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Review
. 2015:562:305-30.
doi: 10.1016/bs.mie.2015.04.009. Epub 2015 Jun 13.

Analytical Ultracentrifugation as a Tool to Study Nonspecific Protein-DNA Interactions

Affiliations
Review

Analytical Ultracentrifugation as a Tool to Study Nonspecific Protein-DNA Interactions

Teng-Chieh Yang et al. Methods Enzymol. 2015.

Abstract

Analytical ultracentrifugation (AUC) is a powerful tool that can provide thermodynamic information on associating systems. Here, we discuss how to use the two fundamental AUC applications, sedimentation velocity (SV), and sedimentation equilibrium (SE), to study nonspecific protein-nucleic acid interactions, with a special emphasis on how to analyze the experimental data to extract thermodynamic information. We discuss three specific applications of this approach: (i) determination of nonspecific binding stoichiometry of E. coli integration host factor protein to dsDNA, (ii) characterization of nonspecific binding properties of Adenoviral IVa2 protein to dsDNA using SE-AUC, and (iii) analysis of the competition between specific and nonspecific DNA-binding interactions observed for E. coli integration host factor protein assembly on dsDNA. These approaches provide powerful tools that allow thermodynamic interrogation and thus a mechanistic understanding of how proteins bind nucleic acids by both specific and nonspecific interactions.

Keywords: Nonspecific binding; Specific and nonspecific competitive binding.

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Figures

Figure 1
Figure 1
The McGhee and von Hippel approach is used to resolve the nonspecific binding properties of protein–DNA interactions. This approach assumes that the protein interacts with a one-dimensional DNA lattice and uses conditional probabilities to analyze three nonspecific binding properties: (A) the occluded site size (number of bases occluded by the bound protein), (B) the overlapping effect, and (D) the nearest-neighbor cooperativity. Because of the overlapping effect, under this nonspecific binding scenario, it is difficult to fully saturate the duplex (C).
Figure 2
Figure 2
Stoichiometry of IHF binding to a 10 bp nonspecific duplex determined by SE-AUC. Samples were prepared by mixing various concentrations of IHF with 400 nM DNA; SE-AUC experiments were performed at three different rotor speeds (22, 27, and 33 K RPM) at 4 °C. The data were analyzed by an ideal single-species model (Eq. 13) to approximate the average molecular weight of all species, from which the degree of binding was calculated using Eq. (15). The error bar indicates 68.3% confidence intervals. Data provided by Dr. Saurarshi J. Sanyal.
Figure 3
Figure 3
NLLS analysis of the primary sedimentation equilibrium data using the MvH model. The data in this figure were analyzed by NLLS according to the MvH model. The smooth curves are the result of this analysis fixing Kns, ω, and n at the values determined from other approaches: Kns = 3:9 × 104M−1, ω = 125, and n = 18:8bp (Yang & Maluf, 2014). In this analysis, the concentration of free IVa2 protein and free DNA at the meniscus position were allowed to float. The insets show the predicted concentration of the total IVa2 protein calculated at 5, 7.5, and 10 K RPM, using the best-fit parameters from the NLLS analysis (Yang & Maluf, 2014).
Figure 4
Figure 4
Interrogation of binding of IHF to minimal DNA duplexes using sedimentation velocity analytical ultracentrifugation (SV-AUC). Increasing concentrations of IHF were added to 27 bp specific and nonspecific DNA model duplexes, and their sedimentation behavior was monitored by SV-AUC. The c(s) distribution for each binding experiment was calculated using Sedfit. (A) Normalized c(s) profiles for the specific DNA. (B) Normalized c(s) profiles for the nonspecific DNA. (C) Weight-average sedimentation coefficients for each of the c(s) distributions shown in panels A (triangles) and B (triangles) were calculated using Sedfit and are plotted as a function of IHF concentration. The dotted line represents the best fit of the specific binding data to the nonspecific finite lattice DNA binding model, which does not adequately describe the data. The solid lines represent the best fit of global analysis of the nonspecific and specific binding data to the (i) nonspecific finite lattice DNA binding and (ii) case 1 models, respectively.
Figure 5
Figure 5
Interrogation of IHF assembly on specific and nonspecific 274 bp DNAs using SV-AUC. The specific duplex has a 27 bp IHF recognition element embedded at the center of the duplex while the nonspecific duplex is of random sequence. Increasing concentrations of IHF were added to each duplex, and their sedimentation behavior was monitored by SV-AUC. The c(s) distribution for each binding experiment were calculated using Sedfit. (A) Normalized c(s) profiles for the specific 274 bp DNA. (B) Normalized c(s) profiles for the nonspecific 274 bp DNA. (C) Weight-average sedimentation coefficients for each of the c(s) distributions shown in panel A (circles, specific DNA) and panel B (circles, nonspecific DNA) were calculated using Sedfit and are plotted as a function of IHF concentration. The solid lines represent the best fits of global analysis of the ensemble of binding data to the DNA unbending model (Fig. 6).
Figure 6
Figure 6
Model for IHF–DNA complexes. Details are provided in the text and in Sanyal et al. (2014).

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