Assessing AF2's ability to predict structural ensembles of proteins

Recent breakthroughs in protein structure prediction have enhanced the precision and speed at which protein configurations can be determined. Additionally, molecular dynamics (MD) simulations serve as a crucial tool for capturing the conformational space of proteins, providing valuable insights into their structural fluctuations. However, the scope of MD simulations is often limited by the accessible timescales and the computational resources available, posing challenges to comprehensively exploring protein behaviors. Recently emerging approaches have focused on expanding the capability of AlphaFold2 (AF2) to predict conformational substates of protein. Here, we benchmark the performance of various workflows that have adapted AF2 for ensemble prediction and compare the obtained structures with ensembles obtained from MD simulations and NMR. We provide an overview of the levels of performance and accessible timescales that can currently be achieved with machine learning (ML) based ensemble generation. Significant minima of the free energy surfaces remain undetected.