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Abstract Darwin’s bark spider (Caerostris darwini) produces giant orb webs from dragline silk that can be twice as tough as other silks, making it the toughest biological material. This extreme toughness comes from increased extensibility relative to other draglines. We showC. darwinidragline-producing major ampullate (MA) glands highly express a novel silk gene transcript (MaSp4) encoding a protein that diverges markedly from closely related proteins and contains abundant proline, known to confer silk extensibility, in a unique GPGPQ amino acid motif. This suggestsC. darwinievolved distinct proteins that may have increased its dragline’s toughness, enabling giant webs.Caerostris darwini’sMA spinning ducts also appear unusually long, potentially facilitating alignment of silk proteins into extremely tough fibers. Thus, a suite of novel traits from the level of genes to spinning physiology to silk biomechanics are associated with the unique ecology of Darwin’s bark spider, presenting innovative designs for engineering biomaterials.
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Sequence-based data-constrained deep learning framework to predict spider dragline mechanical properties
Abstract Spider dragline silk is known for its exceptional strength and toughness; hence understanding the link between its primary sequence and mechanics is crucial. Here, we establish a deep-learning framework to clarify this link in dragline silk. The method utilizes sequence and mechanical property data of dragline spider silk as well as enriching descriptors such as residue-level mobility (B-factor) predictions. Our sequence representation captures the relative position, repetitiveness, as well as descriptors of amino acids that serve to physically enrich the model. We obtain high Pearson correlation coefficients (0.76–0.88) for strength, toughness, and other properties, which show that our B-factor based representation outperforms pure sequence-based models or models that use other descriptors. We prove the utility of our framework by identifying influential motifs and demonstrating how the B-factor serves to pinpoint potential mutations that improve strength and toughness, thereby establishing a validated, predictive, and interpretable sequence model for designing tailored biomaterials.
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- PAR ID:
- 10532498
- Publisher / Repository:
- Communications Materials
- Date Published:
- Journal Name:
- Communications Materials
- Volume:
- 5
- Issue:
- 1
- ISSN:
- 2662-4443
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1038/s43246-024-00519-y
