  Protein-protein interactions are central mediators in many biological
processes. Accurately predicting the effects of mutations on interactions is
crucial for guiding the modulation of these interactions, thereby playing a
significant role in therapeutic development and drug discovery. Mutations
generally affect interactions hierarchically across three levels: mutated
residues exhibit different sidechain conformations, which lead to changes in
the backbone conformation, eventually affecting the binding affinity between
proteins. However, existing methods typically focus only on sidechain-level
interaction modeling, resulting in suboptimal predictions. In this work, we
propose a self-supervised multi-level pre-training framework, ProMIM, to fully
capture all three levels of interactions with well-designed pretraining
objectives. Experiments show ProMIM outperforms all the baselines on the
standard benchmark, especially on mutations where significant changes in
backbone conformations may occur. In addition, leading results from zero-shot
evaluations for SARS-CoV-2 mutational effect prediction and antibody
optimization underscore the potential of ProMIM as a powerful next-generation
tool for developing novel therapeutic approaches and new drugs.
