An Integrated Spin-Labeling/Computational-Modeling Approach for Mapping Global Structures of Nucleic Acids

Abstract

The technique of site-directed spin labeling (SDSL) provides unique information on biomolecules by monitoring the behavior of a stable radical tag (i.e., spin label) using electron paramagnetic resonance (EPR) spectroscopy. In this chapter, we describe an approach in which SDSL is integrated with computational modeling to map conformations of nucleic acids. This approach builds upon a SDSL tool kit previously developed and validated, which includes three components: (i) a nucleotide-independent nitroxide probe, designated as R5, which can be efficiently attached at defined sites within arbitrary nucleic acid sequences; (ii) inter-R5 distances in the nanometer range, measured via pulsed EPR; and (iii) an efficient program, called NASNOX, that computes inter-R5 distances on given nucleic acid structures. Following a general framework of data mining, our approach uses multiple sets of measured inter-R5 distances to retrieve "correct" all-atom models from a large ensemble of models. The pool of models can be generated independently without relying on the inter-R5 distances, thus allowing a large degree of flexibility in integrating the SDSL-measured distances with a modeling approach best suited for the specific system under investigation. As such, the integrative experimental/computational approach described here represents a hybrid method for determining all-atom models based on experimentally-derived distance measurements.

Keywords: DEER; DNA; EPR; Hybrid models; Integrative modeling; Protein–DNA binding; RNA; Site-directed spin labeling; Solution-state structure.

Figure 1

The general strategy of site-directed spin labeling (SDSL). Step 1: Attach a spin label (i.e., a nitroxide) to specific site(s) of a macromolecule. Step 2: Monitor behavior of the spin label using EPR spectroscopy. Step 3: Derive information about the target molecule. The table shows the two primary types of information that can be derived from EPR measurements on nucleic acids. Adapted from Sowa and Qin (2008) with permission.

Figure 2

Nucleic acid labeling using the nucleotide-independent R5 nitroxide probe. (A) Synthesis of compound 2, the reactive R5 precursor. (B) An example of reverse-phase HPLC characterization of compounds 1 (shown in black) and 2 (shown in red, light gray in the print version). Data shown were collected using a C18 column (Prosphere™ C18, Grace Davidson, Inc.) and a linear gradient generated with buffer A: 0.1 M triethylammonium acetate (TEAA, pH 7.0) and 5% (v/v) acetonitrile; and buffer B: 100% acetonitrile. (C) Site-specific attachment of compound 2 to a phosphorothioate-modified nucleic acid strand. As shown, R5 is attached to the S_p diastereomer of the nth nucleotide, and the three torsion angles about the single bonds connecting the pyrroline ring to the nucleic acid are indicated. Adapted from Qin et al. (2007) with permission.

Figure 3

Measuring nanometer distances using DEER spectroscopy. (A) Pulse sequences for the four-pulse DEER. Three pulses are applied to the observe frequency and generate a refocused echo (shown as “Observe”). Another pulse is applied to a different frequency (pump frequency) to flip the pump spin. Shown on the right is a field-sweep spectrum acquired at the observe frequency with pump spin and observe spin indicated. (B) An example of DEER data acquired on a DNA duplex. Left: Measured normalized echo decay (black) overlaid with the simulated background decay (red, light gray in the print version); center: Measured background-corrected echo decay (black) overlaid with the simulated trace computed from the optimized distance distribution (red, light gray in the print version); right: The corresponding optimized distance distribution computed using Deer Analysis 2013. Adapted from Qin et al. (2007), Sowa and Qin (2008), and Zhang et al. (2014) with permission.

Figure 4

The NASNOX program for computing expected inter-R5 distances on given nucleic acid structures. (A) Examples of input parameters. The previously reported web interface of NASNOX_W (Qin et al., 2007) is shown to illustrate the organization of input information in the parameter files used to execute NASNOX in the batch mode. (B) An example of structure output in which a pair of R5’s were modeled onto nucleotides 4 and 14 of the sample1.pdb input structure. The DNA is shown in red (light gray in the print version), and the allowed R5 rotamers at each site are shown in blue (black in the print version). (C) An example of text output showing the individual inter-R5 distances and the average distance and standard deviation for the entire ensemble. Adapted from Qin et al. (2007) with permission.

Figure 5

An example of analyzing a model pool using DEER-measured distances. Data shown are reproduced from reported work on the “p21-RE” duplex (Zhang et al., 2014). (A) The RMSD_struct versus P_t score plot for the 10,000 models generated from MC simulations. Blue circles represent the top 20 ranked models, obtained using 16 sets of measured distances; red triangles represent the top 20 ranked models, obtained using 14 distances. Note that 14 models, including the best-fit model, are retrieved in both searches. (B) Overlay of the top 20 models of the unbound DNA (blue thin lines) and the bound DNA (red). The unbound DNA models are obtained using the integrated SDSL/MC approach, while the bound DNA is from a reported crystal structure, 3TS8. pdb (Emamzadah, Tropia, & Halazonetis, 2011). The analysis shows that the unbound DNA models converge and identifies the mode of DNA deformation upon protein binding (Zhang et al., 2014). Adapted from Zhang et al. (2014) with permission.