DG-Affinity: predicting antigen-antibody affinity with language models from sequences

BackgroundAntibody-mediated immune responses play a crucial role in the immune defense of human body. The evolution of bioengineering has led the progress of antibody-derived drugs, showing promising efficacy in cancer and autoimmune disease therapy. A critical step of this development process is obtaining the affinity between antibodies and their binding antigens.ResultsIn this study, we introduce a novel sequence-based antigen-antibody affinity prediction method, named DG-Affinity. DG-Affinity uses deep neural networks to efficiently and accurately predict the affinity between antibodies and antigens from sequences, without the need for structural information. The sequences of both the antigen and the antibody are first transformed into embedding vectors by two pre-trained language models, then these embeddings are concatenated into an ConvNeXt framework with a regression task. The results demonstrate the superiority of DG-Affinity over the existing structure-based prediction methods and the sequence-based tools, achieving a Pearson's correlation of over 0.65 on an independent test dataset.ConclusionsCompared to the baseline methods, DG-Affinity achieves the best performance and can advance the development of antibody design. It is freely available as an easy-to-use web server at https://www.digitalgeneai.tech/solution/affinity.

Automated summary

This study introduces a novel sequence-based antigen-antibody affinity prediction method, DG-Affinity, which utilizes deep neural networks to accurately predict the affinity between antibodies and antigens from sequences without structural information. DG-Affinity outperforms existing structure-based prediction methods and sequence-based tools, achieving a Pearson's correlation of over 0.65 on an independent test dataset, demonstrating its potential to advance antibody design.