Spider silk is nature’s composite: steel-strong yet elastic. But its gigantic, repetitive proteins have kept full-strength synthetic fibers out of reach. Enter our SilkomeGPT-driven multi-agent framework that combines language models with physics reasoning, in research led by my graduate student
@IrisWeiLu. We trained a language model on ~1,000 real spidroins, generated thousands of novel sequences to explore diverse mechanical features, folded them virtually, then yanked each atom-by-atom in steered molecular dynamics. We generated thousands of force curves that pinpoint which glycine coils give stretch and which β-sheet blocks lock in strength. Along the way, we uncovered a hidden rule: toughness tracks with adaptability. The number of secondary structure transitions - shifts between helix, sheet, and coil during pulling - is highly predictive of molecular toughness (R = 0.77). Proteins that reshape themselves under strain absorb more energy. We also found that protein length alone predicts toughness with R = 0.93, offering a simple lever for energy absorption. But at the fiber scale, mechanics diverge - revealing that hierarchical assembly, not sequence alone, governs real-world strength. We now have a quantitative map from sequence to mechanics, a GPS for designing tougher, more resilient and greener fibers. Applications include custom biomedical materials, parachute lines, biodegradable sutures, even soft exoskeleton cables or soft robotics actuators - all tuned in silico before a single bioreactor run.
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