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使用qFADD.py对DNA损伤位点处的蛋白质积累进行自动建模。

Automated modeling of protein accumulation at DNA damage sites using qFADD.py.

作者信息

Bowerman Samuel, Mahadevan Jyothi, Benson Philip, Rudolph Johannes, Luger Karolin

机构信息

Department of Biochemistry, University of Colorado Boulder, Boulder, Colorado, USA.

Howard Hughes Medical Institute, University of Colorado Boulder, Boulder, Colorado, USA.

出版信息

Biol Imaging. 2022;2. doi: 10.1017/s2633903x22000083. Epub 2022 Aug 30.

Abstract

Eukaryotic cells are constantly subject to DNA damage, often with detrimental consequences for the health of the organism. Cells mitigate this DNA damage through a variety of repair pathways involving a diverse and large number of different proteins. To better understand the cellular response to DNA damage, one needs accurate measurements of the accumulation, retention, and dissipation timescales of these repair proteins. Here, we describe an automated implementation of the "quantitation of fluorescence accumulation after DNA damage" method that greatly enhances the analysis and quantitation of the widely used technique known as laser microirradiation, which is used to study the recruitment of DNA repair proteins to sites of DNA damage. This open-source implementation ("qFADD.py") is available as a stand-alone software package that can be run on laptops or computer clusters. Our implementation includes corrections for nuclear drift, an automated grid search for the model of a best fit, and the ability to model both horizontal striping and speckle experiments. To improve statistical rigor, the grid-search algorithm also includes automated simulation of replicates. As a practical example, we present and discuss the recruitment dynamics of the early responder PARP1 to DNA damage sites.

摘要

真核细胞经常遭受DNA损伤,这往往会对生物体的健康产生有害影响。细胞通过多种涉及大量不同蛋白质的修复途径来减轻这种DNA损伤。为了更好地理解细胞对DNA损伤的反应,需要准确测量这些修复蛋白的积累、保留和消散时间尺度。在这里,我们描述了“DNA损伤后荧光积累定量”方法的自动化实现,该方法极大地增强了对广泛使用的激光微照射技术的分析和定量,该技术用于研究DNA修复蛋白向DNA损伤位点的募集。这个开源实现(“qFADD.py”)作为一个独立的软件包提供,可以在笔记本电脑或计算机集群上运行。我们的实现包括对核漂移的校正、对最佳拟合模型的自动网格搜索,以及对水平条纹和斑点实验进行建模的能力。为了提高统计严谨性,网格搜索算法还包括对重复实验的自动模拟。作为一个实际例子,我们展示并讨论了早期反应蛋白PARP1向DNA损伤位点的募集动态。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5267/10951794/91135c1803fb/S2633903X22000083_fig1.jpg

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