Receptor-based protein binding in the supramolecular network of velvet worm slime
Bibliographic record
Abstract
Abstract 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-sheets-rich domains of high-molecular-weight proteins, the primary building blocks of velvet worm slime fibers. This previously unknown functional context of LRR proteins is presumably involved in reversible, receptor-based supramolecular network formation in these adhesive biofibers and provides possible new avenues for fabricating fully recyclable (bio)polymeric materials. Significance Statement Analyzing structure-function-relationships underlying reversible fiber formation in velvet worm slime may inspire avenues for the sustainable fabrication of protein-based polymeric materials. Here, we present evidence for an evolutionarily conserved mechanism of reversible fiber formation in velvet worm slime based on the receptor-like binding of fiber forming proteins by a leucine-rich repeat (LRR) protein. The structures of both protein components are highly conserved evolutionarily in the two distantly related velvet worm subgroups, indicating pervasive presence of this mechanism across species that has been maintained through the last ∼380 MY. Our results suggest that the ubiquitously occurring LRR motif—better known for its innate immunity and developmental roles—has a novel identified function in processing a biological material, which might contribute to the development of sustainable bio-inspired materials.
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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 | 0.000 | 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".