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Rigidity analysis of protein structures and rapid simulations of protein motion

机译:蛋白质结构的刚性分析和蛋白质运动的快速模拟

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摘要

It is a common goal in biophysics to understand protein structural properties and their relationship to protein function. I investigated protein structural properties using three coarse graining methods: a rigidity analysis method First, a geometric simulation method Froda and normal mode analysis as implemented in Elnemo to identify the protein directions of motion. Furthermore, I also compared the results between the coarse graining methods with the results from molecular dynamics and from experiments that I carried out. The results from the rigidity analysis across a set of protein families presented in chapter 3 highlighted two different patterns of protein rigidity loss, i.e. "sudden" and "gradual". It was found that theses characteristic patterns were in line with the rigidity distribution of glassy networks. The simulations of protein motion by merging flexibility, rigidity and normal mode analyses presented in chapter 4 were able to identify large conformational changes of proteins using minimal computational resources. I investigated the use of RMSD as a measure to characterise protein motion and showed that, despite it is a good measure to identify structural differences when comparing the same protein, the use of extensive RMSD better captures the extend of motion of a protein structure. The in-depth investigation of yeast PDI mobility presented in chapter 5 confirmed former experimental results that predicted a large conformational change for this enzyme. Furthermore, the results predicted: a characteristic rigidity distribution for yeast PDI, a minimum and a maximum active site distance and a relationship between the energy cutoff, i.e. the number of hydrogen bonds part of the network of bonds, and protein mobility. The results obtained were tested against molecular dynamics simulations in chapter 6. The MD simulation also showed a large conformational change for yeast PDI but with a slightly different minimum and maximum inter-cysteine distance. Furthermore, MD was able to reveal new data, i.e. the most likely inter-cysteine distance. In order to test the accuracy of the coarse graining and MD simulations I carried out cross-linking experiments to test the minimum inter-cysteine distance predictions. The results presented in chapter 7 show that human PDI minimum distance is below 12Å whereas the yeast PDI minimum distance must be above 12Å as no cross-linking structures where found with the available (12Å long) cross-linkers.
机译:了解蛋白质的结构特性及其与蛋白质功能的关系是生物物理学的共同目标。我使用三种粗粒度方法研究了蛋白质的结构特性:刚性分析方法首先,在Elnemo中实施的几何模拟方法Froda和法线模式分析,以识别蛋白质的运动方向。此外,我还将粗粒化方法的结果与分子动力学和我进行的实验的结果进行了比较。第3章介绍的一组蛋白质家族的刚度分析结果突出了两种不同的蛋白质刚度损失模式,即“突然”和“逐渐”。发现这些特征图案与玻璃网络的刚度分布一致。通过结合第四章介绍的灵活性,刚度和正常模式分析进行的蛋白质运动模拟,可以使用最少的计算资源来识别蛋白质的大构象变化。我调查了将RMSD用作表征蛋白质运动的一种方法,结果表明,尽管这是比较相同蛋白质时识别结构差异的好方法,但广泛使用RMSD可以更好地捕获蛋白质结构的运动。第5章对酵母PDI迁移性进行了深入研究,证实了以前的实验结果,该结果预测了该酶的构象变化很大。此外,结果预测:酵母PDI的特征刚度分布,最小和最大活性位点距离以及能量截止(即键网络中氢键部分的数量)与蛋白质迁移率之间的关系。在第六章中针对分子动力学模拟测试了获得的结果。MD模拟还显示出酵母PDI的构象变化较大,但最小和最大半胱氨酸间距离略有不同。此外,MD能够揭示新数据,即最可能的半胱氨酸间距离。为了测试粗粒度和MD模拟的准确性,我进行了交联实验以测试最小的半胱氨酸间距离预测。第7章介绍的结果表明,人PDI的最小距离低于12Å,而酵母PDI的最小距离必须高于12Å,因为没有可用的(12Å长)交联剂形成交联结构。

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