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Spider silks are remarkable materials characterized by superb mechanicalproperties such as strength, extensibility and lightweightedness. Yet, to date,limited models are available to fully explore sequence-property relationshipsfor analysis and design. Here we propose a custom generative large-languagemodel to enable design of novel spider silk protein sequences to meet complexcombinations of target mechanical properties. The model, pretrained on a largeset of protein sequences, is fine-tuned on ~1,000 major ampullate spidroin(MaSp) sequences for which associated fiber-level mechanical properties exist,to yield an end-to-end forward and inverse generative strategy. Performance isassessed through: (1), a novelty analysis and protein type classification forgenerated spidroin sequences through BLAST searches, (2) property evaluationand comparison with similar sequences, (3) comparison of molecular structures,as well as, and (4) a detailed sequence motif analyses. We generate silksequences with property combinations that do not exist in nature, and develop adeep understanding the mechanistic roles of sequence patterns in achievingoverarching key mechanical properties (elastic modulus, strength, toughness,failure strain). The model provides an efficient approach to expand the silkomedataset, facilitating further sequence-structure analyses of silks, andestablishes a foundation for synthetic silk design and optimization.

Generative modeling, design and analysis of spider silk protein sequences for enhanced mechanical properties