Revealing the interaction modes of 5-HT2A receptor antagonists and the Structure-Based virtual screening from FDA and TCMNP database

Abstract

5-hydroxytryptamine 2A (5-HT2A) receptor is emerging as an important target for numerous psychoactive drugs due to its imperative roles in psychological diseases. In fact, multiple 5-HT2A receptor antagonists were developed to treat numerous psychiatric disorders, however, their clinical outcome was far from ideal probably due to a blurry information of the exact interaction modes between the receptor and its antagonists. Impressively, with a recent release of its crystal structure, we carefully analyzed the receptor-ligand interactions with Protein Contacts Atlas, structure-based pharmacophore models, and molecular dynamics (MD) simulations to sum up the chemical features for antagonists interacting with 5-HT2A receptor. Moreover, the molecular docking-based virtual screening was applied to discover potential 5-HT2A receptor antagonists from FDA and TCMNP databases. Intriguingly, after a systematic assessment of the docking scores, binding modes and free energies, as well as their MD simulations performances, three compounds in TCMNP database were highlighted to be potential 5-HT2A receptor antagonists. Fascinatedly, these three hits also exhibited highly binding affinities with dopamine D2 receptor (D2R) due to the similarity of the ligand binding pockets of the receptors, indicating them to be promising dual target molecules that are of great benefit for anti-psychotic-drug research and development. In addition, ADME/Tox predictions were conducted for a primary evaluation of their developing potential. Together, this study not only revealed the exact interaction modes between 5-HT2A receptor and its antagonists, which shed a light on a better access for developing its novel antagonists, but also provided promising dual D2 and 5-HT2A receptor antagonists.

Communicated by Ramaswamy H. Sarma

Keywords:

• 5-HT2A receptor
• molecular dynamics simulation
• virtual screening
• MM-GBSA
• ADMET prediction

Disclosure statement

The authors have declared no conflict of interest.

Additional information

Funding

The work was financially supported by the Program for Innovative Talents of Higher Education of Liaoning (2012520005), virtual educational center of medicinal chemistry in Liaoning Province and Natural Science Foundation of Liaoning Province (2019-ZD-0455).