Prediction of SHP2-E76K binding sites based on molecular dynamics simulation and Markov algorithm.

IF 2.7 3区 生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY Journal of Biomolecular Structure & Dynamics Pub Date : 2024-11-19 DOI:10.1080/07391102.2024.2431193
Si-Pei Zhang, Li-Juan Chen, Zhen-Liang Shi, Xin Li, Ying Ma
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Abstract

SHP2-E76K, a mutant encoded by the PTPN11 gene, was associated with various solid tumors, such as lung cancer, glioblastoma, and intellectual disability. SHP2-E76K has become potential drug targets, while there was no effective inhibitor against the mutant currently. At present, the crystal complex structure of SHP099 with SHP2-E76K has been reported in the RCSB PDB protein data bank, however, the dynamic structure of SHP099 binding to the active center of SHP2-E76K protein was still lacking. Therefore, this study used molecular dynamics simulation and Markov model to characterize the kinetics of the inhibitor SHP099 with SHP2-E76K enzyme and to determine the active binding site, which would give a hint of a vital enzyme-substrate interaction in atomistic detail that proposed the potential to be applied for the discovery of more effective SHP2-E76K inhibitors and, in broader terms, dynamic protein-drug interactions.

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基于分子动力学模拟和马尔可夫算法的SHP2-E76K结合位点预测
SHP2-E76K是由PTPN11基因编码的一种突变体,与肺癌、胶质母细胞瘤等多种实体瘤和智力残疾有关。SHP2-E76K已成为潜在的药物靶点,但目前还没有针对该突变体的有效抑制剂。目前,SHP099与SHP2-E76K的晶体复合物结构已在RCSB PDB蛋白数据库中报道,但仍缺乏SHP099与SHP2-E76K蛋白活性中心结合的动态结构。因此,本研究采用分子动力学模拟和马尔可夫模型来描述抑制剂SHP099与SHP2-E76K酶的动力学特性,并确定其活性结合位点,这将从原子细节上揭示酶与底物之间的重要相互作用,有望应用于发现更有效的SHP2-E76K抑制剂,以及更广泛意义上的动态蛋白质-药物相互作用。
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来源期刊
Journal of Biomolecular Structure & Dynamics
Journal of Biomolecular Structure & Dynamics 生物-生化与分子生物学
CiteScore
8.90
自引率
9.10%
发文量
597
审稿时长
2 months
期刊介绍: The Journal of Biomolecular Structure and Dynamics welcomes manuscripts on biological structure, dynamics, interactions and expression. The Journal is one of the leading publications in high end computational science, atomic structural biology, bioinformatics, virtual drug design, genomics and biological networks.
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