Global analysis of sedimentation velocity data sets from multiwavelength analytical ultracentrifugation experiments using enhanced regularisation techniques

Abstract

Multiwavelength sedimentation velocity analytical ultracentrifugation experiments are a powerful technique for the simultaneous analysis of the hydrodynamic and optical properties of macromolecules and particles in solution. A fast and accurate analysis of these data sets is an important prerequisite for further investigation of the disperse and spectral properties of the sample, such as the examination of the structure-property function of nanoparticles or the spectral properties of mixtures with multiple species having different extinction or emission values. Various strategies exist for the analysis of these data sets. However, the techniques and tools presented here significantly extend the current analysis methods, since they enable a global analysis of multiwavelength data sets based on a direct boundary modelling approach that can be performed on a standard desktop computer. The distributions of sedimentation coefficients can be regularised in the sedimentation coefficient dimension, whereby the dependency of the regularisation parameter on the wavelength is taken into account. Furthermore, the method enables regularisation of the determined sedimentation coefficient distributions in the wavelength dimension, which avoids the distribution broadening due to regularisation in the sedimentation coefficient dimension. Consequently, the distribution can be determined with higher precision, particularly for narrowly distributed samples. Additionally, the frictional ratio or the partial specific volume can be determined based on a global fit that considers not just one wavelength but rather incorporates a selected range of wavelengths, thereby providing an increased accuracy of the determined parameter, especially for samples with multiple species having different wavelength-dependent extinction coefficients. Our tool and the algorithms implemented were tested and validated using synthetic data sets with known input parameters. Finally, the possibilities arising from the global multidimensional characterisation of dispersed systems are demonstrated for experimental data for several proteins as well as silver and gold nanoparticles. In addition, comparisons are made to state-of-the-art AUC data analysis software.