Protein A-like peptide generation based on generalized diffusion model.

With the rapid advancement of biotechnology, protein generation and design based on generative models have demonstrated extensive applications in drug development, vaccine research, and biocatalysis. This research proposes a protein generation method based on the generalized diffusion model, which breaks through the traditional diffusion model's reliance on Gaussian noise, enables more flexible protein sequence generation, and preliminarily verifies its advantages. Specifically, protein sequences were first encoded using one-hot encoding and input into the diffusion model to generate novel sequences. Subsequently, the tertiary structures of the generated proteins were predicted using AlphaFold, followed by structural alignment and backbone distance calculation via PyMOL to select the optimal sequences. The predicted derivative protein sequence A_005 was screened from the generated sequences and subjected to an affinity assay with Protein A parental. Experimental results revealed that A_005 exhibited remarkably high affinity, as well as a satisfactory dissociation rate and association rate. The findings demonstrate that the protein generation method based on the generalized diffusion model can effectively design protein sequences with high structural and functional similarity to target sequences. While prior studies have shown that both DDPM and generalized diffusion models achieve high generation quality, the generalized diffusion model outperforms in terms of task adaptability. Our research not only opens new technological pathways for protein design but also lays a solid foundation for future applications in biomedicine, providing significant theoretical and experimental evidence for subsequent drug development.