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Interactions between nucleic acids and proteins are critical for many cellular processes, and their study is of utmost importance to many areas of biochemistry, cellular biology, and virology. Here, we introduce a new analytical method based on sedimentation velocity (SV) analytical ultracentrifugation, in combination with a novel multiwavelength detector to characterize such interactions. We identified the stoichiometry and molar mass of a complex formed during the interaction of a West Nile virus RNA stem loop structure with the human T cell-restricted intracellular antigen-1 related protein. SV has long been proven as a powerful technique for studying dynamic assembly processes under physiological conditions in solution. Here, we demonstrate, for the first time, how the new multiwavelength technology can be exploited to study protein-RNA interactions, and show how the spectral information derived from the new detector complements the traditional hydrodynamic information from analytical ultracentrifugation. Our method allows the protein and nucleic acid signals to be separated by spectral decomposition such that sedimentation information from each individual species, including any complexes, can be clearly identified based on their spectral signatures. The method presented here extends to any interacting system where the interaction partners are spectrally separable.

We describe important advances in analytical ultracentrifugation (AUC) hardware, which add new information to the hydrodynamic information observed in traditional AUC instruments. In contrast to the Beckman-Coulter XLA UV/visible detector, multiwavelength (MWL) detection is able to collect sedimentation data not just for one wavelength, but for a large wavelength range in a single experiment. The additional dimension increases the data density by orders of magnitude, significantly improving the statistics of the measurement and adding important information to the experiment since an additional dimension of spectral characterization is now available to complement the hydrodynamic information. The new detector avoids tedious repeats of experiments at different wavelengths and opens up new avenues for the solution-based investigation of complex mixtures. In this chapter, we describe the capabilities, characteristics, and applications of the new detector design with biopolymers as the focus of study. We show data from two different MWL detectors and discuss strengths and weaknesses of differences in the hardware and different data acquisition modes. Also, difficulties with fiber optic applications in the UV are discussed. Data quality is compared across platforms., (© 2015 Elsevier Inc. All rights reserved.)

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.)