NMR characterization of HtpG, the E. coli Hsp90, using sparse labeling with 13C-methyl alanine

Pederson, Kari; Chalmers, Gordon; Gao, Qi; Elnatan, Daniel; Ramelot, Theresa; Liang, Ma; Montelione, Gaetano T.; Kennedy, Michael A.; Agard, David A.; Prestegard, James H.

doi:10.1007/s10858-017-0123-8

Cited by 16 publications

(14 citation statements)

References 58 publications

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“…Clearly, the larger the number of peaks in a spectrum, the more complicated this process is, in particular for methyl resonances, which show little dispersion. For larger systems, preparing several samples with only one type of amino acid labeled will reduce the overlap (Pederson et al 2017 ; Zhang and Ingen 2016 ; Miyanoiri et al 2016 ). Labeling a single methyl group per amino acid also helps (Tugarinov et al 2006 ).…”

Section: Discussionmentioning

confidence: 99%

Methyl group assignment using pseudocontact shifts with PARAssign

et al. 2017

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A new version of the program PARAssign has been evaluated for assignment of NMR resonances of the 76 methyl groups in leucines, isoleucines and valines in a 25 kDa protein, using only the structure of the protein and pseudocontact shifts (PCS) generated with a lanthanoid tag at up to three attachment sites. The number of reliable assignments depends strongly on two factors. The principle axes of the magnetic susceptibility tensors of the paramagnetic centers should not be parallel so as to avoid correlated PCS. Second, the fraction of resonances in the spectrum of a paramagnetic sample that can be paired with the diamagnetic counterparts is critical for the assignment. With the data from two tag positions a reliable assignment could be obtained for 60% of the methyl groups and for many of the remaining resonances the number of possible assignments is limited to two or three. With a single tag, reliable assignments can be obtained for methyl groups with large PCS near the tag. It is concluded that assignment of methyl group resonances by paramagnetic tagging can be particularly useful in combination with some additional data, such as from mutagenesis or NOE-based experiments. Approaches to yield the best assignment results with PCS generating tags are discussed.Electronic supplementary materialThe online version of this article (doi:10.1007/s10858-017-0136-3) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Methyl group assignment using pseudocontact shifts with PARAssign

et al. 2017

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“…The intensities (maximum amplitude) of the signals were extracted using CARA (computer aided resonance assignment) ( 30 ) on NMRPipe-processed experiments ( 31 ) and analyzed with Python scripts (fig. S8) following Pederson et al ( 29 ).…”

Section: Methodsmentioning

confidence: 99%

“…The samples were tested with methyl-TROSY experiments ( 19 , 28 ) (fig. S7) at 900 MHz and 1.1 GHz, and J couplings and J + RDC were determined using a J -modulated methyl-TROSY ( 29 ) experiment depicted in fig. S6 as matrices of 2048 × 128 complex data points with 96 transients per ( t 1 ) increment.…”

Section: Methodsmentioning

confidence: 99%

Dynamic tuning of FRET in a green fluorescent protein biosensor

et al. 2019

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Förster resonance energy transfer (FRET) between mutants of green fluorescent protein is widely used to monitor protein-protein interactions and as a readout mode in fluorescent biosensors. Despite the fundamental importance of distance and molecular angles of fluorophores to each other, structural details on fluorescent protein FRET have been missing. Here, we report the high-resolution x-ray structure of the fluorescent proteins mCerulean3 and cpVenus within the biosensor Twitch-2B, as they undergo FRET and characterize the dynamics of this biosensor with B02-dependent paramagnetic nuclear magnetic resonance at 900 MHz and 1.1 GHz. These structural data provide the unprecedented opportunity to calculate FRET from the x-ray structure and to compare it to experimental data in solution. We find that interdomain dynamics limits the FRET effect and show that a rigidification of the sensor further enhances FRET.

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“…The package was tested on a set of four small non-glycosylated proteins having known structures and crosspeak assignments, as well as a small glycoprotein [9]. It was subsequently applied to a larger non-glycosylated and perdeuterated protein, for which only the structure of isolated domains was known [10]. While the general approach showed success with smaller systems, it became clear that for larger systems, factors in addition to the technical aspects of associating predictions with experimental measurement would have to be considered.…”

Section: Introductionmentioning

confidence: 99%

NMR Resonance Assignment Methodology: Characterizing Large Sparsely Labeled Glycoproteins

Chalmers

Eletsky

Morris

et al. 2019

Journal of Molecular Biology

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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 ones labeled with a single amino acid type, or a limited subset of types, isotopically enriched with 15 N or 13 C, 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 36kDa luminal domain of the sialyltransferase, rST6Gal1, in which all phenylalanines are labeled with 15 N, 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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NMR characterization of HtpG, the E. coli Hsp90, using sparse labeling with 13C-methyl alanine

Cited by 16 publications

References 58 publications

Methyl group assignment using pseudocontact shifts with PARAssign

Methyl group assignment using pseudocontact shifts with PARAssign

Dynamic tuning of FRET in a green fluorescent protein biosensor

NMR Resonance Assignment Methodology: Characterizing Large Sparsely Labeled Glycoproteins

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