Journal of Biomolecular NMR最新文献

Selective stable isotope labeling has transformed structural and dynamics analysis of RNA by NMR spectroscopy. These methods can remove ¹³C-¹³C dipolar couplings that complicate ¹³C relaxation analyses. While these phenomena are well documented for sites with adjacent ¹³C nuclei (e.g. ribose C1′), less is known about so-called isolated sites (e.g. adenosine C2). To investigate and quantify the effects of long-range (>?2??) ¹³C-¹³C dipolar interactions on RNA dynamics, we simulated adenosine C2 relaxation rates in uniformly [U-¹³C/¹⁵N]-ATP or selectively [2-¹³C]-ATP labeled RNAs. Our simulations predict non-negligible ¹³C-¹³C dipolar contributions from adenosine C4, C5, and C6 to C2 longitudinal (R₁) relaxation rates in [U-¹³C/¹⁵N]-ATP labeled RNAs. Moreover, these contributions increase at higher magnetic fields and molecular weights to introduce discrepancies that exceed 50%. This will become increasingly important at GHz fields. Experimental R₁ measurements in the 61 nucleotide human hepatitis B virus encapsidation signal ε RNA labeled with [U-¹³C/¹⁵N]-ATP or [2-¹³C]-ATP corroborate these simulations. Thus, in the absence of selectively labeled samples, long-range ¹³C-¹³C dipolar contributions must be explicitly taken into account when interpreting adenosine C2 R₁ rates in terms of motional models for large RNAs.

Spectral editing is crucial to simplify the crowded solid-state NMR spectra of proteins. New techniques are introduced to edit ¹³C-¹³C correlations of uniformly labeled proteins under moderate magic-angle spinning (MAS), based on our recent frequency-selective homonuclear recoupling sequences [Zhang et al., J. Phys. Chem. Lett. 2020, 11, 8077–8083]. The signals of alanine, serine, or threonine residues are selected out by selective ¹³Cα-¹³Cβ double-quantum filtering (DQF). The ¹³Cα-¹³Cβ correlations of alanine residues are selectively established with efficiency up to?~?1.8 times that by dipolar-assisted rotational resonance (DARR). The techniques are shown in 2D/3D NCCX experiments and applied to the uniformly ¹³C, ¹⁵N labeled Aquaporin Z (AqpZ) membrane protein, demonstrating their potential to simplify spectral analyses in biological solid-state NMR.

In recent years, the transformative potential of deep neural networks (DNNs) for analysing and interpreting NMR data has clearly been recognised. However, most applications of DNNs in NMR to date either struggle to outperform existing methodologies or are limited in scope to a narrow range of data that closely resemble the data that the network was trained on. These limitations have prevented a widescale uptake of DNNs in NMR. Addressing this, we introduce FID-Net, a deep neural network architecture inspired by WaveNet, for performing analyses on time domain NMR data. We first demonstrate the effectiveness of this architecture in reconstructing non-uniformly sampled (NUS) biomolecular NMR spectra. It is shown that a single network is able to reconstruct a diverse range of 2D NUS spectra that have been obtained with arbitrary sampling schedules, with a range of sweep widths, and a variety of other acquisition parameters. The performance of the trained FID-Net in this case exceeds or matches existing methods currently used for the reconstruction of NUS NMR spectra. Secondly, we present a network based on the FID-Net architecture that can efficiently virtually decouple ¹³C_α-¹³C_β couplings in HNCA protein NMR spectra in a single shot analysis, while at the same time leaving glycine residues unmodulated. The ability for these DNNs to work effectively in a wide range of scenarios, without retraining, paves the way for their widespread usage in analysing NMR data.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the etiological cause of the coronavirus disease 2019, for which no effective antiviral therapeutics are available. The SARS-CoV-2 main protease (M^pro) is essential for viral replication and constitutes a promising therapeutic target. Many efforts aimed at deriving effective M^pro inhibitors are currently underway, including an international open-science discovery project, codenamed COVID Moonshot. As part of COVID Moonshot, we used saturation transfer difference nuclear magnetic resonance (STD-NMR) spectroscopy to assess the binding of putative M^pro ligands to the viral protease, including molecules identified by crystallographic fragment screening and novel compounds designed as M^pro inhibitors. In this manner, we aimed to complement enzymatic activity assays of M^pro performed by other groups with information on ligand affinity. We have made the M^pro STD-NMR data publicly available. Here, we provide detailed information on the NMR protocols used and challenges faced, thereby placing these data into context. Our goal is to assist the interpretation of M^pro STD-NMR data, thereby accelerating ongoing drug design efforts.

Cross-β amyloid fibrils and membrane-bound β-barrels are two important classes of β-sheet proteins. To investigate whether there are systematic differences in the backbone and sidechain conformations of these two families of proteins, here we analyze the ¹³C chemical shifts of 17 amyloid proteins and 7 β-barrel membrane proteins whose high-resolution structures have been determined by NMR. These 24 proteins contain 373 β-sheet residues in amyloid fibrils and 521 β-sheet residues in β-barrel membrane proteins. The ¹³C chemical shifts are shown in 2D ¹³C–¹³C correlation maps, and the amino acid residues are categorized by two criteria: (1) whether they occur in β-strand segments or in loops and turns; (2) whether they are water-exposed or dry, facing other residues or lipids. We also examine the abundance of each amino acid in amyloid proteins and β-barrels and compare the sidechain rotameric populations. The ¹³C chemical shifts indicate that hydrophobic methyl-rich residues and aromatic residues exhibit larger static sidechain conformational disorder in amyloid fibrils than in β-barrels. In comparison, hydroxyl- and amide-containing polar residues have more ordered sidechains and more ordered backbones in amyloid fibrils than in β-barrels. These trends can be explained by steric zipper interactions between β-sheet planes in cross-β fibrils, and by the interactions of β-barrel residues with lipid and water in the membrane. These conformational trends should be useful for structural analysis of amyloid fibrils and β-barrels based principally on NMR chemical shifts.

Nuclear magnetic resonance spectroscopy is used routinely for studying the three-dimensional structures and dynamics of proteins and nucleic acids. Structure determination is usually done by adding restraints based upon NMR data to a classical energy function and performing restrained molecular simulations. Here we report on the implementation of a script to extract NMR restraints from a NMR-STAR file and export it to the GROMACS software. With this package it is possible to model distance restraints, dihedral restraints and orientation restraints. The output from the script is validated by performing simulations with and without restraints, including the ab initio refinement of one peptide.

The dynamics of molecules in solution is usually quantified by the determination of timescale-specific amplitudes of motions. High-resolution nuclear magnetic resonance (NMR) relaxometry experiments—where the sample is transferred to low fields for longitudinal (T₁) relaxation, and back to high field for detection with residue-specific resolution—seeks to increase the ability to distinguish the contributions from motion on timescales slower than a few nanoseconds. However, tumbling of a molecule in solution masks some of these motions. Therefore, we investigate to what extent relaxometry improves timescale resolution, using the “detector” analysis of dynamics. Here, we demonstrate improvements in the characterization of internal dynamics of methyl-bearing side chains by carbon-13 relaxometry in the small protein ubiquitin. We show that relaxometry data leads to better information about nanosecond motions as compared to high-field relaxation data only. Our calculations show that gains from relaxometry are greater with increasing correlation time of rotational diffusion.

A unique aspect of NMR is its capacity to provide integrated insight into both the structure and intrinsic dynamics of biomolecules. Chemical exchange phenomena that often serve as probes of dynamic processes in biological macromolecules can be quantitatively investigated with chemical exchange saturation transfer (CEST) experiments. ²H-decoupling sidebands, however, always occur in the profiles of ¹³CHD₂ ¹³C-CEST experiments when using the simple CW (continuous wave) method, which may obscure the detection of minor dips of excited states. Traditionally, these sidebands are manually eliminated from the profiles before data analysis by removing experimental points in the range of ²H-decoupling field strength?±50?Hz away from the major dips of the ground state on either side of the dips. Unfortunately, this may also eliminate potential minor dips if they overlap with the decoupling sidebands. Here, we developed methods that use pseudo-continuous waves with variable RF amplitudes distributed onto ramps for ²H decoupling. The new methods were thoroughly validated on Bruker spectrometers at a range of fields (¹H frequencies of 600, 700, and 850?MHz, and 1.1 GHz). By using these methods, we successfully removed the sidebands from the NMR profiles of ¹³CHD₂ ¹³C-CEST experiments.