Bayesian Analysis of Static Light Scattering Data for Globular Proteins

TL;DR

This paper introduces a Bayesian statistical model for analyzing static light scattering data to accurately estimate the second virial coefficient and other parameters, improving understanding of protein aggregation.

Contribution

It presents a fully Bayesian approach that explicitly models error structures and incorporates prior information, advancing beyond traditional heuristic methods.

Findings

Bayesian model provides more accurate estimates of the second virial coefficient.

Application to lysozyme and gammaS-crystallin reveals insights into aggregation behavior.

Simulation highlights importance of concentration monitoring for reducing uncertainty.

Abstract

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 OLS 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 gammaS-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.