Browse
Submit Data
Databases
API
Help

Dataset Information

23 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

NMR resonance assignment and dynamics of profilin from Heimdallarchaeota.

ABSTRACT: The origin of the eukaryotic cell is an unsettled scientific question. The Asgard superphylum has emerged as a compelling target for studying eukaryogenesis due to the previously unseen diversity of eukaryotic signature proteins. However, our knowledge about these proteins is still relegated to metagenomic data and very little is known about their structural properties. Additionally, it is still unclear if these proteins are functionally homologous to their eukaryotic counterparts. Here, we expressed, purified and structurally characterized profilin from Heimdallarchaeota in the Asgard superphylum. The structural analysis shows that while this profilin possesses similar secondary structural elements as eukaryotic profilin, it contains additional secondary structural elements that could be critical for its function and an indication of divergent evolution.

SUBMITTER: Haq SR

PROVIDER: S-EPMC7522288 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

NMR resonance assignment and dynamics of profilin from Heimdallarchaeota.

Haq Syed Razaul SR Survery Sabeen S Hurtig Fredrik F Lindås Ann-Christin AC Chi Celestine N CN

Scientific reports 20200928 1

The origin of the eukaryotic cell is an unsettled scientific question. The Asgard superphylum has emerged as a compelling target for studying eukaryogenesis due to the previously unseen diversity of eukaryotic signature proteins. However, our knowledge about these proteins is still relegated to metagenomic data and very little is known about their structural properties. Additionally, it is still unclear if these proteins are functionally homologous to their eukaryotic counterparts. Here, we expr ...[more]

PMID: 32985518

Similar Datasets

Contact replacement for NMR resonance assignment.

Project description:MOTIVATION: Complementing its traditional role in structural studies of proteins, nuclear magnetic resonance (NMR) spectroscopy is playing an increasingly important role in functional studies. NMR dynamics experiments characterize motions involved in target recognition, ligand binding, etc., while NMR chemical shift perturbation experiments identify and localize protein-protein and protein-ligand interactions. The key bottleneck in these studies is to determine the backbone resonance assignment, which allows spectral peaks to be mapped to specific atoms. This article develops a novel approach to address that bottleneck, exploiting an available X-ray structure or homology model to assign the entire backbone from a set of relatively fast and cheap NMR experiments. RESULTS: We formulate contact replacement for resonance assignment as the problem of computing correspondences between a contact graph representing the structure and an NMR graph representing the data; the NMR graph is a significantly corrupted, ambiguous version of the contact graph. We first show that by combining connectivity and amino acid type information, and exploiting the random structure of the noise, one can provably determine unique correspondences in polynomial time with high probability, even in the presence of significant noise (a constant number of noisy edges per vertex). We then detail an efficient randomized algorithm and show that, over a variety of experimental and synthetic datasets, it is robust to typical levels of structural variation (1-2 AA), noise (250-600%) and missings (10-40%). Our algorithm achieves very good overall assignment accuracy, above 80% in alpha-helices, 70% in beta-sheets and 60% in loop regions. AVAILABILITY: Our contact replacement algorithm is implemented in platform-independent Python code. The software can be freely obtained for academic use by request from the authors.

| S-EPMC2718645 | biostudies-literature

Exploiting image registration for automated resonance assignment in NMR.

Project description:Analysis of protein NMR data involves the assignment of resonance peaks in a number of multidimensional data sets. To establish resonance assignment a three-dimensional search is used to match a pair of common variables, such as chemical shifts of the same spin system, in different NMR spectra. We show that by displaying the variables to be compared in two-dimensional plots the process can be simplified. Moreover, by utilizing a fast Fourier transform cross-correlation algorithm, more common to the field of image registration or pattern matching, we can automate this process. Here, we use sequential NMR backbone assignment as an example to show that the combination of correlation plots and segmented pattern matching establishes fast backbone assignment in fifteen proteins of varying sizes. For example, the 265-residue RalBP1 protein was 95.4% correctly assigned in 10 s. The same concept can be applied to any multidimensional NMR data set where analysis comprises the comparison of two variables. This modular and robust approach offers high efficiency with excellent computational scalability and could be easily incorporated into existing assignment software.

| S-EPMC4452424 | biostudies-literature

Solution NMR resonance assignment strategies for ?-barrel membrane proteins.

Project description:Membrane proteins in detergent micelles are large and dynamic complexes that present challenges for solution NMR investigations such as spectral overlap and line broadening. In this study, multiple methods are introduced to facilitate resonance assignment of ?-barrel membrane proteins using Opa60 from Neisseria gonorrhoeae as a model system. Opa60 is an eight-stranded ?-barrel with long extracellular loops (?63% of the protein) that engage host receptors and induce engulfment of the bacterium. The NMR spectra of Opa60 in detergent micelles exhibits significant spectral overlap and resonances corresponding to the loop regions had variable line widths, which interfered with a complete assignment of the protein. To assign the ?-barrel residues, trypsin cleavage was used to remove much of the extracellular loops while preserving the detergent solubilized ?-barrel. The removal of the loop resonances significantly improved the assignment of the Opa60 ?-barrel region (97% of the resonances corresponding to the ?-barrel and periplasmic turns were assigned). For the loop resonance assignments, two strategies were implemented; modulating temperature and synthetic peptides. Lowering the temperature broadened many peaks beyond detection and simplified the spectra to only the most dynamic regions of the loops facilitating 27 loop resonances to be assigned. To further assign functionally important and unstructured regions of the extracellular loops, a synthetic 20 amino acid peptide was synthesized and had nearly complete spectral overlap with the full-length protein allowing 17 loop resonances to be assigned. Collectively, these strategies are effective tools that may accelerate solution NMR structure determination of ?-barrel membrane proteins.

| S-EPMC3832050 | biostudies-literature

NMR Resonance Assignment Methodology: Characterizing Large Sparsely Labeled Glycoproteins.

Project description:Characterization of proteins using NMR methods begins with assignment of resonances to specific residues. This is usually accomplished using sequential connectivities between nuclear pairs in proteins uniformly labeled with NMR active isotopes. This becomes impractical for larger proteins, and especially for proteins that are best expressed in mammalian cells, including glycoproteins. Here an alternate protocol for the assignment of NMR resonances of sparsely labeled proteins, namely, the ones labeled with a single amino acid type, or a limited subset of types, isotopically enriched with 15N or 13C, is described. The protocol is based on comparison of data collected using extensions of simple two-dimensional NMR experiments (correlated chemical shifts, nuclear Overhauser effects, residual dipolar couplings) to predictions from molecular dynamics trajectories that begin with known protein structures. Optimal pairing of predicted and experimental values is facilitated by a software package that employs a genetic algorithm, ASSIGN_SLP_MD. The approach is applied to the 36-kDa luminal domain of the sialyltransferase, rST6Gal1, in which all phenylalanines are labeled with 15N, and the results are validated by elimination of resonances via single-point mutations of selected phenylalanines to tyrosines. Assignment allows the use of previously published paramagnetic relaxation enhancements to evaluate placement of a substrate analog in the active site of this protein. The protocol will open the way to structural characterization of the many glycosylated and other proteins that are best expressed in mammalian cells.

| S-EPMC6554063 | biostudies-literature

5D solid-state NMR spectroscopy for facilitated resonance assignment.

Project description:1H-detected solid-state NMR spectroscopy has been becoming increasingly popular for the characterization of protein structure, dynamics, and function. Recently, we showed that higher-dimensionality solid-state NMR spectroscopy can aid resonance assignments in large micro-crystalline protein targets to combat ambiguity (Klein et al., Proc. Natl. Acad. Sci. U.S.A. 2022). However, assignments represent both, a time-limiting factor and one of the major practical disadvantages within solid-state NMR studies compared to other structural-biology techniques from a very general perspective. Here, we show that 5D solid-state NMR spectroscopy is not only justified for high-molecular-weight targets but will also be a realistic and practicable method to streamline resonance assignment in small to medium-sized protein targets, which such methodology might not have been expected to be of advantage for. Using a combination of non-uniform sampling and the signal separating algorithm for spectral reconstruction on a deuterated and proton back-exchanged micro-crystalline protein at fast magic-angle spinning, direct amide-to-amide correlations in five dimensions are obtained with competitive sensitivity compatible with common hardware and measurement time commitments. The self-sufficient backbone walks enable efficient assignment with very high confidence and can be combined with higher-dimensionality sidechain-to-backbone correlations from protonated preparations into minimal sets of experiments to be acquired for simultaneous backbone and sidechain assignment. The strategies present themselves as potent alternatives for efficient assignment compared to the traditional assignment approaches in 3D, avoiding user misassignments derived from ambiguity or loss of overview and facilitating automation. This will ease future access to NMR-based characterization for the typical solid-state NMR targets at fast MAS.

| S-EPMC10687145 | biostudies-literature

Automatic structure-based NMR methyl resonance assignment in large proteins.

Project description: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.

| S-EPMC6820720 | biostudies-literature

Reduced-dimensionality NMR spectroscopy for high-throughput protein resonance assignment.

Project description:A suite of reduced-dimensionality (13)C,(15)N,(1)H-triple-resonance NMR experiments is presented for rapid and complete protein resonance assignment. Even when using short measurement times, these experiments allow one to retain the high spectral resolution required for efficient automated analysis. "Sampling limited" and "sensitivity limited" data collection regimes are defined, respectively, depending on whether the sampling of the indirect dimensions or the sensitivity of a multidimensional NMR experiments per se determines the minimally required measurement time. We show that reduced-dimensionality NMR spectroscopy is a powerful approach to avoid the "sampling limited regime"--i.e., a standard set of ten experiments proposed here allows one to effectively adapt minimal measurement times to sensitivity requirements. This is of particular interest in view of the greatly increased sensitivity of NMR spectrometers equipped with cryogenic probes. As a step toward fully automated analysis, the program AUTOASSIGN has been extended to provide sequential backbone and (13)C(beta) resonance assignments from these reduced-dimensionality NMR data.

| S-EPMC123011 | biostudies-literature

G-matrix Fourier transform NMR spectroscopy for complete protein resonance assignment.

Project description:A G-matrix Fourier transform (GFT) NMR spectroscopy-based strategy for resonance assignment of proteins is described. Each of the GFT NMR experiments presented here rapidly affords four-, five-, or six-dimensional spectral information in combination with precise measurements of chemical shifts. The resulting high information content enables one to obtain nearly complete assignments by using only four NMR experiments. For the backbone amide proton detected "out-and-back" experiments, data collection was further accelerated up to approximately 2.5-fold by use of longitudinal (1)H relaxation optimization. The GFT NMR experiments were acquired for three proteins with molecular masses ranging from 8.6 to 17 kDa, demonstrating that the proposed strategy is of key interest for automated resonance assignment in structural genomics.

| S-EPMC470728 | biostudies-literature

A novel strategy for NMR resonance assignment and protein structure determination.

Project description:The quality of protein structures determined by nuclear magnetic resonance (NMR) spectroscopy is contingent on the number and quality of experimentally-derived resonance assignments, distance and angular restraints. Two key features of protein NMR data have posed challenges for the routine and automated structure determination of small to medium sized proteins; (1) spectral resolution - especially of crowded nuclear Overhauser effect spectroscopy (NOESY) spectra, and (2) the reliance on a continuous network of weak scalar couplings as part of most common assignment protocols. In order to facilitate NMR structure determination, we developed a semi-automated strategy that utilizes non-uniform sampling (NUS) and multidimensional decomposition (MDD) for optimal data collection and processing of selected, high resolution multidimensional NMR experiments, combined it with an ABACUS protocol for sequential and side chain resonance assignments, and streamlined this procedure to execute structure and refinement calculations in CYANA and CNS, respectively. Two graphical user interfaces (GUIs) were developed to facilitate efficient analysis and compilation of the data and to guide automated structure determination. This integrated method was implemented and refined on over 30 high quality structures of proteins ranging from 5.5 to 16.5 kDa in size.

| S-EPMC3715383 | biostudies-literature

Robust structure-based resonance assignment for functional protein studies by NMR.

Project description:High-throughput functional protein NMR studies, like protein interactions or dynamics, require an automated approach for the assignment of the protein backbone. With the availability of a growing number of protein 3D structures, a new class of automated approaches, called structure-based assignment, has been developed quite recently. Structure-based approaches use primarily NMR input data that are not based on J-coupling and for which connections between residues are not limited by through bonds magnetization transfer efficiency. We present here a robust structure-based assignment approach using mainly H(N)-H(N) NOEs networks, as well as (1)H-(15) N residual dipolar couplings and chemical shifts. The NOEnet complete search algorithm is robust against assignment errors, even for sparse input data. Instead of a unique and partly erroneous assignment solution, an optimal assignment ensemble with an accuracy equal or near to 100% is given by NOEnet. We show that even low precision assignment ensembles give enough information for functional studies, like modeling of protein-complexes. Finally, the combination of NOEnet with a low number of ambiguous J-coupling sequential connectivities yields a high precision assignment ensemble. NOEnet will be available under: http://www.icsn.cnrs-gif.fr/download/nmr.

| S-EPMC2813526 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data