AlphaFold can be used to predict the oligomeric states of proteins

Homooligomerisation is a prevalent and important process that many proteins undergo to form the quaternary structures required for biological function. However, determining oligomeric states and structures experimentally remains technically challenging and time-consuming for many proteins. Here, we show that the protein structure prediction tools AlphaFold2-Multimer and AlphaFold3 can be used to quickly and accurately predict oligomeric states and structures for a range of soluble and membrane proteins. Using a benchmark set of 40 proteins, we provide optimal parameters for minimizing computational cost while maintaining accuracy. We further extended this analysis to a large dataset of over 1,000 proteins using AlphaFold2-Multimer and observe comparable overall performance but find that accurate oligomeric state prediction remains challenging for proteins that lack close structural representatives in the AlphaFold training set. Together, our results suggest both the utility and current limitations of AlphaFold-based oligomeric state prediction, highlight cases where multiple physiologically relevant assemblies may be plausible, and provide practical guidance for applying these methods to proteins lacking experimental structural data.