Customizing Spider Silk: Generative Models with Mechanical Property Conditioning for Protein Engineering

The remarkable mechanical properties of spider silk, including its tensile strength and ex-tensibility, are primarily governed by the repeat regions of the proteins that constitute the fiber, the major ampullate spidroins (MaSps). However, establishing correlations between mechanical characteristics and repeat sequences remains challenging due to the intricate se-quence–structure–function relationships of MaSps and the limited availability of annotated datasets. In this study, we present a novel computational framework for designing MaSp repeat sequences with customizable mechanical properties. To achieve this, we developed a lightweight GPT-based generative model by distilling the pre-trained ProtGPT2 protein language model. The distilled model was subjected to multi-level fine-tuning using curated subsets of the Spider Silkome dataset. Specifically, we adapted the model for MaSp repeat generation using 6,000 MaSp repeat sequences and further refined it via cross-validation on 592 repeats associated with experimentally determined fiber-level mechanical properties. Our model generates biologically plausible MaSp repeat regions tailored to specific mechanical properties, while also predicting those properties for given sequences. Validation includes sequence-level analysis, assessing physicochemical attributes, the expected distribution of key motifs, and secondary structure compositions. A correlation study using BLAST on the Spider Silkome dataset and a test set of MaSp repeats with known mechanical properties further confirmed the predictive accuracy of the model. This framework advances the rational design of spider silk-inspired biomaterials, offering a versatile tool for engineering protein sequences with tailored mechanical attributes.