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Protein NMR Spectroscopy : Principles and Practice

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Description Protein NMR Spectroscopy, Second Edition combines a comprehensive theoretical treatment of NMR spectroscopy with an extensive exposition of the experimental techniques applicable to proteins and other biological macromolecules in solution. Key Features Provides an understanding of the theoretical principles important for biological NMR spectroscopy Demonstrates how to implement, optimize and troubleshoot modern multi-dimensional NMR experiments Allows for the capability of designing effective experimental protocols for investigations of protein structures and dynamics Includes a comprehensive set of example NMR spectra of ubiquitin provides a reference for validation of experimental methods.

Provides an understanding of the theoretical principles important for biological NMR spectroscopy Demonstrates how to implement, optimize and troubleshoot modern multi-dimensional NMR experiments Allows for the capability of designing effective experimental protocols for investigations of protein structures and dynamics Includes a comprehensive set of example NMR spectra of ubiquitin provides a reference for validation of experimental methods.

In other words, the resolution is determined by the number of points sampled in the time-domain signal. Changing the digital resolution in frequency domain is equivalent to changing the number of sampled points in the corresponding time-domain. Since we study the effects of high digital resolution on peak overlap and protein structure calculations, we refer to the observed changes interchangeably as a function of the digital resolution of the number of sampled points.

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Protein Nmr Spectroscopy Principles Practice by Cavanagh John Fairbrother Wayne Palmer

In order to study the effect of the digital resolution on protein structure calculations, we varied the digital resolution for the 1 H indirect dimension by changing the maximal number of points from 28 to Table S1. The numbers of sampled points for the carbon and nitrogen dimensions of 13 C- and 15 N-resolved NOESY spectra were set to 64 complex points each. Uniformly sampled schedules, containing all linear points, were generated for comparison calculations. Non-uniformly sampled schedules were prepared using nussampler [39].

The current version allows generating schedules with options for incremental matched sampling with and without repetitions of sampled points, examples are given in Figure S2. For all calculations in this paper repetitions of sampled points were allowed.


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The signal-to-noise improvement ratios were calculated using equation 3 with uniformly and non-uniformly sampled schedules. The chemical shift table for each protein was obtained from experimentally determined chemical shifts values deposited in RefDB [35].

The 13 C- and 15 N-resolved NOESY peak lists for all structures in the dataset were back-calculated with CYANA [40] — [43] using the chemical shift tables and a calibration of inter-nuclear spatial distances in the range between 2. The peak lists were refined based on two different criteria.

First, the peaks below a certain signal intensity threshold level 2. Secondly, remaining peaks were removed if they overlapped with any other peak in all dimensions i. This means that after removal of any overlapped peaks, the peak lists essentially had no peak overlap. The size of the peak lists is the direct consequence of the amount of peak overlap present in the corresponding NOESY spectra at a given digital resolution. The peak lists were produced separately for all structures in the dataset and for all digital resolutions. A chemical shift spectral overlap CSSO index was defined as the average number of overlaps present for each peak in a peak list before the above-mentioned removal of overlaps.

The overlap in a cross peak was checked using the chemical shifts values in each of the dimensions.

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Chemical shift difference values between a cross peak and any other cross peaks in all three dimensions were calculated. These differences were compared simultaneously with the digital resolution and the natural linewidth in each of the dimensions. A cross peak was counted as overlapped if the corresponding differences were less than the digital resolution or the natural linewidth, whichever is higher, in all the dimensions.

The cross peak was retained if no overlap was observed with any other cross peaks in any one of the dimensions. The former yield inter-atomic spatial upper distance limits; the latter provide backbone torsion angle restraints. Structure calculations were performed using the standard CYANA protocol that uses all this information [2] , [42]. The method was applied to the protein structures at all digital resolutions.

The results were characterized by calculating the mean RMSD value of the distribution. Additionally, we analyzed the proteins by dividing them into three different groups by molecular size. The size ranges were 10—15 kDa, 15—20 kDa, and 20—35 kDa with , 76, and 25 protein structures, respectively. Increasing the digital resolution has important consequences for the success of the protein structure calculation protocol. First of all, it is reflected in the RMSD values of the calculated protein structures from the corresponding reference structures.

All profiles are shown in Figure S4. These profiles were grouped by molecular size or by RMSD values of calculated structures. The result is shown in Figure 2. The histogram of all RMSD values obtained from the profiles at the highest resolution is shown in Figure 3. The RMSD values and the fitted gamma distribution show a statistically significant fit.

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A Median of the heavy atom RMSD to the reference structures are shown for protein structures in the molecular size range from 10 to 15 kDa solid , 76 protein structures in the molecular size range 15—20 kDa dashed and 25 protein structures in the molecular size range 20—35 kDa dotted. The histogram shows the heavy atom RMSD values of protein structures.

Dashed, dotted, and solid lines correspond to , 14, and 4 protein structures with RMSD values of 1. The improvement increases with the number of sampled points and the size of the protein. The latter dependence is explained by the decrease of the transverse relaxation time for large proteins. The peak counts gradually increase with increase in digital resolution for proteins of all sizes Figure S1. For the 5 kDa protein, an increase in peak counts is observed until more than points are sampled. Similarly, for a protein of 10 kDa size, the count saturates after sampling about points.

The trend is similar for proteins of all sizes: it is inverse proportional to size. Larger proteins require smaller numbers of points for peak counts to saturate, beyond which there is no significant improvement in peak counts. The protein structure dataset was divided into three classes based on the molecular sizes ranging between 10—15 kDa, 15—20 kDa, and 20—25 kDa.

The median heavy-atom root-mean-square deviation RMSD of the protein structures varies with increasing digital resolution. Figure 2a shows changes in RMSD values for the three different size-groups. All groups show a similar trend for the RMSD profiles: high value at low resolution, then a rapid decrease, and a plateau after around points. It is clear that a roughly two-fold improvement in RMSD values at the highest resolution with respect to the lowest resolution can be obtained for proteins structures of all sizes.

A low Kolmogorov-Smirnov statistic value 0. As expected, all groups show the highest peak overlap at the lowest resolution and the lowest peak overlap at the highest resolution. Essentially, this means that each peak is overlapped on average with 9 or 5 other peaks. The CSSO indices for all groups decrease as a function of resolution until a certain point. We refer to this as the critical point of digital resolution. The index remains stable beyond the critical resolution.

In order to assess the impact of digital resolution on the amount of peak overlap and on the RMSD values of calculated protein structures, the structures were grouped on the basis of the final heavy-atom RMSD values. The highest CSSO index with For structures with RMSD values less than 1.