Journal of Biomolecular NMR最新文献

Protein side chain dynamics play a vital role in many biological processes, but differentiating mobile from rigid side chains remains a technical challenge in structural biology. Solution NMR spectroscopy is ideally suited for this but suffers from limited signal-to-noise, signal overlap, and a need for fractional ¹³C or ²H labeling. Here we introduce a simple strategy measuring initial ¹H relaxation rates during a ¹H TOCSY sequence like DIPSI-2, which can be appended to the beginning of any multi-dimensional NMR sequence that begins on ¹H. The TOCSY RF field compels all ¹H atoms to behave similarly under the influence of strong coupling and rotating frame cross-relaxation, so that differences in relaxation rates are due primarily to side chain mobility. We apply the scheme to a thermostable mutant Pin1 WW domain and demonstrate that the observed ¹H relaxation rates correlate well with two independent NMR measures of side-chain dynamics, cross-correlated ¹³C relaxation rates in ¹³C^βH₂ methylene groups and maximum observable ³J couplings sensitive to the χ₁ side chain dihedral angle (³J_Hα,Hβ, ³J_N,Hβ, and ³J_CO,Hβ). The most restricted side chains belong to Trp26 and Asn40, which are closely packed to constitute the folding center of the WW domain. None of the other conserved aromatic residues is as immobile as the first tryptophan side chain of the WW domain. The proposed ¹H relaxation methodology should make it relatively easy to measure side chain dynamics on uniformly ¹⁵N- or ¹³C-labeled proteins, so long as chemical shift assignments are obtainable.

Flexibility between the paramagnetic tag and its protein conjugates is a common yet unresolved issue in the applications of paramagnetic NMR spectroscopy in biological systems. The flexibility greatly attenuates the magnetic anisotropy and compromises paramagnetic effects especially for pseudocontact shift and residual dipolar couplings. Great efforts have been made to improve the rigidity of paramagnetic tag in the protein conjugates, however, the effect of local environment vicinal to the protein ligation site on the paramagnetic effects remains poorly understood. In the present work, the stereospecific effect of chiral tether between the protein and a tag on the paramagnetic effects produced by the tag attached via a D- and L-type linker between the protein and paramagnetic metal chelating moiety was assessed. The remarkable chiral effect of the D- and L-type tether between the tag and the protein on the rigidity of paramagnetic tag is disclosed in a number of protein-tag-Ln complexes. The chiral tether formed between the D-type tag and L-type protein surface minimizes the effect of the local environment surrounding the ligation site on the averaging of paramagnetic tag, which is helpful to preserve the rigidity of a paramagnetic tag in the protein conjugates.

The predominant protein expression host for NMR spectroscopy is Escherichia coli, however, it does not synthesize appropriate post-translation modifications required for mammalian protein function and is not ideal for expressing naturally secreted proteins that occupy an oxidative environment. Mammalian expression platforms can address these limitations; however, these are not amenable to cost-effective uniform ¹⁵ N labeling resulting from highly complex growth media requirements. Yeast expression platforms combine the simplicity of bacterial expression with the capabilities of mammalian platforms, however yeasts require optimization prior to isotope labeling. Yeast expression will benefit from methods to boost protein expression levels and developing labeling conditions to facilitate growth and high isotope incorporation within the target protein. In this work, we describe a novel platform based on the yeast Saccharomyces cerevisiae that simultaneously expresses the Kar2p chaperone and protein disulfide isomerase in the ER to facilitate the expression of secreted proteins. Furthermore, we developed a growth medium for uniform ¹⁵ N labeling. We recovered 2.2 mg/L of uniformly ¹⁵ N-labeled human immunoglobulin (Ig)G1 Fc domain with 90.6% ¹⁵ N labeling. NMR spectroscopy revealed a high degree of similarity between the yeast and mammalian-expressed IgG1 Fc domains. Furthermore, we were able to map the binding interaction between IgG1 Fc and the Z domain through chemical shift perturbations. This platform represents a novel cost-effective strategy for ¹⁵ N-labeled immunoglobulin fragments.

EXSY (exchange spectroscopy) NMR provides the residue-specific equilibrium constants, K, and residue-specific kinetic rate constants, k, of a polypeptide chain in a two-state exchange in the slow exchange regime. A linear free energy relationship (LFER) discovered in a log k versus log K plot is considered to be a physicochemical basis for smooth folding and conformational changes of protein molecules. For accurate determination of the thermodynamic and kinetic parameters, the measurement bias arising from state-specific differences in the R₁ and R₂ relaxation rates of ¹H and other nuclei in HSQC and EXSY experiments must be minimized. Here, we showed that the time-zero HSQC acquisition scheme (HSQC0) is effective for this purpose, in combination with a special analytical method (Π analysis) for EXSY. As an example, we applied the HSQC0 + Π method to the two-state exchange of nukacin ISK-1 in an aqueous solution. Nukacin ISK-1 is a 27-residue lantibiotic peptide containing three mono-sulfide linkages. The resultant bias-free residue-based LFER provided valuable insights into the transition state of the topological interconversion of nukacin ISK-1. We found that two amino acid residues were exceptions in the residue-based LFER relationship. We inferred that the two residues could adopt special conformations in the transition state, to allow the threading of some side chains through a ring structure formed by one of the mono-sulfide linkages. In this context, the two residues are a useful target for the manipulation of the physicochemical properties and biological activities of nukacin ISK-1.

Macromolecules often exchange between functional states on timescales that can be accessed with NMR spectroscopy and many NMR tools have been developed to characterise the kinetics and thermodynamics of the exchange processes, as well as the structure of the conformers that are involved. However, analysis of the NMR data that report on exchanging macromolecules often hinges on complex least-squares fitting procedures as well as human experience and intuition, which, in some cases, limits the widespread use of the methods. The applications of deep neural networks (DNNs) and artificial intelligence have increased significantly in the sciences, and recently, specifically, within the field of biomolecular NMR, where DNNs are now available for tasks such as the reconstruction of sparsely sampled spectra, peak picking, and virtual decoupling. Here we present a DNN for the analysis of chemical exchange saturation transfer (CEST) data reporting on two- or three-site chemical exchange involving sparse state lifetimes of between approximately 3–60 ms, the range most frequently observed via experiment. The work presented here focuses on the ¹H CEST class of methods that are further complicated, in relation to applications to other nuclei, by anti-phase features. The developed DNNs accurately predict the chemical shifts of nuclei in the exchanging species directly from anti-phase ¹H^N CEST profiles, along with an uncertainty associated with the predictions. The performance of the DNN was quantitatively assessed using both synthetic and experimental anti-phase CEST profiles. The assessments show that the DNN accurately determines chemical shifts and their associated uncertainties. The DNNs developed here do not contain any parameters for the end-user to adjust and the method therefore allows for autonomous analysis of complex NMR data that report on conformational exchange.

NMR spectroscopy allows the study of biomolecules in close-to-native conditions. Structural information can be inferred from the NMR spectra when an assignment is available. Protein assignment is usually a time-consuming task, being specially challenging in the case of large, supramolecular systems. Here, we present an extension of existing state-of-the-art strategies for methyl group assignment that partially overcomes signal overlapping and other difficulties associated to isolated methyl groups. Our approach exploits the ability of proteins to populate two or more conformational states, allowing for unique NOE restraints in each protein conformer. The method is compatible with automated assignment algorithms, granting assignments beyond the limits of a single protein state. The approach also benefits from long-range structural restraints obtained from metal-induced pseudocontact shifts (PCS) and paramagnetic relaxation enhancements (PREs). We illustrate the method with the complete assignment of the 199 methyl groups of a MIL^proSV^proSAT methyl-labeled sample of the UDP-glucose pyrophosphorylase enzyme from Leishmania major (LmUGP). Protozoan parasites of the genus Leishmania causes Leishmaniasis, a neglected disease affecting over 12 million people worldwide. LmUGP is responsible for the de novo biosynthesis of uridine diphosphate-glucose, a precursor in the biosynthesis of the dense surface glycocalyx involved in parasite survival and infectivity. NMR experiments with LmUGP and related enzymes have the potential to unravel new insights in the host resistance mechanisms used by Leishmania major. Our efforts will help in the development of selective and efficient drugs against Leishmania.

Rapid progress in machine learning offers new opportunities for the automated analysis of multidimensional NMR spectra ranging from protein NMR to metabolomics applications. Most recently, it has been demonstrated how deep neural networks (DNN) designed for spectral peak picking are capable of deconvoluting highly crowded NMR spectra rivaling the facilities of human experts. Superior DNN-based peak picking is one of a series of critical steps during NMR spectral processing, analysis, and interpretation where machine learning is expected to have a major impact. In this perspective, we lay out some of the unique strengths as well as challenges of machine learning approaches in this new era of automated NMR spectral analysis. Such a discussion seems timely and should help define common goals for the NMR community, the sharing of software tools, standardization of protocols, and calibrate expectations. It will also help prepare for an NMR future where machine learning and artificial intelligence tools will be common place.

Sulfur-containing sites in proteins are of great importance for both protein structure and function, including enzymatic catalysis, signaling pathways, and recognition of ligands and protein partners. Selenium-77 is an NMR active spin-1/2 nucleus that shares many physiochemical properties with sulfur and can be readily introduced into proteins at sulfur sites without significant perturbations to the protein structure. The sulfur-containing amino acid methionine is commonly found at protein–protein or protein–ligand binding sites. Its selenium-containing counterpart, selenomethionine, has a broad chemical shift dispersion useful for NMR-based studies of complex systems. Methods such as (¹H)-⁷⁷Se-¹³C double cross polarization or {⁷⁷Se}-¹³C REDOR could be valuable to map the local environment around selenium sites in proteins but have not been demonstrated to date. In this work, we explore these dipolar transfer mechanisms for structural characterization of the GB1 V39SeM variant of the model protein GB1 and demonstrate that ⁷⁷Se-¹³C based correlations can be used to map the local environment around selenium sites in proteins. We have found that the general detection limit is ~ 5 Å, but longer range distances up to ~ 7 Å can be observed as well. This study establishes a framework for the future characterization of selenium sites at protein–protein or protein–ligand binding interfaces.

Recent advances in the field of protein structure determination using liquid-state NMR enable the elucidation of multi-state protein conformations that can provide insight into correlated and non-correlated protein dynamics at atomic resolution. So far, NMR-derived multi-state structures were typically evaluated by means of visual inspection of structure superpositions, target function values that quantify the violation of experimented restraints and root-mean-square deviations that quantify similarity between conformers. As an alternative or complementary approach, we present here the use of a recently introduced structural correlation measure, PDBcor, that quantifies the clustering of protein states as an additional measure for multi-state protein structure analysis. It can be used for various assays including the validation of experimental distance restraints, optimization of the number of protein states, estimation of protein state populations, identification of key distance restraints, NOE network analysis and semiquantitative analysis of the protein correlation network. We present applications for the final quality analysis stages of typical multi-state protein structure calculations.