Kideraspa: designing variants of staphylococcal protein a based on a diffusion model with kidera factors.

The interaction between staphylococcal protein A (SpA) and human immunoglobulin G (IgG) is pivotal in treating diseases such as cancer, inflammation, infections, and autoimmune disorders. However, acquiring natural SpA variants is labor-intensive, traditional protein design methods often depend on extensive datasets and detailed structural information, limiting their efficiency and applicability. To overcome these limitations, we propose a deep learning-based approach that directly targets desired binding functions by introducing mutations at selected SpA sites to optimize its properties. Specifically, we present a de novo protein design strategy that integrates a diffusion-based generative model with Kidera factor representations to create SpA variants. The framework comprises three modules: sequence generation, where protein sequences are encoded via Kidera factors and novel variants are generated using a diffusion model; computational screening, employing tools like AlphaFold3 to assess structural properties, solubility, and physicochemical characteristics, thereby selecting high-potential candidates; and experimental validation, involving wet-lab experiments to evaluate the biological activities and binding affinities of the designed proteins. The generated SpA variants demonstrated high success rates and strong binding affinities toward IgG. These findings confirm the effectiveness of our method in producing functional proteins comparable to natural counterparts, offering a scalable and data-efficient alternative to protein engineering.