Generative design and molecular mechanics characterization of silk proteins based on unfolding behavior

Abstract

Spider silk exhibits exceptional mechanical properties, biocompatibility, and biodegradability, making it a promising material for bioengineered applications. However, the complexity and diversity of silk proteins, coupled with limited experimental data, have hindered the rational design of silk-based biomaterials. Furthermore, the mechanobiology of these proteins and their impact on silk fiber properties remain underexplored. In this study, we introduce a series of novel silk protein sequences and characterize their nonlinear unfolding behavior and mechanical properties through molecular dynamics (MD) simulations. Focusing on major ampullate spidroin (MaSp) silk proteins, we curate a dataset that integrates experimentally acquired sequences with synthetic sequences generated by SilkomeGPT, a generative model for silk-inspired proteins. Structural predictions are performed using OmegaFold, from which high-fidelity regions are extracted and analyzed. Their unfolding responses are assessed via implicit all-atom MD simulations, enabling characterization of their mechanical behavior. This computationally efficient framework facilitates the rational design of spider silk proteins by linking atomistic and sequence features to larger-scale properties. The developed dataset systematically captures structural uncertainties, while simulations provide atomic-level insights into how protein mechanics contribute to fiber properties, advancing the mechanobiological understanding of spider silk and supporting diverse applications in biomaterials design.