Antigen-aware Deep Learning Model for Antibody Sequence Generation.

Abstract

Advancements in artificial intelligence over the last decade have transformed numerous fields, including biotechnology. Recent developments in deep learning (DL) have led to the creation of models capable of generating antibody sequences with remarkable efficiency. These models, built on cutting-edge NLP-based architectures and trained on extensive datasets of protein sequences, harness the inherent information encoded in protein sequences, from structural conformations to binding affinities. By leveraging deep learning, these methods can potentially reduce the reliance on traditional, resource-intensive experimental procedures for antibody development. However, antigen-specific antibody sequence generation is still a problem that needs to be addressed. In this study, AbAtT5, a finetuned transformer-based model specifically designed for generating antigen-specific antibodies using full-length antigen sequences is introduced. AbAtT5 is finetuned on a large protein language model, protT5, harnessing the potential of transfer learning by updating the weights and biases of the pre-trained model. AbAtT5 demonstrated superior performance compared to existing models like HERN and EAGLE, achieving improvements of up to 1.88% in VAAR and 18.04% in SeqID. These findings underscore the model's potential to accelerate the antibody design process by providing more accurate sequence generation. The ability of AbAtT5 to generate antibodies with higher sequence identity and alignment rates highlights its promise as a powerful tool in the field of computational antibody generation, offering a more efficient approach to identifying potent antigen-specific antibody candidates.