Abstract
A method for removing time- and radially invariant noise from sedimentation velocity and sedimentation equilibrium experiments performed in an analytical ultracentrifuge is presented. The method averages repeat radial incident light measurements as a function of the photomultiplier response at different wavelengths to remove the majority of the time-invariant noise contributions from intensity data measurements. The results of this method are compared to traditional absorbance data generated with a buffer reference and the Beckman Optima AUC data acquisition program, and with the standard UltraScan refinement workflow. The method avoids the amplification of stochastic noise inherent in the absorbance scan subtraction traditionally employed in sedimentation velocity and equilibrium data. In addition, the collection of intensity data frees up the reference channel for additional samples, doubling the capacity of the instrument. In comparison to absorbance data, the residual mean square deviation of a fitted sedimentation velocity experiment without additional noise correction by UltraScan was improved by a factor of 4.5 when using the new method. This improvement benefits sedimentation equilibrium experiments as well as analytical buoyant density equilibrium experiments where routine time-invariant noise correction calculations cannot be performed.
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Data availability
The UltraScan software used to analyze the AUC data is open source and freely available from the Github repository (https://github.com/ehb54/ultrascan3), the AUC data itself is available upon request from the corresponding author from the UltraScan LIMS server at the Canadian Center for Hydrodynamics. All raw and edited AUC data are available in the open source OpenAUC format.
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Acknowledgements
This research and S.M. were supported by the Biomolecular Interaction Technology Center, University of Delaware, and the Canada 150 Research Chairs program (C150-2017-00015, BD), the Canada Foundation for Innovation (CFI-37589, BD), the National Institutes of Health (1R01GM120600, BD), and the Canadian Natural Science and Engineering Research Council (DG-RGPIN-2019-05637, BD). UltraScan supercomputer calculations were supported through NSF/XSEDE under Grant TG-MCB070039N (BD) and University of Texas under Grant TG457201 (BD).
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Special Issue: Analytical Ultracentrifugation 2022.
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Mortezazadeh, S., Demeler, B. Systematic noise removal from analytical ultracentrifugation data with UltraScan. Eur Biophys J 52, 203–213 (2023). https://doi.org/10.1007/s00249-023-01631-6
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DOI: https://doi.org/10.1007/s00249-023-01631-6