Journal of Biomolecular NMR最新文献

Determination of protein structure and dynamics is key to understand the mechanism of protein action. Perdeuterated proteins have been used to obtain high resolution/sensitivty NMR experiments via proton-detection. These methods utilizes ¹H, ¹³C and ¹⁵N nuclei for chemical shift dispersion or relaxation probes, despite the existing abundant deuterons. However, a high-sensitivity NMR method to utilize deuterons and e.g. determine site-specific deuterium quadrupolar pattern information has been lacking due to technical difficulties associated with deuterium’s large quadrupolar couplings. Here, we present a novel deuterium-excited and proton-detected three-dimensional ²H–¹³C–¹H MAS NMR experiment to utilize deuterons and to obtain site-specific methyl ²H quadrupolar patterns on detuterated proteins for the first time. A high-resolution fingerprint ¹H–¹⁵N HSQC-spectrum is correlated with the anisotropic deuterium quadrupolar tensor in the third dimension. Results from a model perdeuterated protein has been shown.

NMR-spectroscopy has certain unique advantages for recording unfolding transitions of proteins compared e.g. to optical methods. It enables per-residue monitoring and separate detection of the folded and unfolded state as well as possible equilibrium intermediates. This allows a detailed view on the state and cooperativity of folding of the protein of interest and the correct interpretation of subsequent experiments. Here we summarize in detail practical and theoretical aspects of such experiments. Certain pitfalls can be avoided, and meaningful simplification can be made during the analysis. Especially a good understanding of the NMR exchange regime and relaxation properties of the system of interest is beneficial. We show by a global analysis of signals of the folded and unfolded state of GB1 how accurate values of unfolding can be extracted and what limits different NMR detection and unfolding methods. E.g. commonly used exchangeable amides can lead to a systematic under determination of the thermodynamic protein stability. We give several perspectives of how to deal with more complex proteins and how the knowledge about protein stability at residue resolution helps to understand protein properties under crowding conditions, during phase separation and under high pressure.

Mammalian cells are widely used for producing recombinant glycoproteins of pharmaceutical interest. However, a major drawback of using mammalian cells is the high production costs associated with uniformly isotope-labeled glycoproteins due to the large quantity of labeled l-glutamine required for their growth. To address this problem, we developed a cost-saving method for uniform isotope labeling by cultivating the mammalian cells under glutamine-free conditions, which was achieved by co-expression of glutamine synthase. We demonstrate the utility of this approach using fucosylated and non-fucosylated Fc glycoforms of human immunoglobulin G1.

Solid-state NMR spectroscopy is a powerful technique to study insoluble and non-crystalline proteins and protein complexes at atomic resolution. The development of proton (¹H) detection at fast magic-angle spinning (MAS) has considerably increased the analytical capabilities of the technique, enabling the acquisition of ¹H-detected fingerprint experiments in few hours. Here an approach based on double-quantum (DQ) ¹³C spectroscopy, detected on ¹H, is proposed for fast MAS regime (> 60 kHz) to perform the sequential assignment of insoluble proteins of small size, without any specific deuteration requirement. By combining two three-dimensional ¹H detected experiments correlating a ¹³C DQ dimension respectively to its intra-residue and sequential ¹⁵ N-¹H pairs, a sequential walk through DQ (Ca + CO) resonance is obtained. The approach takes advantage of fast MAS to achieve an efficient sensitivity and the addition of a DQ dimension provides spectral features useful for the resonance assignment process.

Non-uniform sampling (NUS) is a popular way of reducing the amount of time taken by multidimensional NMR experiments. Among the various non-uniform sampling schemes that exist, the Poisson-gap (PG) schedules are particularly popular, especially when combined with compressed-sensing (CS) reconstruction of missing data points. However, the use of PG is based mainly on practical experience and has not, as yet, been explained in terms of CS theory. Moreover, an apparent contradiction exists between the reported effectiveness of PG and CS theory, which states that a “flat” pseudo-random generator is the best way to generate sampling schedules in order to reconstruct sparse spectra. In this paper we explain how, and in what situations, PG reveals its superior features in NMR spectroscopy. We support our theoretical considerations with simulations and analyses of experimental data from the Biological Magnetic Resonance Bank (BMRB). Our analyses reveal a previously unnoticed feature of many NMR spectra that explains the success of ”blue-noise” schedules, such as PG. We call this feature “clustered sparsity”. This refers to the fact that the peaks in NMR spectra are not just sparse but often form clusters in the indirect dimension, and PG is particularly suited to deal with such situations. Additionally, we discuss why denser sampling in the initial and final parts of the clustered signal may be useful.

This study introduces a conceptually new solvent suppression scheme with adiabatic inversion pulses for ¹H-detected multidimensional solid-state NMR (SSNMR) of biomolecules and other systems, which is termed “Solvent suppression of Liquid signal with Adiabatic Pulse” (SLAP). ¹H-detected 2D ¹³C/¹H SSNMR data of uniformly ¹³C- and ¹⁵N-labeled GB1 sample using ultra-fast magic angle spinning at a spinning rate of 60 kHz demonstrated that the SLAP scheme showed up to 3.5-fold better solvent suppression performance over a traditional solvent-suppression scheme for SSNMR, MISSISSIPPI (Zhou and Rienstra, J Magn Reson 192:167–172, 2008) with 2/3 of the average RF power.

Protein-ligand interaction is one of the highlights of molecular recognition. The most popular application of this type of interaction is drug development which requires a high throughput screening of a ligand that binds to the target protein. Our goal was to find a binding ligand with a simple detection, and once this type of ligand was found, other methods could then be used to measure the detailed kinetic or thermodynamic parameters. We started with the idea that the ligand NMR signal would disappear if it was bound to the non-tumbling mass. In order to create the non-tumbling mass, we tried the aggregates of a target protein, which was fused to the elastin-like polypeptide. We chose the maltose binding proteinas a test case, and we tried it with several sugars, which included maltose, glucose, sucrose, lactose, galactose, maltotriose, and β-cyclodextrin. The maltose signal in the H-1 NMR spectrum disappeared completely as hoped around the protein to ligand ratio of 1:3 at 298 K where the proteins aggregated. The protein signals also disappeared upon aggregation except for the fast-moving part, which resulted in a cleaner background than the monomeric form. Since we only needed to look for a disappearing signal amongst those from the mixture, it should be useful in high throughput screening. Other types of sugars except for the maltotriose and β-cyclodextrin, which are siblings of the maltose, did not seem to bind at all. We believe that our system would be especially more effective when dealing with a smaller target protein, so both the protein and the bound ligand would lose their signals only when the aggregates formed. We hope that our proposed method would contribute to accelerating the development of the potent drug candidates by simultaneously identifying several binders directly from a mixture.

Aromatic side chains are attractive probes of protein dynamic, since they are often key residues in enzyme active sites and protein binding sites. Dynamic processes on microsecond to millisecond timescales can be studied by relaxation dispersion experiments that attenuate conformational exchange contributions to the transverse relaxation rate by varying the refocusing frequency of applied radio-frequency fields implemented as either CPMG pulse trains or continuous spin-lock periods. Here we present an aromatic ¹H R_1ρ relaxation dispersion experiment enabling studies of two to three times faster exchange processes than achievable by existing experiments for aromatic side chains. We show that site-specific isotope labeling schemes generating isolated ¹H–¹³C spin pairs with vicinal ²H–¹²C moieties are necessary to avoid anomalous relaxation dispersion profiles caused by Hartmann–Hahn matching due to the ³J_HH couplings and limited chemical shift differences among ¹H spins in phenylalanine, tyrosine and the six-ring moiety of tryptophan. This labeling pattern is sufficient in that remote protons do not cause additional complications. We validated the approach by measuring ring-flip kinetics in the small protein GB1. The determined rate constants, k_flip, agree well with previous results from ¹³C R_1ρ relaxation dispersion experiments, and yield ¹H chemical shift differences between the two sides of the ring in good agreement with values measured under slow-exchange conditions. The aromatic¹H R_1ρ relaxation dispersion experiment in combination with the site-selective ¹H–¹³C/²H–¹²C labeling scheme enable measurement of exchange rates up to k_ex = 2k_flip = 80,000 s^–1, and serve as a useful complement to previously developed ¹³C-based methods.

Protein secondary structure provides rich structural information, hence the description and understanding of protein structure relies heavily on it. Identification or prediction of secondary structures therefore plays an important role in protein research. In protein NMR studies, it is more convenient to predict secondary structures from chemical shifts as compared to the traditional determination methods based on inter-nuclear distances provided by NOESY experiment. In recent years, there was a significant improvement observed in deep neural networks, which had been applied in many research fields. Here we proposed a deep neural network based on bidirectional long short term memory (biLSTM) to predict protein 3-state secondary structure using NMR chemical shifts of backbone nuclei. While comparing with the existing methods the proposed method showed better prediction accuracy. Based on the proposed method, a web server has been built to provide protein secondary structure prediction service.