Consistent blind protein structure generation from NMR chemical shift data - PubMed (original) (raw)

. 2008 Mar 25;105(12):4685-90.

doi: 10.1073/pnas.0800256105. Epub 2008 Mar 7.

Oliver Lange, Frank Delaglio, Paolo Rossi, James M Aramini, Gaohua Liu, Alexander Eletsky, Yibing Wu, Kiran K Singarapu, Alexander Lemak, Alexandr Ignatchenko, Cheryl H Arrowsmith, Thomas Szyperski, Gaetano T Montelione, David Baker, Ad Bax

Affiliations

Consistent blind protein structure generation from NMR chemical shift data

Yang Shen et al. Proc Natl Acad Sci U S A. 2008.

Abstract

Protein NMR chemical shifts are highly sensitive to local structure. A robust protocol is described that exploits this relation for de novo protein structure generation, using as input experimental parameters the (13)C(alpha), (13)C(beta), (13)C', (15)N, (1)H(alpha) and (1)H(N) NMR chemical shifts. These shifts are generally available at the early stage of the traditional NMR structure determination process, before the collection and analysis of structural restraints. The chemical shift based structure determination protocol uses an empirically optimized procedure to select protein fragments from the Protein Data Bank, in conjunction with the standard ROSETTA Monte Carlo assembly and relaxation methods. Evaluation of 16 proteins, varying in size from 56 to 129 residues, yielded full-atom models that have 0.7-1.8 A root mean square deviations for the backbone atoms relative to the experimentally determined x-ray or NMR structures. The strategy also has been successfully applied in a blind manner to nine protein targets with molecular masses up to 15.4 kDa, whose conventional NMR structure determination was conducted in parallel by the Northeast Structural Genomics Consortium. This protocol potentially provides a new direction for high-throughput NMR structure determination.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.

Fig. 1.

Plots of normalized accuracy of database fragments selected for ubiquitin. For each ubiquitin segment, 200 fragment candidates of the same length were selected using either the standard ROSETTA procedure (filled triangles), or an MFR search of the 5665-protein structural database, assigned by the programs DC (filled circles) or SPARTA (filled diamonds). For all panels, coordinate rmsds (N, Cα, and C′) between query segment and selected fragments are normalized with respect to randomly selected fragments. (A and B) Average (A) and lowest (B) normalized rmsd of 200 selected fragments, as a function of fragment size, relative to the x-ray coordinates of the corresponding ubiquitin segment, averaged over all (overlapped) consecutive segments. (C and D) Average normalized rmsd of 200 nine-residue (C) and three-residue (D) fragments relative to the x-ray coordinates, as a function of position in the ubiquitin sequence. (E and F) Lowest normalized rmsd of any of these selected nine-residue (E) or three-residue (F) fragments.

Fig. 2.

Fig. 2.

Plots of ROSETTA all atom energy versus Cα rmsd relative to the experimental structures for four representative test proteins. (A–D) Standard ROSETTA all atom energy. (A′–D′) ROSETTA energy, rescored by using the experimental chemical shifts (Eq. 1). (A) Ubiquitin. (B) Calbindin. (C) HPr. (D) TM1112. For A′–D′, the model with the lowest energy, marked by an arrow, is shown in Fig. 3 or

SI Fig. 9

.

Fig. 3.

Fig. 3.

Backbone ribbon representations (32) of the lowest-energy CS-ROSETTA structure (red) superimposed on the experimental x-ray/NMR structures (blue), with superposition optimized for ordered residues, as defined in the footnote to

SI Table 3

. (A) GB3. (B) CspA. (C) Calbindin. (D) Ubiquitin. (E) DinI. (F) Apo_lafbp. Overlays of the 10 remaining structures are shown in

SI Fig. 9

.

Fig. 4.

Fig. 4.

Results from blind CS-ROSETTA structure generation for four structural genomics targets (Table 2). The remaining five are in

SI Fig. 12

. (A–D) Superposition of lowest-energy CS-ROSETTA models (red) with experimental NMR structures (blue), with superposition optimized for ordered residues, as defined in the footnote to

SI Table 5

. (E–H) Plots of rescored (Eq. 1) ROSETTA all-atom energy versus Cα rmsd relative to the lowest-energy model (bold dot on vertical axis). (A and E) StR82. (B and F) RpT7. (C and G) VfR117. (D and H) NeT4.

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References

    1. Atreya HS, Szyperski T. Rapid NMR data collection. Methods Enzymol. 2005;394:78–108. - PubMed
    1. Freeman R, Kupce E. New methods for fast multidimensional NMR. J Biomol NMR. 2003;27:101–113. - PubMed
    1. Wagner G, Pardi A, Wüthrich K. Hydrogen-bond length and H-1-NMR chemical-shifts in proteins. J Am Chem Soc. 1983;105:5948–5949.
    1. Williamson MP, Asakura T. Empirical comparisons of models for chemical-shift calculation in proteins. J Magn Reson B. 1993;101:63–71.
    1. Case DA. Calibration of ring-current effects in proteins and nucleic acids. J Biomol NMR. 1995;6:341–346. - PubMed

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