Simultaneous Assignment and Structure Determination of Proteins From Sparsely Labeled NMR Datasets.
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ABSTRACT:
SUBMITTER: Mondal A
PROVIDER: S-EPMC8667806 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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.
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Project description:Not all proteins are amenable to uniform isotopic labeling with 13C and 15N, something needed for the widely used, and largely deductive, triple resonance assignment process. Among them are proteins expressed in mammalian cell culture where native glycosylation can be maintained, and proper formation of disulfide bonds facilitated. Uniform labeling in mammalian cells is prohibitively expensive, but sparse labeling with one or a few isotopically enriched amino acid types is an option for these proteins. However, assignment then relies on accessing the best match between a variety of measured NMR parameters and predictions based on 3D structure, often from X-ray crystallography. Finding this match is a challenging process that has benefitted from many computational tools, including trained neural nets for chemical shift prediction, genetic algorithms for searches through a myriad of assignment possibilities, and now AI-based prediction of high-quality structures for protein targets. AssignSLP_GUI, a new version of a software package for assignment of resonances from sparsely-labeled proteins, uses many of these tools. These tools and new additions to the package are highlighted in an application to a sparsely-labeled domain from a glycoprotein, CEACAM1.
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Project description:Sparse isotopic labeling of proteins for NMR studies using single types of amino acid (15N or 13C enriched) has several advantages. Resolution is enhanced by reducing numbers of resonances for large proteins, and isotopic labeling becomes economically feasible for glycoproteins that must be expressed in mammalian cells. However, without access to the traditional triple resonance strategies that require uniform isotopic labeling, NMR assignment of crosspeaks in heteronuclear single quantum coherence (HSQC) spectra is challenging. We present an alternative strategy which combines readily accessible NMR data with known protein domain structures. Based on the structures, chemical shifts are predicted, NOE cross-peak lists are generated, and residual dipolar couplings (RDCs) are calculated for each labeled site. Simulated data are then compared to measured values for a trial set of assignments and scored. A genetic algorithm uses the scores to search for an optimal pairing of HSQC crosspeaks with labeled sites. While none of the individual data types can give a definitive assignment for a particular site, their combination can in most cases. Four test proteins previously assigned using triple resonance methods and a sparsely labeled glycosylated protein, Robo1, previously assigned by manual analysis, are used to validate the method and develop a criterion for identifying sites assigned with high confidence.
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Project description:A solid-state NMR approach for simultaneous resonance assignment and three-dimensional structure determination of a membrane protein in lipid bilayers is described. The approach is based on the scattering, hence the descriptor "shotgun," of (15)N-labeled amino acids throughout the protein sequence (and the resulting NMR spectra). The samples are obtained by protein expression in bacteria grown on media in which one type of amino acid is labeled and the others are not. Shotgun NMR short-circuits the laborious and time-consuming process of obtaining complete sequential assignments prior to the calculation of a protein structure from the NMR data by taking advantage of the orientational information inherent to the spectra of aligned proteins. As a result, it is possible to simultaneously assign resonances and measure orientational restraints for structure determination. A total of five two-dimensional (1)H/(15)N PISEMA (polarization inversion spin exchange at the magic angle) spectra, from one uniformly and four selectively (15)N-labeled samples, were sufficient to determine the structure of the membrane-bound form of the 50-residue major pVIII coat protein of fd filamentous bacteriophage. Pisa (polarity index slat angle) wheels are an essential element in the process, which starts with the simultaneous assignment of resonances and the assembly of isolated polypeptide segments, and culminates in the complete three-dimensional structure of the protein with atomic resolution. The principles are also applicable to weakly aligned proteins studied by solution NMR spectroscopy. [The structure we determined for the membrane-bound form of the Fd bacteriophage pVIII coat protein has been deposited in the Protein Data Bank as PDB file 1MZT.]
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Project description:Protein NMR assignments of large proteins using traditional triple resonance techniques depends on double or triple labeling of samples with (15)N, (13)C, and (2)H. This is not always practical with proteins that require expression in nonbacterial hosts. Labeling with isotopically labeled versions of single amino acids (sparse labeling) often is possible; however, resonance assignment then requires a new strategy. Here a procedure for the assignment of cross-peaks in (15)N-(1)H correlation spectra of sparsely labeled proteins is presented. It relies on the correlation of proton-deuterium amide exchange rates in native and denatured spectra of the intact protein, followed by correlation of chemical shifts in the spectra of the denatured protein with chemical shifts of sequenced peptides derived from the protein. The procedure is successfully demonstrated on a sample of a protein, Galectin-3, selectively labeled with (15)N at all alanine residues.
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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.
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Project description:We report acquisition of diagonal-compensated protein structural restraints from four-dimensional solid-state NMR spectra on extensively deuterated and (1)H back-exchanged proteins. To achieve this, we use homonuclear (1)H-(1)H correlations with diagonal suppression and nonuniform sampling (NUS). Suppression of the diagonal allows the accurate identification of cross-peaks which are otherwise obscured by the strong autocorrelation or whose intensity is biased due to partial overlap with the diagonal. The approach results in unambiguous spectral interpretation and relatively few but reliable restraints for structure calculation. In addition, the diagonal suppression produces a spectrum with low dynamic range for which ultrasparse NUS data sets can be readily reconstructed, allowing straightforward application of NUS with only 2% sampling density with the advantage of more heavily sampling time-domain regions of high signal intensity. The method is demonstrated here for two proteins, α-spectrin SH3 microcrystals and hydrophobin functional amyloids. For the case of SH3, suppression of the diagonal results in facilitated identification of unambiguous restraints and improvement of the quality of the calculated structural ensemble compared to nondiagonal-suppressed 4D spectra. For the only partly assigned hydrophobin rodlets, the structure is yet unknown. Applied to this protein of biological significance with large inhomogeneous broadening, the method allows identification of unambiguous crosspeaks that are otherwise obscured by the diagonal.
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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.
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Project description:AssignFit is a computer program developed within the XPLOR-NIH package for the assignment of dipolar coupling (DC) and chemical shift anisotropy (CSA) restraints derived from the solid-state NMR spectra of protein samples with uniaxial order. The method is based on minimizing the difference between experimentally observed solid-state NMR spectra and the frequencies back calculated from a structural model. Starting with a structural model and a set of DC and CSA restraints grouped only by amino acid type, as would be obtained by selective isotopic labeling, AssignFit generates all of the possible assignment permutations and calculates the corresponding atomic coordinates oriented in the alignment frame, together with the associated set of NMR frequencies, which are then compared with the experimental data for best fit. Incorporation of AssignFit in a simulated annealing refinement cycle provides an approach for simultaneous assignment and structure refinement (SASR) of proteins from solid-state NMR orientation restraints. The methods are demonstrated with data from two integral membrane proteins, one ?-helical and one ?-barrel, embedded in phospholipid bilayer membranes.
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