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Updated: Sep 14, 2025

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
Published on: June 20, 2025
Mechanistic Insights into the Binding of Different Antagonists to 5-HT1AR: A Molecular Docking and Molecular Dynamics
Lulu Guan1, Dushuo Feng1, Jingxuan Ge2
1Department of Sport and Exercise Science, College of Education, Zhejiang University, 866 Yuhangtang Road, Hangzhou, Zhejiang 310058, P. R. China.
Abstract:
The serotonin 1A receptor (5-HT1AR) is involved in a wide range of physiological processes, and it has attracted considerable attention as an important target for developing new medicines. The antagonists of 5-HT1AR display therapeutic potential in depression and cognitive dysfunction, and the elucidation of their interaction with the receptor is crucial to understand pharmacological actions and develop novel therapeutic agents. Herein, we performed conventional molecular docking and molecular dynamics (MD) simulations to address the docking pose and binding mechanisms of six representative antagonists (way100635, way101405, lecozotan, nan190, sdz216-525, and nad299) to 5-HT1AR. We found that among the six antagonists, sdz216-525 exhibited the most negative docking score at -9.5 kcal/mol, while nad299 displayed the least negative score. The common pharmacophore aromatic group appeared in all six antagonists, and piperazine existed in the five antagonists except for nad299. MD simulation results showed that upon the addition of antagonists, the conformation of 5-HT1AR was changed to various extents, and the relative positions of transmembrane 3 (TM3), TM5, and TM6 underwent rearrangement. Among these antagonists, lecozotan exhibited the highest binding affinity to 5-HT1AR, whereas nad299 showed the weakest interaction. The molecular recognition of six antagonists by 5-HT1AR involved different binding patterns, with variable contributions from hydrophobic, H-bonding, cation-π/anion-π, and aromatic stacking interactions. Taken together, our computational study contributes to the understanding of the binding mechanism of antagonists to 5-HT1AR, which may facilitate the design of new antagonists targeting 5-HT1AR.
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