Journal of Biomolecular NMR最新文献

The NMR signals from protein sidechains are rich in information about intra- and inter-molecular interactions, but their detection can be complicated due to spectral overlap as well as conformational and hydrogen exchange. In this work, we demonstrate a protocol for multi-dimensional solid-state NMR spectral editing of signals from basic sidechains based on Hadamard matrix encoding. The Hadamard method acquires multi-dimensional experiments in such a way that both the backbone and under-sampled sidechain signals can be decoded for unambiguous editing in the ¹⁵N spectral frequency dimension. All multi-dimensional ¹⁵N-edited solid-state NMR experiments can be acquired using this strategy, thereby accelerating the acquisition of spectra spanning broad frequency bandwidth. Application of these methods to the ferritin nanocage, reveals signals from N atoms from His, Arg, Lys and Trp sidechains, as well as their tightly bound, ordered water molecules. The Hadamard approach adds to the arsenal of spectroscopic approaches for protein NMR signal detection.

Intrinsically disordered proteins and protein regions are central to many biological processes but difficult to characterize at atomic resolution. Nuclear magnetic resonance is particularly well-suited for providing structural and dynamical information on intrinsically disordered proteins, but existing NMR methodologies need to be constantly refined to provide greater sensitivity and resolution, particularly to capitalise on the potential of high magnetic fields to investigate large proteins. In this paper, we describe how ¹⁵N-detected 2D NMR experiments can be optimised for better performance. We show that using selective aliphatic ¹H decoupling in N-TROSY type experiments results in significant increases in sensitivity and resolution for a prototypical intrinsically disordered protein, α-synuclein, as well as for a heterogeneous intrinsically disordered region of a large multidomain protein, CBP-ID4. We also investigated the performance of incorporating longitudinal relaxation enhancement in N-TROSY experiments, both with and without aliphatic ¹H decoupling, and discussed the findings in light of the available information for the two systems.

Chemical shift assignments of large membrane proteins by solid-state NMR experiments are challenging. Recent advancements in sensitivity-enhanced pulse sequences, have made it feasible to acquire ¹H-detected 4D spectra of these challenging protein samples within reasonable timeframes. However, obtaining unambiguous assignments remains difficult without access to side-chain chemical shifts. Drawing inspiration from sensitivity-enhanced TOCSY experiments in solution NMR, we have explored the potential of ¹³C- ¹³C TOCSY mixing as a viable option for triple sensitivity-enhanced 4D experiments aimed at side-chain assignments in solid-state NMR. Through simulations and experimental trials, we have identified optimal conditions to achieve uniform transfer efficiency for both transverse components and to minimize undesired cross-transfers. Our experiments, conducted on the 30 kDa membrane protein GlpG embedded in E. coli liposomes, have demonstrated enhanced sensitivity compared to the most effective dipolar and J-coupling-based ¹³C- ¹³C mixing sequences. Notably, a non-uniformly sampled 4D hCXCANH spectrum with exceptionally high sensitivity was obtained in just a few days using a 600 MHz spectrometer equipped with a 1.3 mm probe operating at a magic angle spinning rate of 55 kHz.

Inclusion of residual dipolar couplings (RDCs) during the early rounds of protein structure determination requires use of a floating alignment tensor or knowledge of the alignment tensor strength and rhombicity. For proteins with interdomain motion, such analysis can falsely hide the presence of domain dynamics. We demonstrate for three proteins, maltotriose-ligated maltose binding protein (MBP), Ca²⁺-ligated calmodulin, and a monomeric N-terminal deletion mutant of the SARS-CoV-2 Main Protease, MPro, that good alignment tensor estimates of their domains can be obtained from RDCs measured for residues that are identified as α-helical based on their chemical shifts. The program, Helix-Fit, fits the RDCs to idealized α-helical coordinates, often yielding a comparable or better alignment tensor estimate than fitting to the actual high-resolution X-ray helix coordinates. The 13 helices of ligated MBP all show very similar alignment tensors, indicative of a high degree of order relative to one another. By contrast, while for monomeric MPro the alignment strengths of the five helices in the C-terminal helical domain (residues 200–306) are very similar, pointing to a well-ordered domain, the single α-helix Y54-I59 in the N-terminal catalytic domain (residues 10–185) aligns considerably weaker. This result indicates the presence of large amplitude motions of either Y54-I59 or of the entire N-terminal domain relative to the C-terminal domain, contrasting with the high degree of order seen in the native homodimeric structure.

The resonance assignment of large intrinsically disordered proteins (IDPs) is difficult due to the low dispersion of chemical shifts (CSs). Luckily, CSs are often specific for certain residue types, which makes the task easier. Our recent work showed that the CS-based spin-system classification can be improved by applying a linear discriminant analysis (LDA). In this paper, we extend a set of classification parameters by adding temperature coefficients (TCs), i.e., rates of change of chemical shifts with temperature. As demonstrated previously by other groups, the TCs in IDPs depend on a residue type, although the relation is often too complex to be predicted theoretically. Thus, we propose an approach based on experimental data; CSs and TCs values of residues assigned using conventional methods serve as a training set for LDA, which then classifies the remaining resonances. The method is demonstrated on a large fragment (1-239) of highly disordered protein Tau. We noticed that adding TCs to sets of chemical shifts significantly improves the recognition efficiency. For example, it allows distinguishing between lysine and glutamic acid, as well as valine and isoleucine residues based on ({{text {H}}^{text {N}}}), N, ({{text {C}}_alpha }) and C(^{prime }) data. Moreover, adding TCs to CSs of ({{text {H}}^{text {N}}}), N, ({{text {C}}_alpha }), and C(^{prime }) is more beneficial than adding ({{text {C}}_beta }) CSs. Our program for LDA analysis is available at https://github.com/gugumatz/LDA-Temp-Coeff.