Tools for the Quantitative Analysis of Sedimentation Boundaries Detected by Fluorescence Optical Analytical Ultracentrifugation

Abstract

Fluorescence optical detection in sedimentation velocity analytical ultracentrifugation allows the study of macromolecules at nanomolar concentrations and below. This has significant promise, for example, for the study of systems of high-affinity protein interactions. Here we describe adaptations of the direct boundary modeling analysis approach implemented in the software SEDFIT that were developed to accommodate unique characteristics of the confocal fluorescence detection system. These include spatial gradients of signal intensity due to scanner movements out of the plane of rotation, temporal intensity drifts due to instability of the laser and fluorophores, and masking of the finite excitation and detection cone by the sample holder. In an extensive series of experiments with enhanced green fluorescent protein ranging from low nanomolar to low micromolar concentrations, we show that the experimental data provide sufficient information to determine the parameters required for first-order approximation of the impact of these effects on the recorded data. Systematic deviations of fluorescence optical sedimentation velocity data analyzed using conventional sedimentation models developed for absorbance and interference optics are largely removed after these adaptations, resulting in excellent fits that highlight the high precision of fluorescence sedimentation velocity data, thus allowing a more detailed quantitative interpretation of the signal boundaries that is otherwise not possible for this system.

Analysis of High Affinity Self-Association by Fluorescence Optical Sedimentation Velocity Analytical Ultracentrifugation of Labeled Proteins: Opportunities and Limitations

“…In an initial attempt to reconcile the previous hydrodynamic parameters from FDS-SV we previously noted a nonlinearity in the signal with loading concentration of FAM-GluA2 ATD, but excluded the possibility that it could quantitatively account for the apparent discrepancies in s -values [7], based on theoretical considerations of sedimentation analysis of non-linear signals [14]. We believe it is very useful to critically examine the ratio of (gain-corrected) signal and loading concentration over a wide range of loading concentrations as shown in Figure 2 , in order to test for signal non-linearity, and to exclude other possible sources of errors in K D such as oligomeric state-dependent quantum yields.…”

Higher‐order oligomerization promotes localization of SPOP to liquid nuclear speckles

“…The FDS‐SV data were sorted according to the sample position, PMT voltage, and gains using the Fluorescence Tools set in SEDFIT version 14.4 (Zhao et al , 2013b), and subjected to standard c ( s ) analysis (Schuck, 2000) in SEDFIT. Integration between 1 and 6 S was carried out to generate the isotherm analysis of signal‐weighted sedimentation coefficients, s w .…”

Tubulin Dimer Reversible Dissociation

“…In the case of fluorescence data, files were sorted using SEDFIT tools, mainly to remove randomly occurring defective scans, and then initially analyzed with the standard c(s) model. For high intensity samples (with high signal-to-noise ratio), the built-in fluorescence tools were summoned, as described before (44), to account for imperfections in the alignment of the fluorescence optics, temporal drifts in the signal, and excitation beam shadowing at the bottom of the cell.…”