To the Editor—Understanding the impact that missense mutations have on protein structure helps to reveal their biological effects. Although the structural prediction algorithm of AlphaFold2 is able to predict wild-type (WT) structures to high accuracy, it seems to fall short in predicting the impact of missense mutations on the three-dimensional (3D) structures of proteins.

In a landmark achievement, the half-century goal of predicting protein structure from an amino-acid sequence was accomplished by AlphaFold2 (ref. 1), a deep-learning algorithm that relies on homology and experimental structures in the Protein Data Bank (PDB). The program and its predictions for 98.5% of the human proteome are publicly available 2. However, one potential limitation is the insensitivity of AlphaFold2 to structure-disrupting mutations in an input sequence. This is because a database of structure-disrupting mutations does not exist, and AlphaFold2 therefore primarily bases its predictions on WT or homologous sequences instead. This drawback is important because missense mutations frequently associate with human diseases and single amino-acid mutations can lead to protein aggregation, misfolding and dysfunction. Being able to predict the effect of mutations of interest on the 3D structure of proteins will help structural biologists and non-structural biologists alike make informed hypotheses about their mechanisms of pathogenicity. Here, we comment on the implications of this AlphaFold2 limitation using three illustrative examples of selected domains for which experimental data for both WT and structure-disrupting mutations are available. The examples include ubiquitin-associated domains (UBAs) of a human Rad23 protein (hHR23a), BRCT (breast cancer 1 (BRCA1) C-terminal) repeats of BRCA1, and the actin motor protein Myosin VI MyUb domain. We provide comparisons of the AlphaFold2-predicted structural models of known mutants of these domains to their WT counterparts.