revealed by Advanced Nuclear Magnetic Resonance and Mass Spectrometry
Bibliographic record
Abstract
Nature is rich with examples of highly specialized biological materials produced by organisms for functions, including defense, hunting, and protection. Along these lines, velvet worms (Onychophora) expel a protein-based slime used for hunting and defense that upon shearing and dehydration forms fibers as stiff as thermoplastics. These fibers can dissolve back into their precursor proteins in water, after which they can be drawn into new fibers, providing biological inspiration to design recyclable materials. Elevated phosphorus content in velvet worm slime was previously observed and putatively ascribed to protein phosphorylation. Here, we show instead that phosphorus is primarily present as phosphonate moieties in the slime of distantly related velvet worm species. Using high-resolution nuclear magnetic resonance (NMR), natural abundance dynamic nuclear polarization (DNP), and mass spectrometry (MS), we demonstrate that 2-aminoethyl phosphonate (2-AEP) is associated with glycans linked to large slime proteins, while transcriptomic analyses confirm the expression of 2-AEP synthesizing enzymes in slime glands. The evolutionary conservation of this rare protein modification suggests an essential functional role of phosphonates in velvet worm slime and should stimulate further study of the function of this unusual chemical modification in nature.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".