Uncovering the unique characteristics of different groups of 5-HT5AR ligands with reference to their interaction with the target protein.

Abstract

Background: The serotonin 5-HT_5A receptor has attracted much more research attention, due to the therapeutic potential of its ligands being increasingly recognized, and the possibilities that lie ahead of these findings. There is a growing body of evidence indicating that these ligands have procognitive, pro-social, and anti-depressant properties, which offers new avenues for the development of treatments that could address socially important conditions related to the malfunctioning of the central nervous system. The aim of our study was to unravel the molecular determinants for 5-HT_5AR ligands that govern their activity towards the receptor.

Methods: In response to the need for identification of molecular determinants for 5-HT_5AR activity, we prepared a comprehensive collection of 5-HT_5AR ligands, carefully gathering literature and patent data. Leveraging molecular modeling techniques, such as pharmacophore hypothesis development, docking, and molecular dynamics simulations enables to gain valuable insights into the specific interactions of 5-HT_5AR ligand groups with the receptor.

Results: The obtained comprehensive set of 2160 compounds was divided into dozens of subsets, and a pharmacophore model was developed for each group. The results from the docking and molecular dynamics simulations have enabled the identification of crucial ligand-protein interactions that are essential for the compound's activity towards 5-HT_5AR.

Conclusions: The findings from the molecular modeling study provide valuable insights that can guide medicinal chemists in the development of new 5-HT_5AR ligands. Considering the pharmacological significance of these compounds, they have the potential to become impactful treatments for individuals and communities in the future. Understanding how different crystal/cryo-EM structures of 5-HT_5AR affect molecular modeling experiments could have major implications for future computational studies on this receptor.