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

TL;DR

This paper introduces a generative language model for designing novel spider silk protein sequences with enhanced mechanical properties, enabling exploration of sequence-property relationships and synthetic silk development.

Contribution

A novel deep learning model that predicts and designs spider silk sequences with desired mechanical traits, expanding the silkome dataset and aiding synthetic silk engineering.

Findings

Generated sequences exhibit properties not found in nature.

Model accurately classifies and evaluates protein sequences.

Sequence motifs linked to mechanical properties identified.

Abstract

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.