受体酪氨酸激酶的错义突变如何影响组成活性和替代药物敏感性:来自分子动力学模拟的见解

Isaure Chauvot de Beauchêne, L. Tchertanov
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引用次数: 1

摘要

为了更深入地了解与正常和/或异常蛋白质功能相关的分子机制,这些机制与参与这些过程的大分子的结构和动力学密切相关,需要开展肿瘤相关的基础研究。最常见的致癌事件的起源与错义突变有关。由受体酪氨酸激酶(RTKs)的致癌突变促进的突变诱导的结构效应尚未完全表征。计算生物学完善并丰富了实验数据,为调控RTKs活性的分子机制提供了新的视角。在我们的一系列论文中,我们研究了III家族的天然rtk和突变rtk (KIT和CSF-1R)的结构和动力学特征,详细描述了它们的激活机制,依赖于天然蛋白的配体和不同突变体的组成。RTKs的激活机制描述为蛋白质的偶联调节片段、近膜区(JMR)和激活(A-)环之间的变构调节。由于一些突变促进了对临床使用药物的耐药性,我们分析了伊马替尼对这些治疗靶点的亲和力。计算获得的(计算机)数据与体内和体外观察相关联,从而验证了我们基于数值的计算。展望未来,癌症相关模型和模拟的临床验证是将计算机数据转化为临床和药理学水平的生物医学研究的基石。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
How missense mutations in receptors tyrosine kinases impact constitutive activity and alternate drug sensitivity: insights from molecular dynamics simulations
The fundamental oncology-related research is required for a deeper understanding of the molecular mechanisms associated with the normal and/or abnormal protein functions, which are closely related with structure and dynamics of the macromolecules involved in these process. The most common origin of oncogenic events is related to missense mutations. Mutation-induced structural effects promoted by oncogenic mutations in receptor tyrosine kinases (RTKs), are not yet fully characterized. Computational biology completes and enriches experimental data, producing a novel vision of molecular mechanisms governing RTKs activity. In series of our papers, we studied the structural and dynamical features of native and mutated RTKs from III family (KIT and CSF-1R), yielding a detailed description of their mechanisms of activation, ligand-depend for the native proteins and constitutive for the distinct mutants. The mechanisms of RTKs activation are described in terms of allosteric regulation between coupled regulating fragments of the protein, juxta-membrane region (JMR) and activation (A-) loop. As some mutations promote resistance to the clinically-used drugs, we analyzed the affinity of imatinib to these therapeutic targets. The computationally-obtained ( in silico ) data were correlated with in vivo and in vitro observations, thus validating our numerically-based accounts. Going forward, clinical validation of cancer-related models and simulations are cornerstones key of translation of in silico data into biomedical research, at clinical and pharmacological levels.
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