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Record W4327740286 · doi:10.26434/chemrxiv-2023-lqhrx

Phosphonated glycans as post-translational modifications of proteins in velvet worm slime

2023· preprint· en· W4327740286 on OpenAlexaff
Alexander Poulhazan, Alexander B. Baer, Gagan Daliaho, Frédéric Mentink‐Vigier, Alexandre A. Arnold, Darren C. Browne, Lars Hering, Stephanie Archer‐Hartmann, Lauren E. Pepi, Parastoo Azadi, Stephan Schmidt, Georg Mayer, Isabelle Marcotte, Matthew J. Harrington

Bibliographic record

VenueChemRxiv · 2023
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicTardigrade Biology and Ecology
Canadian institutionsMcGill UniversityUniversité du Québec à Montréal
Fundersnot available
KeywordsSlime moldPhosphonateVelvetBiologySILKChemistryBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

To capture prey, onychophorans (velvet worms) expel a slime that forms stiff fibers upon shearing and dehydration. The high quantities of phosphorus previously found in the slime of the velvet worm Euperipatoides rowelli were ascribed to protein phosphorylation. We provide clear evidence, instead, that it is primarily present as phosphonate moieties in the slime of representative from both major onychophoran subgroups which diverged ~380 MYA. Advanced NMR and mass spectrometry demonstrate that 2-aminoethyl phosphonate (2-AEP) is associated with high molecular weight slime proteins as phosphonoglycans. Biogenic phosphonates are a substantial component of the organophosphorus cycle in marine environments but were not previously reported in terrestrial invertebrates. The evolutionary conservation of this rare protein modification suggests a potential role in the formation and function of these biological adhesive fibers with implications for bio-inspiration.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.042
GPT teacher head0.248
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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