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

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 experimental receptors with docked ligands resulted in a ~5% drop, similar to that observed with AlphaFold3 models (~5%), despite the latter offering lower ligand root mean square deviation. However, using AlphaFold2 receptors with docking led to a greater performance loss (10%-20%), comparable to homology models with high sequence identity. Homology models based on low-identity templates showed over 30% decline. These performance differences were most pronounced for interface mutations and low molecular weight ligands. While AlphaFold models offer accurate protein and interaction predictions, they lack mutation-specific information, such as dynamic changes, highlighting the need for complementary mutation-aware methods for reliable analysis. Our findings provide insights into interpreting mutation effects on ligand binding using predicted structures and can guide more robust assessments of drug resistance mechanisms in silico.