NMR assignments of sparsely labeled proteins using a genetic algorithm

Gao, Qi; Chalmers, Gordon; Moremen, Kelley W.; Prestegard, James H.

doi:10.1007/s10858-017-0101-1

Cited by 8 publications

(21 citation statements)

References 51 publications

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“…This should be possible with the addition of additional data types. 15 N-filtered NOEs have proven very valuable in the assignment of HSQC crosspeaks in other applications (Gao et al 2017; Gao et al 2016). 13 C-filtered NOEs for methyl groups would have similar value.…”

Section: Resultsmentioning

confidence: 99%

“…Penalties were assigned based on the occurrence of assignments carrying a one or zero (~10 for ones and 0 for zeros) and added to an overall assignment score. As in the original description of the program (Gao et al 2017), score contributions for agreement of measured and predicted chemical shifts (calculated using the program PPM_ONE) (Li and Brüschweiler 2015) were represented as root-mean-square-deviations (RMSDs) normalized to 1 for deviations equal to estimated errors. Similarly, score contributions for RDCs came from RMSDs of measured versus predicted values, normalized and adjusted for information content.…”

Section: Experimental Methodsmentioning

confidence: 99%

“…However, interest is really in which sites can be assigned to a particular crosspeak with high confidence, as opposed to identifying a completely correct set of assignments. Therefore, we have devised a criterion for selecting these high confidence sites (Gao et al 2017). Based on test cases with known assignments, a site that is assigned to the same crosspeak more than 50% of the time in the list using the aforementioned cut-off, is an assignment made with high confidence (~95% confidence limit).…”

Section: Experimental Methodsmentioning

confidence: 99%

“…Recently we introduced a procedure for assigning 15 N-HSQC crosspeaks without the aid of triple resonance experiments (Gao et al 2017; Gao et al 2016). This relies on a set of experiments easily acquired on sparsely labeled proteins using HSQC-based experiments (chemical shifts, 1 H- 15 N RDCs, and 15 N-HSQC-edited NOESY vectors) and prediction of these parameters using known domain structures.…”

Section: Introductionmentioning

confidence: 99%

“…This relies on a set of experiments easily acquired on sparsely labeled proteins using HSQC-based experiments (chemical shifts, 1 H- 15 N RDCs, and 15 N-HSQC-edited NOESY vectors) and prediction of these parameters using known domain structures. The data and predictions were combined using an assignment program, ASSIGN_SLP_1.1.2 (Gao et al 2017), that uses a genetic algorithm available in MATLAB to search for the assignment giving the best match of prediction and experiment. The procedure was initially devised to allow assignment of glycosylated proteins, which are difficult to prepare in uniformly labeled forms for economic reasons, and it was tested on small and moderately sized non-perdeuterated proteins that were labeled with 15 N in selected amino acid types.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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A strategy for acquiring structural information from sparsely isotopically labeled large proteins is illustrated with an application to the E. coli heat-shock protein, HtpG (high temperature protein G), a 145 kDa dimer. It uses 13C-alanine methyl labeling in a perdeuterated background to take advantage of the sensitivity and resolution of Methyl-TROSY spectra, as well as the backbone-centered structural information from 1H-13C residual dipolar couplings (RDCs) of alanine methyl groups. In all, 40 of the 47 expected crosspeaks were resolved and 36 gave RDC data. Assignments of crosspeaks were partially achieved by transferring assignments from those made on individual domains using triple resonance methods. However, these were incomplete and in many cases the transfer was ambiguous. A genetic algorithm search for consistency between predictions based on domain structures and measurements for chemical shifts and RDCs allowed 60% of the 40 resolved crosspeaks to be assigned with confidence. Chemical shift changes of these crosspeaks on adding an ATP analog to the apo-protein are shown to be consistent with structural changes expected on comparing previous crystal structures for apo- and complex- structures. RDCs collected on the assigned alanine methyl peaks are used to generate a new solution model for the apo-protein structure.

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Section: Resultsmentioning

confidence: 99%

Section: Experimental Methodsmentioning

confidence: 99%

Section: Experimental Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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Measurement of residual dipolar couplings in methyl groups via carbon detection

et al. 2019

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Residual dipolar couplings (RDCs) provide both structural and dynamical information useful in the characterization of biological macromolecules. While most data come from the interaction of simple pairs of directly bonded spin-1/2 nuclei ( 1 H-15 N, 1 H-13 C, 1 H-1 H), it is possible to acquire data from interactions among the multiple spins of 13 C-labeled methyl groups ( 1 H 3 -13 C). This is especially important because of the advantages that observation of 13 C-labled methyl groups offers in working with very large molecules. Here we consider some of the options for measurement of methyl RDCs in large and often fully protonated proteins and arrive at a pulse sequence that exploits both J-modulation and direct detection of 13 C. Its utility is illustrated by application to a fully protonated two domain fragment from the mammalian glycoprotein, Robo1, 13 C-methyllabeled in all valines.

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Backbone and methyl assignment of bacteriorhodopsin incorporated into nanodiscs

et al. 2019

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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 tripleresonance 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 13 C 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, C α , C β , 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.

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NMR assignments of sparsely labeled proteins using a genetic algorithm

Cited by 8 publications

References 51 publications

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

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

Measurement of residual dipolar couplings in methyl groups via carbon detection

Backbone and methyl assignment of bacteriorhodopsin incorporated into nanodiscs

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