Attention-based contrastive self-supervised learning model for antibody sequence-structure co-design

Abstract

In traditional antibody discovery methods, a large volume of biological samples of antibodies and antigens is required for wet lab experiments, making the process both expensive and time-consuming. However, computational methods have the potential to significantly accelerate antibody discovery by predicting antibody-antigen binding affinity, thus optimizing antibody design. While machine learning techniques have been employed to predict these interactions, most existing models rely on sequence data, which lacks critical structural information necessary for more accurate predictions. Structural data, though more informative, remains limited in availability. This study proposes a contrastive self-supervised learning model that utilizes antibody-antigen structure data to predict binding affinity. Contrastive learning has shown promise in other fields for learning effective representations from unlabelled data by distinguishing between high-affinity and low-affinity complexes. Despite the scarcity of structural data, this approach aims to improve the accuracy of binding affinity predictions while reducing data requirements. The study encompasses data preprocessing, feature extraction, model development, and evaluation, with the goal of advancing the efficiency and scalability of antibody-antigen interaction prediction.