Generative modeling, design and analysis of spider silk protein
  sequences for enhanced mechanical properties
Wei Lu David L. Kaplan Markus J. Buehler
Spider silks are remarkable materials characterized by superb mechanical
properties such as strength, extensibility and lightweightedness. Yet, to date,
limited models are available to fully explore sequence-property relationships
for analysis and design. Here we propose a custom generative large-language
model to enable design of novel spider silk protein sequences to meet complex
combinations of target mechanical properties. The model, pretrained on a large
set 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 is
assessed through: (1), a novelty analysis and protein type classification for
generated spidroin sequences through BLAST searches, (2) property evaluation
and comparison with similar sequences, (3) comparison of molecular structures,
as well as, and (4) a detailed sequence motif analyses. We generate silk
sequences with property combinations that do not exist in nature, and develop a
deep understanding the mechanistic roles of sequence patterns in achieving
overarching key mechanical properties (elastic modulus, strength, toughness,
failure strain). The model provides an efficient approach to expand the silkome
dataset, facilitating further sequence-structure analyses of silks, and
establishes a foundation for synthetic silk design and optimization.