Recent years have seen remarkable gains in the accuracy of atomistic molecular dynamics (MD) simulations of intrinsically disordered proteins (IDPs) and expansion in the types of calculated properties that can be directly compared with experimental measurements. These advances occurred due to the use of IDP-tested force fields and the porting of MD simulations to GPUs and other computational technologies. All-atom MD simulations are now explaining the sequence-dependent dynamics of IDPs; elucidating the mechanisms of their binding to other proteins, nucleic acids, and membranes; revealing the modes of drug action on them; and characterizing their phase separation. Artificial intelligence (AI) and machine learning (ML) are further expanding the reach of atomistic MD simulations.
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