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基于分子动力学模拟和结合自由能计算的高活性 c-Met 抑制剂的 3D-QSAR 辅助设计。

3D-QSAR-aided design of potent c-Met inhibitors using molecular dynamics simulation and binding free energy calculation.

机构信息

a Department of Biomedical Sciences, College of Medicine , Chosun University , Gwangju 501-759 , Republic of Korea.

b Department of Cellular·Molecular Medicine, College of Medicine , Chosun University , Gwangju 501-759 , Republic of Korea.

出版信息

J Biomol Struct Dyn. 2019 May;37(8):2165-2178. doi: 10.1080/07391102.2018.1479309. Epub 2018 Nov 1.

DOI:10.1080/07391102.2018.1479309
PMID:30044205
Abstract

Mesenchymal-epithelial transition factor (c-Met) is a member of receptor tyrosine kinase. It involves in various cellular signaling pathways which includes proliferation, motility, migration, and invasion. Over-expression of c-Met has been reported in various cancers. Hence, it is an ideal therapeutic target for cancer. The main objective of the study is to identify crucial residues involved in the inhibition of c-Met kinase and to design a series of potent imidazo [4,5-b] pyrazine derivatives as c-Met inhibitors. Docking was used to identify important active site residues involved in the inhibition of c-Met kinase which was further validated by 100 ns of molecular dynamics simulation and free energy calculation using molecular mechanics generalized born surface area. Furthermore, binding energy decomposition identified that residues Tyr1230, Met1211, Asp1222, Tyr1159, Met1160, Val1092, Ala1108, and Leu1157 contributed favorably to the binding stability of compound 32. Receptor-guided Comparative Molecular Field Analysis (CoMFA) (q = 0.751, NOC = 6, r = 0.933) and Comparative Molecular Similarity Indices Analysis (COMSIA) (q = 0.744, NOC = 6, r = 0.950) models were generated based on the docked conformation of the most active compound 32. The robustness of these models was tested using various validation techniques and found to be predictive. The results of CoMFA and CoMSIA contour maps exposed the regions favorable to enhance the activity. Based on this information, 27 novel c-Met inhibitors were designed. These designed compounds exhibited potent activity than the most active compound of the existing dataset. Communicated by Ramaswamy H. Sarma.

摘要

间质上皮转化因子(c-Met)是受体酪氨酸激酶家族的一员。它涉及多种细胞信号通路,包括增殖、运动、迁移和侵袭。已有报道称,c-Met 在各种癌症中过度表达。因此,它是癌症治疗的理想靶点。本研究的主要目的是确定参与抑制 c-Met 激酶的关键残基,并设计一系列有效的咪唑[4,5-b]吡嗪衍生物作为 c-Met 抑制剂。对接用于鉴定参与抑制 c-Met 激酶的重要活性位点残基,并用 100ns 的分子动力学模拟和分子力学广义 Born 表面积自由能计算进一步验证。此外,结合能分解确定残基 Tyr1230、Met1211、Asp1222、Tyr1159、Met1160、Val1092、Ala1108 和 Leu1157 有利于化合物 32 的结合稳定性。基于对接构象,生成了受体指导的比较分子场分析(CoMFA)(q = 0.751,NOC = 6,r = 0.933)和比较分子相似性指数分析(CoMSIA)(q = 0.744,NOC = 6,r = 0.950)模型。使用各种验证技术测试这些模型的稳健性,发现它们具有预测能力。CoMFA 和 CoMSIA 等高线图的结果揭示了有利于提高活性的区域。基于这些信息,设计了 27 种新型 c-Met 抑制剂。这些设计的化合物表现出比现有数据集最活跃化合物更强的活性。

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