eLife Assessment
This fundamental study introduces a new biology-informed strategy for deep learning models aiming to predict mutational effects in antibody sequences. It provides convincing evidence that separating selection from the nucleotide-level mutation process improves performance over the objectives of protein language models inspired by natural language processing. This paper should be of interest to computational immunologists, but also to the broader community interested in deep learning for biological sequence data and evolution.