Interpretable antibody-antigen interaction prediction by introducing route and priors guidance

Abstract

With the application of personalized and precision medicine, more precise and efficient antibody drug development technology is urgently needed. Identification of antibody-antigen interactions is key to antibody engineering. The time-consuming and expensive nature of wet-lab experiments calls for efficient computational methods. Previous deep-learning-based computing methods for antibody-antigen interaction prediction are distinctly divided into two categories: structure-based and sequence-based. Taking into account the non-overlapping advantage of these two major categories, we propose an interpretable antibody-antigen interaction prediction method, S3AI, that bridges structures to sequences through structural information distillation. Furthermore, non-covalent interactions are modeled explicitly to guide neural networks in understanding the underlying patterns in antigen-antibody docking. Supported by the two innovative designs mentioned above, S3AI significantly and comprehensively surpasses the state-of-the-art models. S3AI maintains excellent robustness when predicting unknown antibody-antigen pairs, surpassing specialized prediction methods designed for out-of-distribution generalization in fair comparisons. More importantly, S3AI captures the universal pattern of antibody-antigen interactions, which not only identifies the CDRs responsible for specific binding to the antigen but also unearthed the importance of CDR-H3 for the interaction. The implicit introduction of knowledge of structure modality and the explicit modeling of chemical constraints build a 'sequence-to-function' route, thereby facilitating S3AI's understanding of complex molecular interactions through providing route and priors guidance. S3AI, which does not require structure input, is suitable for large-scale, parallelized antibody optimization and screening while outperforming state-of-the-art prediction methods. It helps to quickly and accurately identify potential candidates in the vast antibody space, thereby accelerating the development process of antibody drugs.