Next-Generation AUC: Analysis of Multiwavelength Analytical Ultracentrifugation Data.

Autor:	Gorbet GE; Department of Biochemistry, The University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA., Pearson JZ; Physical Chemistry, Department of Chemistry, University of Konstanz, Konstanz, Germany., Demeler AK; Department of Biochemistry, The University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA., Cölfen H; Physical Chemistry, Department of Chemistry, University of Konstanz, Konstanz, Germany., Demeler B; Department of Biochemistry, The University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA. Electronic address: demeler@biochem.uthscsa.edu.
Jazyk:	angličtina
Zdroj:	Methods in enzymology [Methods Enzymol] 2015; Vol. 562, pp. 27-47. Date of Electronic Publication: 2015 Aug 21.
DOI:	10.1016/bs.mie.2015.04.013
Abstrakt:	We describe important advances in methodologies for the analysis of multiwavelength data. In contrast to the Beckman-Coulter XL-A/I ultraviolet-visible light detector, multiwavelength detection is able to simultaneously collect sedimentation data for a large wavelength range in a single experiment. The additional dimension increases the data density by orders of magnitude, posing new challenges for data analysis and management. The additional data not only improve the statistics of the measurement but also provide new information for spectral characterization, which complements the hydrodynamic information. New data analysis and management approaches were integrated into the UltraScan software to address these challenges. In this chapter, we describe the enhancements and benefits realized by multiwavelength analysis and compare the results to those obtained from the traditional single-wavelength detector. We illustrate the advances offered by the new instruments by comparing results from mixtures that contain different ratios of protein and DNA samples, representing analytes with distinct spectral and hydrodynamic properties. For the first time, we demonstrate that the spectral dimension not only adds valuable detail, but when spectral properties are known, individual components with distinct spectral properties measured in a mixture by the multiwavelength system can be clearly separated and decomposed into traditional datasets for each of the spectrally distinct components, even when their sedimentation coefficients are virtually identical. (© 2015 Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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