Structural Information from Spin-Labelled Membrane-Bound Proteins

Impact of N-(2-aminoethyl) Glycine Unit on Watson-Crick Base Pairs

We aim to understand the structure and stability of the backbone tailored Watson-Crick base pairs, Guanine-Cytosine (GC), Adenine-Thymine (AT) and Adenine-Uracil (AU) by incorporating N-(2-aminoethyl) glycine units (linked by amide bonds) at the purine and pyrimidine sites of the nucleobases. Density functional theory (DFT) is employed in which B3LYP/6-311++G∗ ∗ level of theory has been used to optimize all the structures. The peptide attached base pairs are compared with the natural deoxyribose nucleic acid (DNA)/ribonucleic acid (RNA) base pairs and the calculations are carried out in both the gas and solution phases. The structural propensities of the optimized base pairs are analyzed using base pair geometries, hydrogen bond distances and stabilization energies and, compared with the standard reference data. The structural parameters were found to correlate well with the available data. The addition of peptide chain at the back bone of the DNA/RNA base pairs results only with a minimal distortion and hence does not alter the structural configuration of the base pairs. Also enhanced stability of the base pairs is spotted while adding peptidic chain at the purine site rather than the pyrimidine site of the nucleobases. The stability of the complexes is further interpreted by considering the hydrogen bonded N–H stretching frequencies of the respective base pairs. The discrimination in the interaction energies observed in both gas and solution phases are resulted due to the existence of distinct lowest unoccupied molecular orbitals (LUMO) in the solution phase. The reactivity of the base pairs is also analyzed through the in-depth examinations on the highest occupied molecular orbital (HOMO)-LUMO orbitals.

Singular Value Decomposition Method To Determine Distance Distributions in Pulsed Dipolar Electron Spin Resonance: II. Estimating Uncertainty

This paper is a continuation of the method introduced by Srivastava and Freed (2017) that is a new method based on truncated singular value decomposition (TSVD) for obtaining physical results from experimental signals without any need for Tikhonov regularization or other similar methods that require a regularization parameter. We show here how to estimate the uncertainty in the SVD-generated solutions. The uncertainty in the solution may be obtained by finding the minimum and maximum values over which the solution remains converged. These are obtained from the optimum range of singular value contributions, where the width of this region depends on the solution point location (e.g., distance) and the signal-to-noise ratio (SNR) of the signal. The uncertainty levels typically found are very small with substantial SNR of the (denoised) signal, emphasizing the reliability of the method. With poorer SNR, the method is still satisfactory but with greater uncertainty, as expected. Pulsed dipolar electron spin resonance spectroscopy experiments are used as an example, but this TSVD approach is general and thus applicable to any similar experimental method wherein singular matrix inversion is needed to obtain the physically relevant result. We show that the Srivastava-Freed TSVD method along with the estimate of uncertainty can be effectively applied to pulsed dipolar electron spin resonance signals with SNR > 30, and even for a weak signal (e.g., SNR ≈ 3) reliable results are obtained by this method, provided the signal is first denoised using wavelet transforms (WavPDS).

Singular Value Decomposition Method to Determine Distance Distributions in Pulsed Dipolar Electron Spin Resonance

Regularization is often utilized to elicit the desired physical results from experimental data. The recent development of a denoising procedure yielding about 2 orders of magnitude in improvement in SNR obviates the need for regularization, which achieves a compromise between canceling effects of noise and obtaining an estimate of the desired physical results. We show how singular value decomposition (SVD) can be employed directly on the denoised data, using pulse dipolar electron spin resonance experiments as an example. Such experiments are useful in measuring distances and their distributions, P(r) between spin labels on proteins. In noise-free model cases exact results are obtained, but even a small amount of noise (e.g., SNR = 850 after denoising) corrupts the solution. We develop criteria that precisely determine an optimum approximate solution, which can readily be automated. This method is applicable to any signal that is currently processed with regularization of its SVD analysis.

A New Wavelet Denoising Method for Experimental Time-Domain Signals: Pulsed Dipolar Electron Spin Resonance

We adapt a new wavelet-transform-based method of denoising experimental signals to pulse-dipolar electron-spin resonance spectroscopy (PDS). We show that signal averaging times of the time-domain signals can be reduced by as much as 2 orders of magnitude, while retaining the fidelity of the underlying signals, in comparison with noiseless reference signals. We have achieved excellent signal recovery when the initial noisy signal has an SNR ≳ 3. This approach is robust and is expected to be applicable to other time-domain spectroscopies. In PDS, these time-domain signals representing the dipolar interaction between two electron spin labels are converted into their distance distribution functions P(r), usually by regularization methods such as Tikhonov regularization. The significant improvements achieved by using denoised signals for this regularization are described. We show that they yield P(r)'s with more accurate detail and yield clearer separations of respective distances, which is especially important when the P(r)'s are complex. Also, longer distance P(r)'s, requiring longer dipolar evolution times, become accessible after denoising. In comparison to standard wavelet denoising approaches, it is clearly shown that the new method (WavPDS) is superior.