Spectral fitting for signal assignment and structural analysis of uniformly 13C-labeled solid proteins by simulated annealing based on chemical shifts and spin dynamics

VirtualSpectrum, a tool for simulating peak list for multi-dimensional NMR spectra

NMR spectroscopy is a widely used technique for characterizing the structure and dynamics of macromolecules. Often large amounts of NMR data are required to characterize the structure of proteins. To save valuable time and resources on data acquisition, simulated data is useful in the developmental phase, for data analysis, and for comparison with experimental data. However, existing tools for this purpose can be difficult to use, are sometimes specialized for certain types of molecules or spectra, or produce too idealized data. Here we present a fast, flexible and robust tool, VirtualSpectrum, for generating peak lists for most multi-dimensional NMR experiments for both liquid and solid state NMR. It is possible to tune the quality of the generated peak lists to include sources of artifacts from peak overlap, noise and missing signals. VirtualSpectrum uses an analytic expression to represent the spectrum and derive the peak positions, seamlessly handling overlap between signals. We demonstrate our tool by comparing simulated and experimental spectra for different multi-dimensional NMR spectra and analyzing systematically three cases where overlap between peaks is particularly relevant; solid state NMR data, liquid state NMR homonuclear (1)H and (15)N-edited spectra, and 2D/3D heteronuclear correlation spectra of unstructured proteins. We analyze the impact of protein size and secondary structure on peak overlap and on the accuracy of structure determination based on data of different qualities simulated by VirtualSpectrum.

Peakr: simulating solid-state NMR spectra of proteins

MOTIVATION

When analyzing solid-state nuclear magnetic resonance (NMR) spectra of proteins, assignment of resonances to nuclei and derivation of restraints for 3D structure calculations are challenging and time-consuming processes. Simulated spectra that have been calculated based on, for example, chemical shift predictions and structural models can be of considerable help. Existing solutions are typically limited in the type of experiment they can consider and difficult to adapt to different settings.

RESULTS

Here, we present Peakr, a software to simulate solid-state NMR spectra of proteins. It can generate simulated spectra based on numerous common types of internuclear correlations relevant for assignment and structure elucidation, can compare simulated and experimental spectra and produces lists and visualizations useful for analyzing measured spectra. Compared with other solutions, it is fast, versatile and user friendly.

AVAILABILITY AND IMPLEMENTATION

Peakr is maintained under the GPL license and can be accessed at http://www.peakr.org. The source code can be obtained on request from the authors.

Secondary structural analysis of proteins based on (13)C chemical shift assignments in unresolved solid-state NMR spectra enhanced by fragmented structure database

Magic-angle-spinning solid-state (13)C NMR spectroscopy is useful for structural analysis of non-crystalline proteins. However, the signal assignments and structural analysis are often hampered by the signal overlaps primarily due to minor structural heterogeneities, especially for uniformly-(13)C,(15)N labeled samples. To overcome this problem, we present a method for assigning (13)C chemical shifts and secondary structures from unresolved two-dimensional (13)C-(13)C MAS NMR spectra by spectral fitting, named reconstruction of spectra using protein local structures (RESPLS). The spectral fitting was conducted using databases of protein fragmented structures related to (13)C(α), (13)C(β), and (13)C' chemical shifts and cross-peak intensities. The experimental (13)C-(13)C inter- and intra-residue correlation spectra of uniformly isotope-labeled ubiquitin in the lyophilized state had a few broad peaks. The fitting analysis for these spectra provided sequence-specific C(α), C(β), and C' chemical shifts with an accuracy of about 1.5 ppm, which enabled the assignment of the secondary structures with an accuracy of 79 %. The structural heterogeneity of the lyophilized ubiquitin is revealed from the results. Test of RESPLS analysis for simulated spectra of five different types of proteins indicated that the method allowed the secondary structure determination with accuracy of about 80 % for the 50-200 residue proteins. These results demonstrate that the RESPLS approach expands the applicability of the NMR to non-crystalline proteins exhibiting unresolved (13)C NMR spectra, such as lyophilized proteins, amyloids, membrane proteins and proteins in living cells.

Structure and dynamics of membrane proteins by magic angle spinning solid-state NMR

Membrane proteins remain difficult to study by traditional methods. Magic angle spinning solid-state NMR (MAS SSNMR) methods present an important approach for studying membrane proteins of moderate size. Emerging MAS SSNMR methods are based on extensive assignments of the nuclei as a basis for structure determination and characterization of function. These methods have already been used to characterize fibrils and globular proteins and are being increasingly used to study membrane proteins embedded in lipids. This review highlights recent applications to intrinsic membrane proteins and summarizes recent technical advances that will enable these methods to be utilized for more complex membrane protein systems in the near future.

Comparative analysis of NMR chemical shift predictions for proteins in the solid phase

A comparative analysis of nuclear chemical shift predictions of proteins in the solid state by rapid algorithms trained on and verified with solution-state NMR assignments is presented. The precision of predictions by four dedicated computer programs (SHIFTS, PROSHIFTS, SHIFTX and SPARTA) was found to be close to values obtained for proteins in solution. Correlation coefficients depend on the NMR nucleus (N, C', C(alpha) and C(beta)) and on secondary structure (beta-strand, random coil and alpha-helix), but also on the molecular environment (membrane-integral or not). The findings establish a quantitative basis for using chemical shift prediction programs for solid-state NMR applications. On the other hand, prediction inaccuracies identified for certain resonance kind, residue type, and molecular environment point to possible areas of methodological improvement.