Resolving the ambiguous binding site of quercetin at the calcineurin subunit junction using funnel metadynamics with deep learning collective variables.

Calcineurin represents a prominent target for immunosuppressive drugs, where conventional macrocyclic inhibitors utilize an immunophilin-dependent mechanism for their inhibition but are consequently hindered by adverse effects and variable pharmacokinetics. The flavonoid quercetin has been shown to inhibit calcineurin in a non-competitive, immunophilin-independent manner, but its exact binding site remains ambiguous at the junction between calcineurin subunits A and B, with the open solvent-exposed nature of this region proving challenging to model. In this study, we employ funnel metadynamics with collective variables derived from a deep learning model to identify the binding site of quercetin. A selective strategy using multiple simulations with stricter funnel definitions and a smaller number of carefully chosen descriptors for model training proved more effective than a broad-based approach. These simulations were able to effectively distinguish the binding site of quercetin from three experimentally suggested sites, with a calculated free energy of binding of -8.36 ± 0.60 kcal/mol showing excellent agreement with experiment. Ligand-tryptophan distances similarly corroborated measurements from FRET assays with a r