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Predicting 13Cα chemical shifts for validation of protein structures

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Abstract

The 13Cα chemical shifts for 16,299 residues from 213 conformations of four proteins (experimentally determined by X-ray crystallography and Nuclear Magnetic Resonance methods) were computed by using a combination of approaches that includes, but is not limited to, the use of density functional theory. Initially, a validation test of this methodology was carried out by a detailed examination of the correlation between computed and observed 13Cα chemical shifts of 10,564 (of the 16,299) residues from 139 conformations of the human protein ubiquitin. The results of this validation test on ubiquitin show agreement with conclusions derived from computation of the chemical shifts at the ab initio Hartree–Fock level. Further, application of this methodology to 5,735 residues from 74 conformations of the three remaining proteins that differ in their number of amino acid residues, sequence and three-dimensional structure, together with a new scoring function, namely the conformationally averaged root-mean-square-deviation, enables us to: (a) offer a criterion for an accurate assessment of the quality of NMR-derived protein conformations; (b) examine whether X-ray or NMR-solved structures are better representations of the observed 13Cα chemical shifts in solution; (c) provide evidence indicating that the proposed methodology is more accurate than automated predictors for validation of protein structures; (d) shed light as to whether the agreement between computed and observed 13Cα chemical shifts is influenced by the identity of an amino acid residue or its location in the sequence; and (e) provide evidence confirming the presence of dynamics for proteins in solution, and hence showing that an ensemble of conformations is a better representation of the structure in solution than any single conformation.

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Acknowledgments

We thank B.T. Amann for providing us with the reference used for the 13C chemical shifts of protein 1M9O, and Yelena Arnautova for helpful suggestions. This research was supported by grants from the National Institutes of Health (GM-14312, TW-6335, and GM-24893), and the National Science Foundation (MCB05-41633). Support was also received from the National Research Council of Argentina (CONICET), FONCyT-ANPCyT (PAE 22642 / 22672), and from the Universidad Nacional de San Luis [UNSL] (P-328501), Argentina. This research was conducted using the resources of: (1) two Beowulf-type clusters located at (a) the Instituto de Matemática Aplicada San Luis (CONICET-UNSL); and (b) the Baker Laboratory of Chemistry and Chemical Biology, Cornell University; and (2) the National Science Foundation Terascale Computing System at the Pittsburgh Supercomputer Center.

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  1. Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, NY, 14853-1301, USA

    Jorge A. Vila & Harold A. Scheraga

  2. Instituto de Matemática Aplicada San Luis, CONICET, Universidad Nacional de San Luis, Ejército de Los Andes, 950-5700, San Luis, Argentina

    Jorge A. Vila, Myriam E. Villegas & Hector A. Baldoni

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  1. Jorge A. Vila
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Correspondence to Harold A. Scheraga.

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Vila, J.A., Villegas, M.E., Baldoni, H.A. et al. Predicting 13Cα chemical shifts for validation of protein structures. J Biomol NMR 38, 221–235 (2007). https://doi.org/10.1007/s10858-007-9162-x

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