Bayesian analysis of static light scattering data for globular proteins.
Bayesian analysis of static light scattering data for globular proteins.
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DOI：
10.1371/journal.pone.0258429
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发表时间：
2021
期刊：
影响因子：
3.7
通讯作者：
Butts CT
中科院分区：
文献类型：
--
作者：
Yin F;Khago D;Martin RW;Butts CT
静态光散射是一种流行的物理化学技术，可以计算溶液中大分子（例如聚合物或蛋白质）的物理属性，例如回转半径和第二维里系数。第二维里系数是一个物理量，表征粒子之间成对相互作用的大小和符号，因此与聚集倾向相关，这是一种具有相当大的科学和实际意义的属性。由于所需的精度和所涉及的误差结构的复杂性，从实验数据估计第二维里系数具有挑战性。与基于启发式普通最小二乘估计的传统方法相比，第二维里系数的贝叶斯推理允许对误差过程进行显式建模、合并先验信息以及直接测试竞争物理模型的能力。在这里，我们引入了用于小颗粒系统静态光散射实验的完全贝叶斯模型，并对浓度、折射率、低聚物尺寸和第二维里系数进行联合推断。我们应用我们提出的模型，利用内部实验数据研究鸡蛋清溶菌酶和人 γS-晶状体蛋白的聚集行为。基于这些观察，我们还对这一系列实验中不确定性的主要驱动因素进行了模拟研究，特别显示了改进浓度监测和控制以帮助推理的潜力。
Static light scattering is a popular physical chemistry technique that enables calculation of physical attributes such as the radius of gyration and the second virial coefficient for a macromolecule (e.g., a polymer or a protein) in solution. The second virial coefficient is a physical quantity that characterizes the magnitude and sign of pairwise interactions between particles, and hence is related to aggregation propensity, a property of considerable scientific and practical interest. Estimating the second virial coefficient from experimental data is challenging due both to the degree of precision required and the complexity of the error structure involved. In contrast to conventional approaches based on heuristic ordinary least squares estimates, Bayesian inference for the second virial coefficient allows explicit modeling of error processes, incorporation of prior information, and the ability to directly test competing physical models. Here, we introduce a fully Bayesian model for static light scattering experiments on small-particle systems, with joint inference for concentration, index of refraction, oligomer size, and the second virial coefficient. We apply our proposed model to study the aggregation behavior of hen egg-white lysozyme and human γS-crystallin using in-house experimental data. Based on these observations, we also perform a simulation study on the primary drivers of uncertainty in this family of experiments, showing in particular the potential for improved monitoring and control of concentration to aid inference.
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