Targeting the Aryl Hydrocarbon Receptor (AhR): A Review of the In-Silico Screening Approaches to Identify AhR Modulators

Abstract

Aryl hydrocarbon receptor (AhR) is a biological sensor that integrates environmental, metabolic, and endogenous signals to control complex cellular responses in physiological and pathophysiological functions. The full-length AhR encompasses various domains, including a bHLH, a PAS A, a PAS B, and transactivation domains. With the exception of the PAS B and transactivation domains, the available 3D structures of AhR revealed structural details of its subdomains interactions as well as its interaction with other protein partners. Towards screening for novel AhR modulators homology modeling was employed to develop AhR-PAS B domain models. These models were validated using molecular dynamics simulations and binding site identification methods. Furthermore, docking of well-known AhR ligands assisted in confirming these binding pockets and discovering critical residues to host these ligands. In this context, virtual screening utilizing both ligand-based and structure-based methods screened large databases of small molecules to identify novel AhR agonists or antagonists and suggest hits from these screens for validation in an experimental biological test. Recently, machine-learning algorithms are being explored as a tool to enhance the screening process of AhR modulators and to minimize the errors associated with structure-based methods. This chapter reviews all in silico screening that were focused on identifying AhR modulators and discusses future perspectives towards this goal.