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A major challenge in synthesizing strong and tough protein fibers based on spider silk motifs is understanding the coupling between protein sequence and the postspin drawing process. We clarify how drawing-induced elongational force affects ordering, chain extension, interchain contacts, and molecular mobility through mesoscale simulations of silk-based fibers. We show that these emergent features can be used to predict mechanical property enhancements arising from postspin drawing. Simulations recapitulate a purely process-dependent mechanical property envelope in which order enhances fiber strength while preserving toughness. The relationship between chain extension and crystalline domain alignment observed in simulations is validated by Raman spectroscopy of wet-spun fibers. Property enhancements attributed to the progression of anisotropic extension are verified by mechanical tests of drawn silk fibers and justified by theory. These findings elucidate how drawing enhances properties of protein-based fibers and shed light on how to incorporate this effect into predictive models. 

μ-Ballistic simulations performed on the PGN thin films reveal a positive influence of cohesive energy density on the performance. PGN with heavier nanoparticles arrest bullets more rapidly, however, lighter particles exhibit a higher . 

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. 

Grafting polymer chains to the surface of nanoparticles overcomes the challenge of nanoparticle dispersion within nanocomposites and establishes high-volume fractions that are found to enable enhanced material mechanical properties. This study utilizes coarse-grained molecular dynamics simulations to quantify how the shear modulus of polymer-grafted nanoparticle (PGN) systems in their glassy state depends on parameters such as strain rate, nanoparticle size, grafting density, and chain length. The results are interpreted through further analysis of the dynamics of chain conformations and volume fraction arguments. The volume fraction of nanoparticles is found to be the most influential variable in deciding the shear modulus of PGN systems. A simple rule of mixture is utilized to express the monotonic dependence of shear modulus on the volume fraction of nanoparticles. Due to the reinforcing effect of nanoparticles, shortening the grafted chains results in a higher shear modulus in PGNs, which is not seen in linear systems. These results offer timely insight into calibrating molecular design parameters for achieving the desired mechanical properties in PGNs.