Systematic evaluation of computational tools to predict the effects of mutations on protein-ligand binding affinity in the absence of experimental structures

Abstract

Drug resistance caused by mutations is a significant global health concern. One way to better understand this phenomenon is by studying changes in protein-ligand binding affinity upon mutation. While recent advances in protein modelling, such as AlphaFold2 and AlphaFold3, have transformed structural assessments, their utility in predicting mutation-induced binding affinity changes remains underexplored. We evaluated various mutation-based methods and scoring functions using computer-generated protein-ligand complexes. Compared to a baseline using experimental structures, we observed a performance drop ranging from 5% to 30% across different computational models. Specifically, using experimen..