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Enhanced sampling methods can predict free-energy landscapes associated with protein/ligand binding, characterizing the involved intermolecular interactions in a precise way. However, these in silico approaches can be challenged by induced-fit effects. Here, we present a variant of volume-based metadynamics tailored to tackle this problem in a general and efficient way. The validity of the approach is established by applying it to substrate/enzyme complexes of pharmacological relevance: mono-ADP-ribose (ADPr) in complex with mono-ADP-ribosylation hydrolases (MacroD1 and MacroD2), where induced-fit phenomena are known to be significant. The calculated binding free energies are consistent with experiments, with an absolute error smaller than 0.5 kcal/mol. Our simulations reveal that in all circumstances, the active loops, delimiting the boundaries of the binding site, undergo significant conformation rearrangements upon ligand binding. The calculations further provide, for the first time, the molecular basis of ADPr specificity and the relative changes in its experimental binding affinity on passing from MacroD1 to MacroD2 and all its mutants. Our study paves the way to the quantitative description of induced-fit events in molecular recognition.

Enhanced Sampling Approach to the Induced-Fit Docking Problem in Protein–Ligand Binding: The Case of Mono-ADP-Ribosylation Hydrolase Inhibitors