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The three-dimensional structure determination of RNAs by NMR spectroscopy relies on chemical shift assignment, which still constitutes a bottleneck. In order to develop more efficient assignment strategies, we analysed relationships between sequence and 1H and 13C chemical shifts. Statistics of resonances from regularly Watson– Crick base-paired RNA revealed highly characteristic chemical shift clusters. We developed two approaches using these statistics for chemical shift assignment of double-stranded RNA (dsRNA): a manual approach that yields starting points for resonance assignment and simplifies decision trees and an automated approach based on the recently introduced automated resonance assignment algorithm FLYA. Both strategies require only unlabeled RNAs and three 2D spectra for assigning the H2/C2, H5/C5, H6/C6, H8/C8 and H10/C10 chemical shifts. The manual approach proved to be efficient and robust when applied to the experimental data of RNAs with a size between 20 nt and 42 nt. The more advanced automated assignment approach was successfully applied to four stemloop RNAs and a 42 nt siRNA, assigning 92–100% of the resonances from dsRNA regions correctly. This is the first automated approach for chemical shift assignment of non-exchangeable protons of RNA and their corresponding 13C resonances, which provides an important step toward automated structure determination of RNAs.
A B-factor for NOEs?
(2022)
Nuclear Overhauser effects (NOEs) are influenced by motion. Here, we derive exact, analytical results for a model of isotropic, harmonic fluctuations of atom positions that corresponds to the one underlying crystallographic B-factors. The model includes steric repulsion and yields closed-form expressions for the expected value of general invertible functions of the distance between two atoms, with the special case r-6 for NOEs. We discuss the implications for the definition of an NOE-based B-factor in solution NMR.
To date, in-cell NMR has elucidated various aspects of protein behaviour by associating structures in physiological conditions. Meanwhile, current studies of this method mostly have deduced protein states in cells exclusively based on ‘indirect’ structural information from peak patterns and chemical shift changes but not ‘direct’ data explicitly including interatomic distances and angles. To fully understand the functions and physical properties of proteins inside cells, it is indispensable to obtain explicit structural data or determine three-dimensional (3D) structures of proteins in cells. Whilst the short lifetime of cells in a sample tube, low sample concentrations, and massive background signals make it difficult to observe NMR signals from proteins inside cells, several methodological advances help to overcome the problems. Paramagnetic effects have an outstanding potential for in-cell structural analysis. The combination of a limited amount of experimental in-cell data with software for ab initio protein structure prediction opens an avenue to visualise 3D protein structures inside cells. Conventional nuclear Overhauser effect spectroscopy (NOESY)-based structure determination is advantageous to elucidate the conformations of side-chain atoms of proteins as well as global structures. In this article, we review current progress for the structure analysis of proteins in living systems and discuss the feasibility of its future works.
Investigating three-dimensional (3D) structures of proteins in living cells by in-cell nuclear magnetic resonance (NMR) spectroscopy opens an avenue towards understanding the structural basis of their functions and physical properties under physiological conditions inside cells. In-cell NMR provides data at atomic resolution non-invasively, and has been used to detect protein-protein interactions, thermodynamics of protein stability, the behavior of intrinsically disordered proteins, etc. in cells. However, so far only a single de novo 3D protein structure could be determined based on data derived only from in-cell NMR. Here we introduce methods that enable in-cell NMR protein structure determination for a larger number of proteins at concentrations that approach physiological ones. The new methods comprise (1) advances in the processing of non-uniformly sampled NMR data, which reduces the measurement time for the intrinsically short-lived in-cell NMR samples, (2) automatic chemical shift assignment for obtaining an optimal resonance assignment, and (3) structure refinement with Bayesian inference, which makes it possible to calculate accurate 3D protein structures from sparse data sets of conformational restraints. As an example application we determined the structure of the B1 domain of protein G at about 250 μM concentration in living E. coli cells.
In every established species, protein-protein interactions have evolved such that they are fit for purpose. However, the molecular details of the evolution of new protein-protein interactions are poorly understood. We have used nuclear magnetic resonance spectroscopy to investigate the changes in structure and dynamics during the evolution of a protein-protein interaction involving the intrinsically disordered CREBBP (CREB-binding protein) interaction domain (CID) and nuclear coactivator binding domain (NCBD) from the transcriptional coregulators NCOA (nuclear receptor coactivator) and CREBBP/p300, respectively. The most ancient low-affinity “Cambrian-like” [540 to 600 million years (Ma) ago] CID/NCBD complex contained less secondary structure and was more dynamic than the complexes from an evolutionarily younger “Ordovician-Silurian” fish ancestor (ca. 440 Ma ago) and extant human. The most ancient Cambrian-like CID/NCBD complex lacked one helix and several interdomain interactions, resulting in a larger solvent-accessible surface area. Furthermore, the most ancient complex had a high degree of millisecond-to-microsecond dynamics distributed along the entire sequences of both CID and NCBD. These motions were reduced in the Ordovician-Silurian CID/NCBD complex and further redistributed in the extant human CID/NCBD complex. Isothermal calorimetry experiments show that complex formation is enthalpically favorable and that affinity is modulated by a largely unfavorable entropic contribution to binding. Our data demonstrate how changes in structure and motion conspire to shape affinity during the evolution of a protein-protein complex and provide direct evidence for the role of structural, dynamic, and frustrational plasticity in the evolution of interactions between intrinsically disordered proteins.
Background: The automation of objectively selecting amino acid residue ranges for structure superpositions is important for meaningful and consistent protein structure analyses. So far there is no widely-used standard for choosing these residue ranges for experimentally determined protein structures, where the manual selection of residue ranges or the use of suboptimal criteria remain commonplace. Results: We present an automated and objective method for finding amino acid residue ranges for the superposition and analysis of protein structures, in particular for structure bundles resulting from NMR structure calculations. The method is implemented in an algorithm, CYRANGE, that yields, without protein-specific parameter adjustment, appropriate residue ranges in most commonly occurring situations, including low-precision structure bundles, multi-domain proteins, symmetric multimers, and protein complexes. Residue ranges are chosen to comprise as many residues of a protein domain that increasing their number would lead to a steep rise in the RMSD value. Residue ranges are determined by first clustering residues into domains based on the distance variance matrix, and then refining for each domain the initial choice of residues by excluding residues one by one until the relative decrease of the RMSD value becomes insignificant. A penalty for the opening of gaps favours contiguous residue ranges in order to obtain a result that is as simple as possible, but not simpler. Results are given for a set of 37 proteins and compared with those of commonly used protein structure validation packages. We also provide residue ranges for 6351 NMR structures in the Protein Data Bank. Conclusions: The CYRANGE method is capable of automatically determining residue ranges for the superposition of protein structure bundles for a large variety of protein structures. The method correctly identifies ordered regions. Global structure superpositions based on the CYRANGE residue ranges allow a clear presentation of the structure, and unnecessary small gaps within the selected ranges are absent. In the majority of cases, the residue ranges from CYRANGE contain fewer gaps and cover considerably larger parts of the sequence than those from other methods without significantly increasing the RMSD values. CYRANGE thus provides an objective and automatic method for standardizing the choice of residue ranges for the superposition of protein structures. Additional files Additional file 1: Dependence of Q on the order parameter rank. The quantity Qi is plotted against the order parameter rank i for 9 different protein structure bundles. Additional file 2: Dependence of P on the clustering stage. The quantity Pi is plotted against the clustering stage i for 9 different protein structure bundles. Additional file 3: Dependence of CYRANGE results on the minimal cluster size parameter my. The sequence coverage (red) and RMSD (blue) of the residue ranges determined by CYRANGE were plotted as a function of my for 9 different protein structure bundles. The dotted vertical line indicates the default value, my = 8. Where CYRANGE found two domains, the RMSD values of the individual domains are shown in light and dark blue. Additional file 4: Dependence of CYRANGE results on the domain boundary extension parameter m. See Additional File 3 for details. Additional file 5: Dependence of CYRANGE results on the minimal gap width g. See Additional File 3 for details. Additional file 6: Dependence of CYRANGE results on the relative RMSD decrease parameter delta. See Additional File 3 for details. Additional file 7: Dependence of CYRANGE results on the absolute RMSD decrease parameter delta abs. See Additional File 3 for details. Additional file 8: Dependence of CYRANGE results on the gap penalty parameter gamma. See Additional File 3 for details. Additional file 9: Correlation between the sequence coverage from CYRANGE, FindCore and PSVS, and the GDT total score, GDT_TS. Each data point represents a protein shown in Figures 3 and 4. The coverage is the percentage of amino acid residues included in the residue ranges found by the different methods. The GDT_TS value is defined by GDT_TS = (P1 + P2 + P4 + P8)/4, where Pd is the fraction of residues that can be superimposed under a distance cutoff of d Å. Additional file 10: Correlation between the RMSD value for the residue ranges from CYRANGE, FindCore and PSVS, and the GDT total score, GDT_TS. Each data point represents one protein domain. See Additional File 9 for details.
The YaeJ protein is a codon-independent release factor with peptidyl-tRNA hydrolysis (PTH) activity, and functions as a stalled-ribosome rescue factor in Escherichia coli. To identify residues required for YaeJ function, we performed mutational analysis for in vitro PTH activity towards rescue of ribosomes stalled on a non-stop mRNA, and for ribosome-binding efficiency. We focused on residues conserved among bacterial YaeJ proteins. Additionally, we determined the solution structure of the GGQ domain of YaeJ from E. coli using nuclear magnetic resonance spectroscopy. YaeJ and a human homolog, ICT1, had similar levels of PTH activity, despite various differences in sequence and structure. While no YaeJ-specific residues important for PTH activity occur in the structured GGQ domain, Arg118, Leu119, Lys122, Lys129 and Arg132 in the following C-terminal extension were required for PTH activity. All of these residues are completely conserved among bacteria. The equivalent residues were also found in the C-terminal extension of ICT1, allowing an appropriate sequence alignment between YaeJ and ICT1 proteins from various species. Single amino acid substitutions for each of these residues significantly decreased ribosome-binding efficiency. These biochemical findings provide clues to understanding how YaeJ enters the A-site of stalled ribosomes.
Resonance assignments are challenging for membrane proteins due to the size of the lipid/detergent-protein complex and the presence of line-broadening from conformational exchange. As a consequence, many correlations are missing in the triple-resonance NMR experiments typically used for assignments. Herein, we present an approach in which correlations from these solution-state NMR experiments are supplemented by data from 13C unlabeling, single-amino acid type labeling, 4D NOESY data and proximity of moieties to lipids or water in combination with a structure of the protein. These additional data are used to edit the expected peaklists for the automated assignment protocol FLYA, a module of the program package CYANA. We demonstrate application of the protocol to the 262-residue proton pump from archaeal bacteriorhodopsin (bR) in lipid nanodiscs. The lipid-protein assembly is characterized by an overall correlation time of 44 ns. The protocol yielded assignments for 62% of all backbone (H, N, Cα, Cβ, C′) resonances of bR, corresponding to 74% of all observed backbone spin systems, and 60% of the Ala, Met, Ile (δ1), Leu and Val methyl groups, thus enabling to assign a large fraction of the protein without mutagenesis data. Most missing resonances stem from the extracellular half, likely due intermediate exchange line-broadening. Further analysis revealed that missing information of the amino acid type of the preceding residue is the largest problem, and that 4D NOESY experiments are particularly helpful to compensate for that information loss.
The spliceosomal protein SF3b49, a component of the splicing factor 3b (SF3b) protein complex in the U2 small nuclear ribonucleoprotein, contains two RNA recognition motif (RRM) domains. In yeast, the first RRM domain (RRM1) of Hsh49 protein (yeast orthologue of human SF3b49) reportedly interacts with another component, Cus1 protein (orthologue of human SF3b145). Here, we solved the solution structure of the RRM1 of human SF3b49 and examined its mode of interaction with a fragment of human SF3b145 using NMR methods. Chemical shift mapping showed that the SF3b145 fragment spanning residues 598-631 interacts with SF3b49 RRM1, which adopts a canonical RRM fold with a topology of β1-α1-β2-β3-α2-β4. Furthermore, a docking model based on NOESY measurements suggests that residues 607-616 of the SF3b145 fragment adopt a helical structure that binds to RRM1 predominantly via α1, consequently exhibiting a helix-helix interaction in almost antiparallel. This mode of interaction was confirmed by a mutational analysis using GST pull-down assays. Comparison with structures of all RRM domains when complexed with a peptide found that this helix-helix interaction is unique to SF3b49 RRM1. Additionally, all amino acid residues involved in the interaction are well conserved among eukaryotes, suggesting evolutionary conservation of this interaction mode between SF3b49 RRM1 and SF3b145.
The Taiwan cobra (Naja naja atra) chymotrypsin inhibitor (NACI) consists of 57 amino acids and is related to other Kunitz-type inhibitors such as bovine pancreatic trypsin inhibitor (BPTI) and Bungarus fasciatus fraction IX (BF9), another chymotrypsin inhibitor. Here we present the solution structure of NACI. We determined the NMR structure of NACI with a root-mean-square deviation of 0.37 Å for the backbone atoms and 0.73 Å for the heavy atoms on the basis of 1,075 upper distance limits derived from NOE peaks measured in its NOESY spectra. To investigate the structural characteristics of NACI, we compared the three-dimensional structure of NACI with BPTI and BF9. The structure of the NACI protein comprises one 310-helix, one α-helix and one double-stranded antiparallel β-sheet, which is comparable with the secondary structures in BPTI and BF9. The RMSD value between the mean structures is 1.09 Å between NACI and BPTI and 1.27 Å between NACI and BF9. In addition to similar secondary and tertiary structure, NACI might possess similar types of protein conformational fluctuations as reported in BPTI, such as Cys14–Cys38 disulfide bond isomerization, based on line broadening of resonances from residues which are mainly confined to a region around the Cys14–Cys38 disulfide bond.