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Multidimensional NMR spectra are the basis for studying proteins by NMR spectroscopy and crucial for the development and evaluation of methods for biomolecular NMR data analysis. Nevertheless, in contrast to derived data such as chemical shift assignments in the BMRB and protein structures in the PDB databases, this primary data is in general not publicly archived. To change this unsatisfactory situation, we present a standardized set of solution NMR data comprising 1329 2–4-dimensional NMR spectra and associated reference (chemical shift assignments, structures) and derived (peak lists, restraints for structure calculation, etc.) annotations. With the 100-protein NMR spectra dataset that was originally compiled for the development of the ARTINA deep learning-based spectra analysis method, 100 protein structures can be reproduced from their original experimental data. The 100-protein NMR spectra dataset is expected to help the development of computational methods for NMR spectroscopy, in particular machine learning approaches, and enable consistent and objective comparisons of these methods. [ABSTRACT FROM AUTHOR]

Peptides and proteins containing non-canonical amino acids (ncAAs) are a large and important class of biopolymers. They include non-ribosomally synthesised peptides, post-translationally modified proteins, expressed or synthesised proteins containing unnatural amino acids, and peptides and proteins that are chemically modified. Here, we describe a general procedure for generating atomic descriptions required to incorporate ncAAs within popular NMR structure determination software such as CYANA, CNS, Xplor-NIH and ARIA. This procedure is made publicly available via the existing Automated Topology Builder (ATB) server (https://atb.uq.edu.au , last access: 17 February 2023) with all submitted ncAAs stored in a dedicated database. The described procedure also includes a general method for linking of side chains of amino acids from CYANA templates. To ensure compatibility with other systems, atom names comply with IUPAC guidelines. In addition to describing the workflow, 3D models of complex natural products generated by CYANA are presented, including vancomycin. In order to demonstrate the manner in which the templates for ncAAs generated by the ATB can be used in practice, we use a combination of CYANA and CNS to solve the structure of a synthetic peptide designed to disrupt Alzheimer-related protein–protein interactions. Automating the generation of structural templates for ncAAs will extend the utility of NMR spectroscopy to studies of more complex biomolecules, with applications in the rapidly growing fields of synthetic biology and chemical biology. The procedures we outline can also be used to standardise the creation of structural templates for any amino acid and thus have the potential to impact structural biology more generally. [ABSTRACT FROM AUTHOR]

Most experimental methods for structural biology proceed in vitro and therefore the contribution of the intracellular environment on protein structure and dynamics is absent. Studying proteins at atomic resolution in living mammalian cells has been elusive due to the lack of methodologies. In-cell nuclear magnetic resonance spectroscopy (in-cell NMR) is an emerging technique with the power to do so. Here, we improved current methods of in-cell NMR by the development of a reporter system that allows monitoring the delivery of exogenous proteins into mammalian cells, a process that we called here "transexpression". The reporter system was used to develop an efficient protocol for in-cell NMR which enables spectral acquisition with higher quality for both disordered and folded proteins. With this method, the 3D atomic resolution structure of the model protein GB1 in human cells was determined with a backbone root-mean-square deviation (RMSD) of 1.1 Å. An improved method for in-cell NMR is developed using a reporter to evaluate protein delivery and delivery parameters to different cell lines and applied to IDPs and folded proteins including structure determination of GB1 at 1.1 Å resolution. [ABSTRACT FROM AUTHOR]

Peptides and proteins containing non-canonical amino acids (ncAAs) are a large and important class of biopolymers. They include non-ribosomally synthesised peptides, post-translationally modified proteins, expressed or synthesised proteins containing unnatural amino acids, and peptides and proteins that are chemically modified. Here, we describe a general procedure for generating atomic descriptions required to incorporate ncAAs within popular NMR structure determination software such as CYANA, CNS, Xplor-NIH and ARIA. This procedure is made publicly available via the existing Automated Topology Builder (ATB) server (atb.uq.edu.au) with all submitted ncAAs stored in a dedicated database. The described procedure also includes a general method for linking of sidechains of amino acids from CYANA templates. To ensure compatibility with other systems, atom names comply with IUPAC guidelines. In addition to describing the workflow, 3D models of complex natural products generated by CYANA are presented, including vancomycin. In order to demonstrate the manner in which the templates for ncAAs generated by the ATB can be used in practice we use a combination of CYANA and CNS to solve the structure of a synthetic peptide designed to disrupt Alzheimer-related protein-protein interactions. Automating the generation of structural templates for ncAAs will extend the utility of NMR spectroscopy to studies of more complex biomolecules, with applications in the rapidly growing fields of synthetic and chemical biology. The procedures we outline can also be used to standardise the creation of structural templates for any amino acid and thus have the potential to impact structural biology more generally. [ABSTRACT FROM AUTHOR]

Nuclear Magnetic Resonance (NMR) spectroscopy is a major technique in structural biology with over 11,800 protein structures deposited in the Protein Data Bank. NMR can elucidate structures and dynamics of small and medium size proteins in solution, living cells, and solids, but has been limited by the tedious data analysis process. It typically requires weeks or months of manual work of a trained expert to turn NMR measurements into a protein structure. Automation of this process is an open problem, formulated in the field over 30 years ago. We present a solution to this challenge that enables the completely automated analysis of protein NMR data within hours after completing the measurements. Using only NMR spectra and the protein sequence as input, our machine learning-based method, ARTINA, delivers signal positions, resonance assignments, and structures strictly without human intervention. Tested on a 100-protein benchmark comprising 1329 multidimensional NMR spectra, ARTINA demonstrated its ability to solve structures with 1.44 Å median RMSD to the PDB reference and to identify 91.36% correct NMR resonance assignments. ARTINA can be used by non-experts, reducing the effort for a protein assignment or structure determination by NMR essentially to the preparation of the sample and the spectra measurements. The analysis of protein NMR spectra is time-consuming and can occupy a human expert for weeks or months. The researchers in this work present a deep learning-based method that delivers signal positions, chemical shift assignments, and structures of proteins within hours after completion of the NMR measurements. [ABSTRACT FROM AUTHOR]

Adequate digital resolution and signal sensitivity are two critical factors for protein structure determinations by solution NMR spectroscopy. The prime objective for obtaining high digital resolution is to resolve peak overlap, especially in NOESY spectra with thousands of signals where the signal analysis needs to be performed on a large scale. Achieving maximum digital resolution is usually limited by the practically available measurement time. We developed a method utilizing non-uniform sampling for balancing digital resolution and signal sensitivity, and performed a large-scale analysis of the effect of the digital resolution on the accuracy of the resulting protein structures. Structure calculations were performed as a function of digital resolution for about 400 proteins with molecular sizes ranging between 5 and 33 kDa. The structural accuracy was assessed by atomic coordinate RMSD values from the reference structures of the proteins. In addition, we monitored also the number of assigned NOESY cross peaks, the average signal sensitivity, and the chemical shift spectral overlap. We show that high resolution is equally important for proteins of every molecular size. The chemical shift spectral overlap depends strongly on the corresponding spectral digital resolution. Thus, knowing the extent of overlap can be a predictor of the resulting structural accuracy. Our results show that for every molecular size a minimal digital resolution, corresponding to the natural linewidth, needs to be achieved for obtaining the highest accuracy possible for the given protein size using state-of-the-art automated NOESY assignment and structure calculation methods. [ABSTRACT FROM AUTHOR]

Abstract: The WW domain is known as one of the smallest protein modules with a triple-stranded β-sheet fold. Here, we present the solution structure of the second WW domain from the mouse salvador homolog 1 protein. This WW domain forms a homodimer with a β-clam-like motif, as evidenced by size exclusion chromatography, analytical ultracentrifugation and NMR spectroscopy. While typical WW domains are believed to function as monomeric modules that recognize proline-rich sequences, by using conserved aromatic and hydrophobic residues that are solvent-exposed on the surface of the β-sheet, this WW domain buries these residues in the dimer interface. [Copyright &y& Elsevier]

Nuclear-magnetic-resonance spectroscopy can determine the three-dimensional structure of proteins in solution. However, its potential has been limited by the difficulty of interpreting NMR spectra in the presence of broadened and overlapping resonance lines and low signal-to-noise ratios. Here we present stereo-array isotope labelling (SAIL), a technique that can overcome many of these problems by applying a complete stereospecific and regiospecific pattern of stable isotopes that is optimal with regard to the quality and information content of the resulting NMR spectra. SAIL uses exclusively chemically and enzymatically synthesized amino acids for cell-free protein expression. We demonstrate for the 17-kDa protein calmodulin and the 41-kDa maltodextrin-binding protein that SAIL offers sharpened lines, spectral simplification without loss of information, and the ability to rapidly collect the structural restraints required to solve a high-quality solution structure for proteins twice as large as commonly solved by NMR. It thus makes a large class of proteins newly accessible to detailed solution structure determination. [ABSTRACT FROM AUTHOR]

Describes the solution structure of the mouse enhancer of rudimentary (ER) protein determined by using heteronuclear nuclear magnetic resonance spectroscopy. Role of the regulatory or enzymatic activity of ER in pyrimidine biosynthesis and the cell cycle; Dimer or trimer behavior of ER in solution.

Novel algorithms are presented for automated NOESY peak picking and NOE signal identification in homonuclear 2D and heteronuclear-resolved 3D [1H,1H]-NOESY spectra during de novo protein structure determination by NMR, which have been implemented in the new software ATNOS (automated NOESY peak picking). The input for ATNOS consists of the amino acid sequence of the protein, chemical shift lists from the sequence-specific resonance assignment, and one or several 2D or 3D NOESY spectra. In the present implementation, ATNOS performs multiple cycles of NOE peak identification in concert with automated NOE assignment with the software CANDID and protein structure calculation with the program DYANA. In the second and subsequent cycles, the intermediate protein structures are used as an additional guide for the interpretation of the NOESY spectra. By incorporating the analysis of the raw NMR data into the process of automated de novo protein NMR structure determination, ATNOS enables direct feedback between the protein structure, the NOE assignments and the experimental NOESY spectra. The main elements of the algorithms for NOESY spectral analysis are techniques for local baseline correction and evaluation of local noise level amplitudes, automated determination of spectrum-specific threshold parameters, the use of symmetry relations, and the inclusion of the chemical shift information and the intermediate protein structures in the process of distinguishing between NOE peaks and artifacts. The ATNOS procedure has been validated with experimental NMR data sets of three proteins, for which high-quality NMR structures had previously been obtained by interactive interpretation of the NOESY spectra. The ATNOS-based structures coincide closely with those obtained with interactive peak picking. Overall, we present the algorithms used in this paper as a further important step towards objective and efficient de novo protein structure determination by NMR. [ABSTRACT FROM AUTHOR]

Motivation Multi-dimensional NMR spectra are generally used for NMR signal assignment and structure analysis. There are several programs that can achieve highly automated NMR signal assignments and structure analysis. On the other hand, NMR spectra tend to have a large number of noise peaks even for data acquired with good sample and machine conditions, and it is still difficult to eliminate these noise peaks. Results We have developed a method to eliminate noise peaks using convolutional neural networks, implemented in the program package Filt_Robot. The filtering accuracy of Filt_Robot was around 90–95% when applied to 2D and 3D NMR spectra, and the numbers of resulting non-noise peaks were close to those in corresponding manually prepared peaks lists. The filtering can strongly enhance automated NMR spectra analysis. Availability and implementation The full package of the program, documents and example data are available from http://bmrbdep.pdbj.org/en/nmr%5ftool%5fbox/Filt%5fRobot.html. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Isotopically labeled methyl groups provide NMR probes in large, otherwise deuterated proteins. However, the resonance assignment constitutes a bottleneck for broader applicability of methyl-based NMR. Here, we present the automated MethylFLYA method for the assignment of methyl groups that is based on methyl-methyl nuclear Overhauser effect spectroscopy (NOESY) peak lists. MethylFLYA is applied to five proteins (28–358 kDa) comprising a total of 708 isotope-labeled methyl groups, of which 612 contribute NOESY cross peaks. MethylFLYA confidently assigns 488 methyl groups, i.e. 80% of those with NOESY data. Of these, 459 agree with the reference, 6 were different, and 23 were without reference assignment. MethylFLYA assigns significantly more methyl groups than alternative algorithms, has an average error rate of 1%, modest runtimes of 0.4–1.2 h, and can handle arbitrary isotope labeling patterns and data from other types of NMR spectra. The structures and dynamics of large proteins can be studied with methyl-based NMR but peak assignment is still challenging. Here the authors present MethylFLYA that allows automated assignment of methyl groups and apply it to five proteins with molecular weights in the range from 28 to 358 kDa. [ABSTRACT FROM AUTHOR]

Folding of G-protein coupled receptors (GPCRs) according to the two-stage model (Popot, J. L., and Engelman, D. M. (1990) Biochemistry 29, 4031-4037) is postulated to proceed in 2 steps: partitioning of the polypeptide into the membrane followed by diffusion until native contacts are formed. Herein we investigate conformational preferences of fragments of the yeast Ste2p receptor using NMR. Constructs comprising the first, the first two, and the first three transmembrane (TM) segments, as well as a construct comprising TM1-TM2 covalently linked to TM7 were examined. We observed that the isolated TM1 does not form a stable helix nor does it integrate well into the micelle. TM1 is significantly stabilized upon interaction with TM2, forming a helical hairpin reported previously (Neumoin, A., Cohen, L. S., Arshava, B., Tantry, S., Becker, J. M., Zerbe, O., and Naider, F. (2009) Biophys. J. 96, 3187-3196), and in this case the protein integrates into the hydrophobic interior of the micelle. TM123 displays a strong tendency to oligomerize, but hydrogen exchange data reveal that the center of TM3 is solvent exposed. In all GPCRs so-far structurally characterized TM7 forms many contacts with TM1 and TM2. In our study TM127 integrates well into the hydrophobic environment, but TM7 does not stably pack against the remaining helices. Topology mapping in microsomal membranes also indicates that TM1 does not integrate in a membrane-spanning fashion, but that TM12, TM123, and TM127 adopt predominantly native-like topologies. The data from our study would be consistent with the retention of individual helices of incompletely synthesized GPCRs in the vicinity of the translocon until the complete receptor is released into the membrane interior. [ABSTRACT FROM AUTHOR]

Summary: As methods for analysis of biomolecular structure and dynamics using nuclear magnetic resonance spectroscopy (NMR) continue to advance, the resulting 3D structures, chemical shifts, and other NMR data are broadly impacting biology, chemistry, and medicine. Structure model assessment is a critical area of NMR methods development, and is an essential component of the process of making these structures accessible and useful to the wider scientific community. For these reasons, the Worldwide Protein Data Bank (wwPDB) has convened an NMR Validation Task Force (NMR-VTF) to work with wwPDB partners in developing metrics and policies for biomolecular NMR data harvesting, structure representation, and structure quality assessment. This paper summarizes the recommendations of the NMR-VTF, and lays the groundwork for future work in developing standards and metrics for biomolecular NMR structure quality assessment. [ABSTRACT FROM AUTHOR]

Summary: The protocols currently used for protein structure determination by nuclear magnetic resonance (NMR) depend on the determination of a large number of upper distance limits for proton-proton pairs. Typically, this task is performed manually by an experienced researcher rather than automatically by using a specific computer program. To assess whether it is indeed possible to generate in a fully automated manner NMR structures adequate for deposition in the Protein Data Bank, we gathered 10 experimental data sets with unassigned nuclear Overhauser effect spectroscopy (NOESY) peak lists for various proteins of unknown structure, computed structures for each of them using different, fully automatic programs, and compared the results to each other and to the manually solved reference structures that were not available at the time the data were provided. This constitutes a stringent “blind” assessment similar to the CASP and CAPRI initiatives. This study demonstrates the feasibility of routine, fully automated protein structure determination by NMR. [ABSTRACT FROM AUTHOR]