Combining transformer and 3DCNN models to achieve co-design of structures and sequences of antibodies in a diffusional manner

Antibody drugs are among the fastest growing therapeutic modalities in modern drug research and development. Due to the huge search space of antibody sequences, the traditional experimental screening method cannot fully meet the needs of antibody discover. More and more rational design methods have been proposed to improve the success rate of antibody drugs. In recent years, artificial intelligence methods have increasingly become an important means of rational design. We have proposed an algorithm for antibody design, called AlphaPanda (AlphaFold2 inspired Protein-specific antibody design in a diffusional manner). The algorithm mainly combines the transformer model, the 3DCNN model and the diffusion generative model, use the transformer model to capture the global information and uses the 3DCNN model to capture the local structural characteristics of the antibody-antigen complexes, and then uses the diffusion model to generate sequences and structures of antibodies. The 3DCNN model can capture pairwise interactions in antibody-antigen complex, as well as non-pairwise interactions in antibody-antigen complex, and it requires less training sample data, while avoiding the defects of the generation progress by the autoregressive model and by the self-consistent iterative model. Diffusion generative model can generate sequence and structure effectively and with high quality. By combining 3DCNN method and diffusion model method, we have achieved the integration of 3DCNN model to the protein design with flexible main chains. By utilizing the advantages of these aspects, a good performance has been achieved by the AlphaPanda algorithm. The algorithm we propose can not only be applied to antibody design, but also be more widely applied to various fields of other protein design. The source code can be get from github (https://github.com/YueHuLab/AlphaPanda).