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基于增强采样构建的隐式马尔可夫状态模型分析GLP-1R激动剂对GLP-1R激活机制

Mechanism of activation of GLP-1R agonist on GLP-1R based on Markov model constructed by enhanced sampling
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摘要 目的 基于增强采样构建的隐式马尔可夫状态模型,分析胰高血糖素样肽1受体(GLP-1R)激动剂PF-06882961激活GLP-1R的机制。方法 从PDB数据库中下载GLP-1R晶体结构(PDBID:6X1A),基于该晶体结构构建PF06882961与GLP-1R结合的高斯加速动力学(GaMD)体系,模拟PF06882961与GLP-1R结合的动力学轨迹。使用工具包Pyemma读取PF06882961与GLP-1R结合的GaMD动力学轨迹,构建马尔可夫模型。然后分别从一级结构[关键氨基酸残基间的αC间距(Glu247-His180;Glu364-Arg190)]和二级结构[关键α螺旋间扭转角(Val365-Pro358-Ala350;Arg380-Phe390-Met397)]两个层面对构建的马尔可夫模型中PF-06882961与GLP-1R复合物若干构象进行聚类分析,得出5个结构具有差异的PF-06882961与GLP-1R复合物宏观态构象(S1、2、3、4、5),将其可视化后分析各个宏观态构象之间的结构差异,以明确PF-06882961激活GLP-1R的结构基础。结果 从二级结构层面进行聚类分析时,PF06882961与GLP-1R结合后,GLP-1R细胞外结构域部分与跨膜结构域间距离减小,GLP-1R下游的G蛋白发生了重要构象转变。从一级结构及二级结构层面进行聚类分析时,PF-06882961结合GLP-1R后,GLP-1R的跨膜结构域内关键氨基酸残基重排出新的极性网络(Glu364-Tyr241-His180-Glu247),细胞外结构域内由Phe385-Tyr203-Tyr148组成π-π堆叠网络。结论 PF-06882961与GLP-1R结合后,通过由Phe385-Tyr203-Tyr148组成的π-π堆叠网络、由Glu364-Tyr241-His180-Glu247重排而成的新极性网络分别稳定GLP-1R的细胞外结构域及跨膜结构域,从而激活GLP-1R。 Objective To investigate the mechanism of activation of glucagon-like peptide 1(GLP-1R) by GLP-1R agonist PF-06882961 based on the hidden Markov state model constructed by enhanced sampling.Methods The binding structure of GLP-1R and PF-06882961(PDBID:6X1A) was downloaded from the PDB database,and the Gaussian accelerated molecular dynamics(GaMD) system of it was constructed based on the structure to simulate its dynamic trajectory of the binding of PF06882961 to GLP-1R.The GaMD dynamics trajectory of PF06882961 and GLP-1R was read by the toolkit Pyemma to construct a hidden Markov model(HMM).Then,a cluster analysis was performed on several conformations of PF-06882961 and GLP-1R complex in the constructed Markov model from the primary structure [αC spacing between key amino acid residues(Glu247-His180;Glu364-Arg190)] and the secondary structure [torsion angle between key α helices(Val365-Pro358-Ala350;Arg380-Phe390-Met397)],and five macroscopic conformations(S1,2,3,4,5) of PF-06882961 and GLP-1R complex with different structures were obtained.After visualization,the structural differences between each macroscopic conformation were analyzed to clarify the structural basis of PF-06882961 activating GLP-1R.Results Cluster analysis at the secondary structure level showed that the distance between the extracellular domain and the transmembrane domain of GLP-1R decreased after PF06882961 binding to GLP-1R,and the downstream(G protein) of GLP-1R underwent important conformational changes.Cluster analysis at the primary structure and secondary structure levels showed that the key amino acid residues in the transmembrane domain of GLP-1R were rearranged into a new polar network(Glu364-Tyr241-His180-Glu247),and the extracellular domain was composed of π-π stacking network by Phe385-Tyr203-Tyr148 after PF06882961 binding to GLP-1R.Conclusion After PF-06882961 binding to GLP-1R,the extracellular domain and transmembrane domain of GLP-1R were stabilized by π-π stacking network composed of Phe385-Tyr203-Tyr148 and a new polar network rearranged by Glu364-Tyr241-His180-Glu247,thereby activating GLP-1R.
作者 刘一卜 汤磊 范菊娣 LIU Yibu;TANG Lei;FAN Judi(School of Pharmacy,Guizhou Medical University,Guiyang 550004,China)
出处 《山东医药》 CAS 2024年第3期44-50,共7页 Shandong Medical Journal
基金 国家自然科学基金(82060632) 中央引导地方科技发展基金(黔科中引地[2022]4017) 贵州省卫生健康委科学技术基金项目(gzwkj2022-469)。
关键词 胰高血糖素样肽1受体激动剂 PF-06882961 胰高血糖素样肽1受体 马尔可夫状态模型 高斯加速动力学 分子动力学模拟 glucagon-like peptide-1 receptor agonist PF-06882961 glucagon-like peptide-1 receptor Markov state model Gaussian accelerated molecular dynamics molecular dynamics simulation
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