Investigating the Aryl Hydrocarbon Receptor Agonist/Antagonist Conformational Switch Using Well-Tempered Metadynamics Simulations

Identification of aryl hydrocarbon receptor allosteric antagonists from clinically approved drugs

The human aryl hydrocarbon receptor (AhR), a ligand-dependent transcription factor, plays a pivotal role in a diverse array of pathways in biological and pathophysiological events. This position AhR as a promising target for both carcinogenesis and antitumor strategies. In this study we utilized computational modeling to screen and identify FDA-approved drugs binding to the allosteric site between α2 of bHLH and PAS-A domains of AhR, with the aim of inhibiting its canonical pathway activity. Our findings indicated that nilotinib effectively fits into the allosteric pocket and forms interactions with crucial residues F82, Y76, and Y137. Binding free energy value of nilotinib is the lowest among top hits and maintains stable within its pocket throughout entire (MD) simulations time. Nilotinib has also substantial interactions with F295 and Q383 when it binds to orthosteric site and activate AhR. Surprisingly, it does not influence AhR nuclear translocation in the presence of AhR agonists; instead, it hinders the formation of the functional AhR-ARNT-DNA heterodimer assembly, preventing the upregulation of regulated enzymes like CYP1A1. Importantly, nilotinib exhibits a dual impact on AhR, modulating AhR activity via the PAS-B domain and working as a noncompetitive allosteric antagonist capable of blocking the canonical AhR signaling pathway in the presence of potent AhR agonists. These findings open a new avenue for the repositioning of nilotinib beyond its current application in diverse diseases mediated via AhR.

Illuminating ligand-induced dynamics in nuclear receptors through MD simulations

Nuclear receptors (NRs) regulate gene expression in critical physiological processes, with their functionality finely tuned by ligand-induced conformational changes. While NRs may sometimes undergo significant conformational motions in response to ligand-binding, these effects are more commonly subtle and challenging to study by traditional structural or biophysical methods. Molecular dynamics (MD) simulations are a powerful tool to bridge the gap between static protein-ligand structures and dynamical changes that govern NR function. Here, we summarize a handful of recent studies that apply MD simulations to study NRs. We present diverse methodologies for analyzing simulation data with a detailed examination of the information each method can yield. By delving into the strengths, limitations and unique contributions of these tools, this review provides guidance for extracting meaningful data from MD simulations to advance the goal of understanding the intricate mechanisms by which ligands orchestrate a range of functional outcomes in NRs.