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Improved chemical shift prediction by Rosetta conformational sampling

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Abstract

Chemical shift frequencies represent a time-average of all the conformational states populated by a protein. Thus, chemical shift prediction programs based on sequence and database analysis yield higher accuracy for rigid rather than flexible protein segments. Here we show that the prediction accuracy can be significantly improved by averaging over an ensemble of structures, predicted solely from amino acid sequence with the Rosetta program. This approach to chemical shift and structure prediction has the potential to be useful for guiding resonance assignments, especially in solid-state NMR structural studies of membrane proteins in proteoliposomes.

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Acknowledgments

This research was supported by grants from the National Institutes of Health (R21GM094727; P01AI074805). It utilized the Biotechnology Research Center for NMR Molecular Imaging of Proteins at UCSD (P41EB002031).

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Authors and Affiliations

  1. Sanford Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, CA, 92037, USA

    Ye Tian & Francesca M. Marassi

  2. Department of Chemistry and Biochemistry, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0307, USA

    Ye Tian & Stanley J. Opella

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  1. Ye Tian
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  2. Stanley J. Opella
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  3. Francesca M. Marassi
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Correspondence to Francesca M. Marassi.

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Tian, Y., Opella, S.J. & Marassi, F.M. Improved chemical shift prediction by Rosetta conformational sampling. J Biomol NMR 54, 237–243 (2012). https://doi.org/10.1007/s10858-012-9677-7

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