In the past, we have substantially increased the resolution and sensitivity of the technique by introducing diffusional deconvolution to the calculation of sedimentation coefficient distributions. This has become the state-of-the-art of sedimentation velocity analytical ultracentrifugation and is widely applied by research laboratories in this field. Computationally, this method required the solution of the Lamm equation (governing the evolution of the spatial macromolecular concentration profile), and the inversion of an integral equation in conjunction with maximum entropy regularization. Recently we have been able to significantly further increase the resolution of the method by replacing maximum entropy regularization step with a more general Bayesian analysis. ? ? We have further exploited this strategy for the detection of trace protein oligomeric species, which is a topic of great general interest for the study of assembly nucleation events, and of high importance for the detection of immunogenic aggregates in biotechnology. We have refined the application of Bayesian regularization, to reliably detect protein oligomers below the 1% limit.? ? In a different approach, we have also implemented Bayesian regularization in the multi-signal sedimentation velocity analytical ultracentrifugation analysis, to further enhance the resolution of this method for co-existing heterogeneous macromolecular complexes. ? ? As a model system for the hydrodynamic characterization of very large particles, such as drug delivery particles, we have embarked on the study of the size-distribution of gold nano-particles. Such particles are non-trivial to study by ultracentrifugation because they display very low diffusion, and may have significant contributions of surface layers and solvation effects to the buoyancy, both making the determination of the molar mass distribution difficult. At the same time, the hydrodynamic separation by ultracentrifugation is unrivalled by other methods, which makes analytical ultracentrifugation a highly promising tool for their characterization.
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