Molecular-Dynamics Simulation of Serotonin Receptor Subtypes and Ligand Binding Mode Analysis

• Main Authors: Jing-Yeh Chen, 陳敬業
• Other Authors: 林榮信
• Format: Others
• Language: en_US
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/73406663777243031634

碩士 === 臺灣大學 === 藥學研究所 === 98 === An ideal serotonergic agent should not only distinguish its eponymous counterparts from other GPCRs, but also discriminate between subtypes. To achieve higher selectivity it is desirable to take a structure-based approach, and constructing in silico models by utilizing other GPCR structures is currently the only option for serotonin receptors. In this work, multiple alignment was conducted across class A human GPCR sequences for guiding homology (de novo) model constructions. Using the crystal structures of rhodopsin and β2-adrenergic receptor being major templates, seven distinct serotonin receptor subtypes 5-HT2A~5-HT2C and 5-HT4~5-HT7) were built. Selective 5-HT receptor inhibitors were chosen, structurally optimized by quantum-chemical calculations, and docked into the binding pockets. Each apo-and bound-form receptor was then immersed into a lipid bilayer/water environment and subjected to nanosecond-scale molecular dynamics (MD) simulations. Based on both induced-fit and conformational selection hypotheses, favorable ligand binding mode(s) were sampled either by ligand-bound form MD simulations, which mimicked the induced-fit process upon binding, or by relaxed complex scheme (RCS), which captured various conformers from MD snapshots to be "selected" by massive dockings. The poses were then evaluated and ranked by scoring functions. Instead of exploring through all the subtypes exhaustively, the objective was first aimed on elucidating the molecular mechanism contributing to selectivity between 5-HT2A and 5-HT7 subtypes. By analyzing the "molecular switches" of each receptor, the orientation of key residues, and the occupied volumes by drugs, several key components confering selectivity were found. The aforementioned process is applicable to other serotonin subtypes as well as non-serotonergic GPCRs, such as dopamine receptors and opoid receptors. Automated implementation is also a feasible option, and can provide a virtual screening platform which accounts for the flexibility on both ligands and receptors.