Calculation of hydrodynamic parameters of biopolymers from scattering data using whole-body approaches

Calculation of hydrodynamic parameters of biopolymers from scattering data using whole-body approaches
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使用全身方法根据散射数据计算生物聚合物的流体动力学参数

DOI:
10.1007/bfb0118014
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发表时间:
1997
期刊:
The Biochemical journal
影响因子:
--
通讯作者:
P. Zipper
P. Zipper
中科院分区:
--
文献类型:
--
作者:
H. Durchschlag;P. Zipper

文献摘要

被引文献

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给出了一种将溶液散射与流体动力学参数联系起来的计算方法。生物聚合物是用全身方法模拟的,用球体或扁平/扁平的旋转椭球来近似它们的整体形状。摩尔质量、偏比体积、回转半径、体积和表面积体积比被用来预测各种生物聚合物的沉积、扩散系数和特性粘度,此外还推导了几个进一步的参数,如摩擦系数、Simha因子、斯托克斯和粘度半径。建立一套全面的结构和水动力数据,包括若干相关性,可以检查观测和预测的参数。在此背景下,一些经验关系的有效性也得到了检验。研究了各种不同摩尔质量和形状的粗略球状生物聚合物(简单和结合蛋白质、核糖核酸)。这些比较既包括正在分析的生物聚合物的自然状态,也包括因环境或连接状态的变化而发生的结构变化。在实验值和预期参数之间实现了深远的一致性。详细的误差传播计算允许仔细检查要预测的参数的准确性。
A calculation procedure is presented which relates solution scattering and hydrodynamic parameters. Biopolymers are modeled by whole-body approaches, approximating their overall shape by spheres or prolate/oblate ellipsoids of revolution. Molar masses, partial specific volumes, radii of gyration, volumes and surface-to-volume ratios are used for predicting sedimentation and diffusion coefficients and intrinsic viscosities of a variety of biopolymers, in addition to the derivation of several further parameters such as frictional coefficients, Simha factors, Stokes and viscosity radii. The establishment of a comprehensive set of structural and hydrodynamic data including several correlations allows the examination of observed and predicted parameters. In this context also the validity of some empirical relations was tested. A variety of roughly globular biopolymers (simple and conjugated proteins, ribonucleic acids) of different molar mass and shape have been examined. The comparisons comprise both the native states of the biopolymers under analysis and structural alterations in response to changes in environment or state of ligation. Far-reaching conformity between experimental values and anticipated parameters was achieved. Detailed error propagation calculations allow a close scrutiny of the accuracy of the parameters to be predicted.