On the problem of resonance assignments in solid state NMR of uniformly ¹⁵N,¹³C-labeled proteins

Cryo-EM and Solid State NMR Together Provide a More Comprehensive Structural Investigation of Protein Fibrils

The Tropomyosin 1 isoform I/C C-terminal domain (Tm1-LC) fibril structure is studied jointly with cryogenic electron microscopy (cryo-EM) and solid state nuclear magnetic resonance (NMR). This study demonstrates the complementary nature of these two structural biology techniques. Chemical shift assignments from solid state NMR are used to determine the secondary structure at the level of individual amino acids, which is faithfully seen in cryo-EM reconstructions. Additionally, solid state NMR demonstrates that the region not observed in the reconstructed cryo-EM density is primarily in a highly mobile random coil conformation rather than adopting multiple rigid conformations. Overall, this study illustrates the benefit of investigations combining cryo-EM and solid state NMR to investigate protein fibril structure.

ASAP: An automatic sequential assignment program for congested multidimensional solid state NMR spectra

Accurate signal assignments can be challenging for congested solid-state NMR (ssNMR) spectra. We describe an automatic sequential assignment program (ASAP) to partially overcome this challenge. ASAP takes three input files: the residue type assignments (RTAs) determined from the better-resolved NCACX spectrum, the full peak list of the NCOCX spectrum, and the protein sequence. It integrates our auto-residue type assignment strategy (ARTIST) with the Monte Carlo simulated annealing (MCSA) algorithm to overcome the hurdle for accurate signal assignments caused by incomplete side-chain resonances and spectral congestion. Combined, ASAP demonstrates robust performance and accelerates signal assignments of large proteins (>200 residues) that lack crystalline order.

Do Models beyond Hybrid Density Functionals Increase the Agreement with Experiment for Predicted NMR Chemical Shifts or Electric Field Gradient Tensors in Organic Solids?

Ab initio predictions of chemical shifts and electric field gradient (EFG) tensor components are frequently used to help interpret solid-state nuclear magnetic resonance (NMR) experiments. Typically, these predictions employ density functional theory (DFT) with generalized gradient approximation (GGA) functionals, though hybrid functionals have been shown to improve accuracy relative to experiment. Here, the performance of a dozen models beyond the GGA approximation are examined for the prediction of solid-state NMR observables, including meta-GGA, hybrid, and double-hybrid density functionals and second-order Møller-Plesset perturbation theory (MP2). These models are tested on organic molecular crystal data sets containing 169 experimental ¹³C and ¹⁵N chemical shifts and 114 ¹⁷O and ¹⁴N EFG tensor components. To make these calculations affordable, gauge-including projector augmented wave (GIPAW) Perdew-Burke-Ernzerhof (PBE) calculations with periodic boundary conditions are combined with a local intramolecular correction computed at the higher level of theory. Within the context of typical NMR property calculations performed on a static, DFT-optimized crystal structure, the benchmarking finds that the double-hybrid DFT functionals produce errors versus experiment that are no smaller than those of hybrid functionals in the best cases, and they can be larger. MP2 errors versus experiment are even bigger. Overall, no practical advantages are found for using any of the tested double-hybrid functionals or MP2 to predict experimental solid-state NMR chemical shifts and EFG tensor components for routine organic crystals, especially given the higher computational cost of those methods. This finding likely reflects error cancellation benefiting the hybrid functionals. Improving the accuracy of the predicted chemical shifts and EFG tensors relative to experiment would probably require more robust treatments of the crystal structures, their dynamics, and other factors.

Solid-State NMR: Methods for Biological Solids

In the last two decades, solid-state nuclear magnetic resonance (ssNMR) spectroscopy has transformed from a spectroscopic technique investigating small molecules and industrial polymers to a potent tool decrypting structure and underlying dynamics of complex biological systems, such as membrane proteins, fibrils, and assemblies, in near-physiological environments and temperatures. This transformation can be ascribed to improvements in hardware design, sample preparation, pulsed methods, isotope labeling strategies, resolution, and sensitivity. The fundamental engagement between nuclear spins and radio-frequency pulses in the presence of a strong static magnetic field is identical between solution and ssNMR, but the experimental procedures vastly differ because of the absence of molecular tumbling in solids. This review discusses routinely employed state-of-the-art static and MAS pulsed NMR methods relevant for biological samples with rotational correlation times exceeding 100's of nanoseconds. Recent developments in signal filtering approaches, proton methodologies, and multiple acquisition techniques to boost sensitivity and speed up data acquisition at fast MAS are also discussed. Several examples of protein structures (globular, membrane, fibrils, and assemblies) solved with ssNMR spectroscopy have been considered. We also discuss integrated approaches to structurally characterize challenging biological systems and some newly emanating subdisciplines in ssNMR spectroscopy.

Identification of the Rigid Core for Aged Liquid Droplets of an RNA-Binding Protein Low Complexity Domain

The biomolecular condensation of proteins with low complexity sequences plays a functional role in RNA metabolism and a pathogenic role in neurodegenerative diseases. The formation of dynamic liquid droplets brings biomolecules together to achieve complex cellular functions. The rigidification of liquid droplets into β-strand-rich hydrogel structures composed of protein fibrils is thought to be purely pathological in nature. However, low complexity sequences often harbor multiple fibril-prone regions with delicately balanced functional and pathological interactions. Here, we investigate the maturation of liquid droplets formed by the low complexity domain of the TAR DNA-binding protein 43 (TDP-43). Solid state nuclear magnetic resonance measurements on the aged liquid droplets identify residues 365-400 as the structured core, which are squarely outside the region between residues 311-360 thought to be most important for pathological fibril formation and aggregation. The results of this study suggest that multiple segments of this low complexity domain are prone to form fibrils and that stabilization of β-strand-rich structure in one segment precludes the other region from adopting a rigid fibril structure.

Solid-state NMR spectroscopy

Solid-state nuclear magnetic resonance (NMR) spectroscopy is an atomic-level method used to determine the chemical structure, three-dimensional structure, and dynamics of solids and semi-solids. This Primer summarizes the basic principles of NMR as applied to the wide range of solid systems. The fundamental nuclear spin interactions and the effects of magnetic fields and radiofrequency pulses on nuclear spins are the same as in liquid-state NMR. However, because of the anisotropy of the interactions in the solid state, the majority of high-resolution solid-state NMR spectra is measured under magic-angle spinning (MAS), which has profound effects on the types of radiofrequency pulse sequences required to extract structural and dynamical information. We describe the most common MAS NMR experiments and data analysis approaches for investigating biological macromolecules, organic materials, and inorganic solids. Continuing development of sensitivity-enhancement approaches, including 1H-detected fast MAS experiments, dynamic nuclear polarization, and experiments tailored to ultrahigh magnetic fields, is described. We highlight recent applications of solid-state NMR to biological and materials chemistry. The Primer ends with a discussion of current limitations of NMR to study solids, and points to future avenues of development to further enhance the capabilities of this sophisticated spectroscopy for new applications.

Side Chain Hydrogen-Bonding Interactions within Amyloid-like Fibrils Formed by the Low-Complexity Domain of FUS: Evidence from Solid State Nuclear Magnetic Resonance Spectroscopy

In aqueous solutions, the 214-residue low-complexity domain of the FUS protein (FUS-LC) is known to undergo liquid-liquid phase separation and also to self-assemble into amyloid-like fibrils. In previous work based on solid state nuclear magnetic resonance (ssNMR) methods, a structural model for the FUS-LC fibril core was developed, showing that residues 39-95 form the fibril core. Unlike fibrils formed by amyloid-β peptides, α-synuclein, and other amyloid-forming proteins, the FUS-LC core is largely devoid of purely hydrophobic amino acid side chains. Instead, the core-forming segment contains numerous hydroxyl-bearing residues, including 18 serines, six threonines, and eight tyrosines, suggesting that the FUS-LC fibril structure may be stabilized in part by inter-residue hydrogen bonds among side chain hydroxyl groups. Here we describe ssNMR measurements, performed on ²H,¹⁵N,¹³C-labeled FUS-LC fibrils, that provide new information about the interactions of hydroxyl-bearing residues with one another and with water. The ssNMR data support the involvement of specific serine, threonine, and tyrosine residues in hydrogen-bonding interactions. The data also reveal differences in hydrogen exchange rates with water for different side chain hydroxyl groups, providing information about solvent exposure and penetration of water into the FUS-LC fibril core.

Conformationally selective multidimensional chemical shift ranges in proteins from a PACSY database purged using intrinsic quality criteria

We have determined refined multidimensional chemical shift ranges for intra-residue correlations ((13)C-(13)C, (15)N-(13)C, etc.) in proteins, which can be used to gain type-assignment and/or secondary-structure information from experimental NMR spectra. The chemical-shift ranges are the result of a statistical analysis of the PACSY database of >3000 proteins with 3D structures (1,200,207 (13)C chemical shifts and >3 million chemical shifts in total); these data were originally derived from the Biological Magnetic Resonance Data Bank. Using relatively simple non-parametric statistics to find peak maxima in the distributions of helix, sheet, coil and turn chemical shifts, and without the use of limited "hand-picked" data sets, we show that ~94% of the (13)C NMR data and almost all (15)N data are quite accurately referenced and assigned, with smaller standard deviations (0.2 and 0.8 ppm, respectively) than recognized previously. On the other hand, approximately 6% of the (13)C chemical shift data in the PACSY database are shown to be clearly misreferenced, mostly by ca. -2.4 ppm. The removal of the misreferenced data and other outliers by this purging by intrinsic quality criteria (PIQC) allows for reliable identification of secondary maxima in the two-dimensional chemical-shift distributions already pre-separated by secondary structure. We demonstrate that some of these correspond to specific regions in the Ramachandran plot, including left-handed helix dihedral angles, reflect unusual hydrogen bonding, or are due to the influence of a following proline residue. With appropriate smoothing, significantly more tightly defined chemical shift ranges are obtained for each amino acid type in the different secondary structures. These chemical shift ranges, which may be defined at any statistical threshold, can be used for amino-acid type assignment and secondary-structure analysis of chemical shifts from intra-residue cross peaks by inspection or by using a provided command-line Python script (PLUQin), which should be useful in protein structure determination. The refined chemical shift distributions are utilized in a simple quality test (SQAT) that should be applied to new protein NMR data before deposition in a databank, and they could benefit many other chemical-shift based tools.