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Record W4408570338 · doi:10.1073/pnas.2416282122

Conserved leucine-rich repeat proteins in the adhesive projectile slime of velvet worms

2025· article· en· W4408570338 on OpenAlexafffund
Zhaolong Hu, Alexander B. Baer, Lars Hering, Ivo de Sena Oliveira, Alexandre Poulhazan, Darren C. Browne, Xue Guo, Quentin Moana Perrin, Radoslaw M. Sobota, Shawn Hoon, Georg Mayer, Srinivasaraghavan Kannan, Chandra Verma, Matthew J. Harrington, Ali Miserez

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicTardigrade Biology and Ecology
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaAlexander von Humboldt-StiftungSingapore Energy CentreNational Research FoundationDeutsche ForschungsgemeinschaftNational Research Foundation Singapore
KeywordsVelvetBiologySlime moldComputational biologyCell biologyChemistry

Abstract

fetched live from OpenAlex

The slime of velvet worms (Onychophora) is a protein-based bioadhesive that undergoes rapid, yet reversible transition from a fluid into stiff fibers used for prey capture and defense, but the mechanism by which this phase transition functions is largely unknown. Here, integrating transcriptomic and proteomic approaches with AI-guided structure predictions, we discover a group of evolutionarily conserved leucine-rich repeat (LRR) proteins in velvet worm slime that readily adopt a receptor-like, protein-binding "horseshoe" structure. Our structural predictions suggest dimerization of LRR proteins and support their interactions with conserved β-sheet-rich domains of high-molecular-weight proteins, the primary building blocks of velvet worm slime fibers. This suggests that LRR proteins might be involved in reversible, receptor-based supramolecular interactions in these biofibers, providing potential avenues for fabricating fully recyclable (bio)polymeric materials.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.534
Threshold uncertainty score0.301

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.043
GPT teacher head0.280
Teacher spread0.237 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2025
Admission routes2
Has abstractyes

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