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Sequence-based data-constrained deep learning framework to predict spider dragline mechanical properties
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AI
DOI：10.1038/s43246-024-00519-y.png)
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Abstract
En 中文
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. Understanding the influence of spider dragline silk sequence on its properties is important for controlling their strength and toughness properties. Here, a deep-learning framework is proposed that describes the behavior of spider dragline silks, linking sequence and mechanics.
Keywords:
SILK
ORIENTATION
VARIABILITY
SIMULATION
TOUGHNESS
STRENGTH
NMR
Journal
C
IF:
9.6
Papers:
1.4K
Citations:
4.3K