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基于动力学的α形状模型分析识别表皮生长因子受体(EGFR)突变诱导的耐药性。

Identifying EGFR mutation-induced drug resistance based on alpha shape model analysis of the dynamics.

作者信息

Ma Lichun, Zou Bin, Yan Hong

机构信息

Department of Electronic Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong.

出版信息

Proteome Sci. 2016 Sep 8;14(1):12. doi: 10.1186/s12953-016-0102-0. eCollection 2016.

DOI:10.1186/s12953-016-0102-0
PMID:27610045
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5015241/
Abstract

BACKGROUND

Epidermal growth factor receptor (EGFR) mutation-induced drug resistance is a difficult problem in lung cancer treatment. Studying the molecular mechanisms of drug resistance can help to develop corresponding treatment strategies and benefit new drug design.

METHODS

In this study, Rosetta was employed to model the EGFR mutant structures. Then Amber was carried out to conduct molecular dynamics (MD) simulation. Afterwards, we used Computational Geometry Algorithms Library (CGAL) to compute the alpha shape model of the mutants.

RESULTS

We analyzed the EGFR mutation-induced drug resistance based on the motion trajectories obtained from MD simulation. We computed alpha shape model of all the trajectory frames for each mutation type. Solid angle was used to characterize the curvature of the atoms at the drug binding site. We measured the knob level of the drug binding pocket of each mutant from two ways and analyzed its relationship with the drug response level. Results show that 90 % of the mutants can be grouped correctly by setting a certain knob level threshold.

CONCLUSIONS

There is a strong correlation between the geometric properties of the drug binding pocket of the EGFR mutants and the corresponding drug responses, which can be used to predict the response of a new EGFR mutant to a drug molecule.

摘要

背景

表皮生长因子受体(EGFR)突变诱导的耐药性是肺癌治疗中的一个难题。研究耐药性的分子机制有助于制定相应的治疗策略并有益于新药设计。

方法

在本研究中,使用Rosetta对EGFR突变体结构进行建模。然后利用Amber进行分子动力学(MD)模拟。之后,我们使用计算几何算法库(CGAL)来计算突变体的α形状模型。

结果

我们基于从MD模拟获得的运动轨迹分析了EGFR突变诱导的耐药性。我们计算了每种突变类型所有轨迹帧的α形状模型。立体角用于表征药物结合位点处原子的曲率。我们从两种方式测量了每个突变体药物结合口袋的旋钮水平,并分析了其与药物反应水平的关系。结果表明,通过设置一定的旋钮水平阈值,90%的突变体能够被正确分组。

结论

EGFR突变体药物结合口袋的几何性质与相应的药物反应之间存在很强的相关性,这可用于预测新的EGFR突变体对药物分子的反应。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7da0/5015241/c7830033139e/12953_2016_102_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7da0/5015241/386f1d35ce2b/12953_2016_102_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7da0/5015241/9827cbccc4eb/12953_2016_102_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7da0/5015241/79ee121d733e/12953_2016_102_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7da0/5015241/39338ee0f355/12953_2016_102_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7da0/5015241/e634272a754b/12953_2016_102_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7da0/5015241/c7830033139e/12953_2016_102_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7da0/5015241/386f1d35ce2b/12953_2016_102_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7da0/5015241/9827cbccc4eb/12953_2016_102_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7da0/5015241/79ee121d733e/12953_2016_102_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7da0/5015241/39338ee0f355/12953_2016_102_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7da0/5015241/e634272a754b/12953_2016_102_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7da0/5015241/c7830033139e/12953_2016_102_Fig6_HTML.jpg

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