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.
| Original language | English (US) |
|---|---|
| Article number | 83 |
| Journal | Communications Materials |
| Volume | 5 |
| Issue number | 1 |
| DOIs | |
| State | Published - Dec 2024 |
Funding
The primary support for this work came from a National Science Foundation Growing Convergence Research Grant (award no. 2219149). Analysis methods for mutation studies were partially supported by the National Science Foundation\u2019s MRSEC program (DMR-2308691) at the Materials Research Center of Northwestern University. The authors acknowledge support from the Department of Mechanical Engineering at Northwestern University. The authors also acknowledge Jacob Graham and Heather White for their valuable input regarding the preparation of this manuscript, and Dr. Wei (Wayne) Chen for the code review.
ASJC Scopus subject areas
- General Materials Science
- Mechanics of Materials