The k off values of ligands unbinding to proteins are key parameters for drug discovery. Their predictions based on molecular simulation may under- or overestimate experiment in a system- and/or technique-dependent way. Here we use an established methodinfrequent metadynamics, based on the AMBER force fieldto compute the k off of the ligand iperoxo (in clinical use) targeting the muscarinic receptor M2. The ligand charges are calculated by either (i) the Amber standard procedure or (ii) B3LYP-DFT. The calculations using (i) turn out not to provide a reasonable estimation of the transition-state free energy. Those using (ii) differ from experiment by 2 orders of magnitude. On the basis of B3LYP DFT QM/MM simulations, we suggest that the observed discrepancy in (ii) arises, at least in part, from the lack of electronic polarization and/or charge transfer in biomolecular force fields. These issues might be present in other systems, such as DNA–protein complexes.

Native electrospray ionization/ion mobility-mass spectrometry (ESI/IM-MS) allows an accurate determination of low-resolution structural features of proteins. Yet, the presence of proton dynamics, observed already by us for DNA in the gas phase, and its impact on protein structural determinants, have not been investigated so far. Here, we address this issue by a multistep simulation strategy on a pharmacologically relevant peptide, the N-terminal residues of amyloid-β peptide (Aβ(1-16)). Our calculations reproduce the experimental maximum charge state from ESI-MS and are also in fair agreement with collision cross section (CCS) data measured here by ESI/IM-MS. Although the main structural features are preserved, subtle conformational changes do take place in the first ∼0.1 ms of dynamics. In addition, intramolecular proton dynamics processes occur on the picosecond-time scale in the gas phase as emerging from quantum mechanics/molecular mechanics (QM/MM) simulations at the B3LYP level of theory. We conclude that proton transfer phenomena do occur frequently during fly time in ESI-MS experiments (typically on the millisecond time scale). However, the structural changes associated with the process do not significantly affect the structural determinants.

The dissociation rate (koff) associated with ligand unbinding events from proteins is a parameter of fundamental importance in drug design. Here we review recent major advancements in molecular simulation methodologies for the prediction ofkoff. Next, we discuss the impact of the potential energy function models on the accuracy of calculatedkoffvalues. Finally, we provide a perspective from high-performance computing and machine learning which might help improve such predictions.