Lasse M. Blaabjerg

The paper disentangles protein-interaction and protein-quality effects in antibody-antigen binding using deep mutational scanning and evaluates models for their ability to predict these effects.

The paper develops a framework to train supervised models on MAVE data by accounting for experimental protocol differences, improving variant effect prediction across datasets.

This paper introduces SSEmb, a model that integrates protein sequence and structure to improve variant effect predictions and downstream tasks like binding site prediction.