Sedimentation coefficient distributions of large particles

Abstract

The spatial and temporal evolution of concentration boundaries in sedimentation velocity analytical ultracentrifugation reports on the size distribution of particles with high hydrodynamic resolution. For large particles such as large protein complexes, fibrils, viral particles, or nanoparticles, sedimentation conditions usually allow migration from diffusion to be neglected relative to sedimentation. In this case, the shape of the sedimentation boundaries of polydisperse mixtures relates directly to the underlying size-distributions. Integral and derivative methods for calculating sedimentation coefficient distributions g*(s) of large particles from experimental boundary profiles have been developed previously, and are recapitulated here in a common theoretical framework. This leads to a previously unrecognized relationship between g*(s) and the time-derivative of concentration profiles. Of closed analytical form, it is analogous to the well-known Bridgman relationship for the radial derivative. It provides a quantitative description of the effect of substituting the time-derivative by scan differences with finite time intervals, which appears as a skewed box average of the true distribution. This helps to theoretically clarify the differences between results from time-derivative method and the approach of directly fitting the integral definition of g*(s) to the entirety of experimental boundary data.

Measuring macromolecular size distributions and interactions at high concentrations by sedimentation velocity

“…Thus, as we have demonstrated here, even analyses that attempt only to extract minimal information on the populations associated with approximate s -values will suffer from the systematic underestimate of faster-sedimenting populations. For example, this will further exacerbate distortions in the dcdt -method to determine an apparent sedimentation coefficient distribution g ( s* ) 43 , rendering any quantitative interpretation of g ( s* ) uncertain.…”

The effect of divalent cations on the thermostability of type II polyketide synthase acyl carrier proteins

“…31 Heterogeneity of the mixtures was determined by using the dc/dt method 32 implemented in DCDT+ (v.2.4.0), and a model-independent, continuous c(s) distribution using the 1 discrete component model in Sedfit (v.15.3). 33 The 1 discrete component model takes into consideration small molar mass components that do not sediment even at 50,000 rpm. 33 Confidence intervals for temperature-corrected sedimentation coefficient, s(20,w), diffusion coefficient, D(20,w), and molecular mass (kDa) were computed using a bootstrapping method, confidence probability level 90% (±1.65 sigma) in DCDT+ (v.2.4.0).…”

Quantitative Analysis of Protein Self‐Association by Sedimentation Velocity

“…By contrast, the use of sedimentation coefficient distribution analyses described in the present protocols accounts naturally for polydispersity and extracts the most robust features of the sedimentation patterns into the s wisotherms. (It should be noted, however, that s w -values derived from size distribution models that do not fit the SV data across the entire time course are equally suspect (Schuck, 2016b). )…”