Using prior knowledge in the determination of macromolecular size-distributions by analytical ultracentrifugation
Using prior knowledge in the determination of macromolecular size-distributions by analytical ultracentrifugation
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DOI：
10.1021/bm070193j
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发表时间：
2007-06-01
影响因子：
6.2
通讯作者：
Schuck, Peter
中科院分区：
文献类型：
--
作者：
Brown, Patrick H.;Balbo, Andrea;Schuck, Peter
分析超离心已经重新出现，作为一种广泛使用的工具，用于研究生物大分子的集合体，以了解例如它们在自由溶液中的尺寸分布和相互作用。这样的信息可以从溶液柱上的浓度和信号梯度的数学分析以及由于重力而产生的它们随时间的演变中获得。在沉降速度分析超浓缩中，这种分析经常使用高分辨率的扩散-去卷积沉降系数分布进行。它们是基于Fredholm积分方程，这是不适定的，除非稳定的正则化。在许多领域中，最大熵和Tikhonov-Phillips正则化都是行之有效的强大方法，可以根据奥卡姆剃刀计算出与数据和先验知识一致的最简约分布。迄今为止，在分析超浓缩法的实施中，隐含的基本假设是所有沉降系数的可能性相等，并且检索到的信息应浓缩到尽可能少的量。然而，通常情况下，更详细的分布将是由特定的详细的先验知识所研究的大分子系综，如预期的样品是单分散或少分散或预期的迁移，以建立一个双峰沉降模式的基础上吉尔伯特-詹金斯的迁移理论的化学反应系统。到目前为止，这样的先验知识在沉降系数或分子量分布的计算中仍然基本上未被使用，或者仅被用作约束。在本文中，我们研究如何事先预期可以直接建立到计算数据分析，保守的方式，荣誉的实验数据的完整信息，是否与事先预期一致。与其他领域的类似结果相一致，我们发现，使用现有的先验知识可以产生显着的影响，所得的分子量，沉降系数，和尺寸和形状分布，并可以显着增加它们的灵敏度和分辨率。此外，使用多个替代先验信息使我们能够探测与数据一致的可能解释的范围。
Analytical ultracentrifugation has reemerged as a widely used tool for the study of ensembles of biological macromolecules to understand, for example, their size-distribution and interactions in free solution. Such information can be obtained from the mathematical analysis of the concentration and signal gradients across the solution column and their evolution in time generated as a result of the gravitational force. In sedimentation velocity analytical ultracentrifugation, this analysis is frequently conducted using high resolution, diffusion-deconvoluted sedimentation coefficient distributions. They are based on Fredholm integral equations, which are ill-posed unless stabilized by regularization. In many fields, maximum entropy and Tikhonov-Phillips regularization are well-established and powerful approaches that calculate the most parsimonious distribution consistent with the data and prior knowledge, in accordance with Occam's razor. In the implementations available in analytical ultracentrifugation, to date, the basic assumption implied is that all sedimentation coefficients are equally likely and that the information retrieved should be condensed to the least amount possible. Frequently, however, more detailed distributions would be warranted by specific detailed prior knowledge on the macromolecular ensemble under study, such as the expectation of the sample to be monodisperse or paucidisperse or the expectation for the migration to establish a bimodal sedimentation pattern based on Gilbert-Jenkins' theory for the migration of chemically reacting systems. So far, such prior knowledge has remained largely unused in the calculation of the sedimentation coefficient or molecular weight distributions or was only applied as constraints. In the present paper, we examine how prior expectations can be built directly into the computational data analysis, conservatively in a way that honors the complete information of the experimental data, whether or not consistent with the prior expectation. Consistent with analogous results in other fields, we find that the use of available prior knowledge can have a dramatic effect on the resulting molecular weight, sedimentation coefficient, and size-and-shape distributions and can significantly increase both their sensitivity and their resolution. Further, the use of multiple alternative prior information allows us to probe the range of possible interpretations consistent with the data.