Predicting 13Cα chemical shifts for validation of protein structures
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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.
Keywords13C chemical shift prediction Solution structure Protein structure validation X-ray and NMR structures Ubiquitin
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.
- Biological Magnetic Resonance Data Bank (http://www.bmrb.wisc.edu)Google Scholar
- Case DA, Dyson HJ, Wright PE (1994) Use of chemical shifts and coupling constant in nuclear magnetic resonance structural studies on peptides and proteins. Methods Enzymol 239:392–416Google Scholar
- Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Zakrzewski VG, Montgomery JA, Stratmann RE Jr, Burant JC, Dapprich S, Millam JM, Daniels AD, Kudin KN, Strain MC, Farkas O, Tomasi J, Barone V, Cossi M, Cammi R, Mennucci B, Pomelli C, Adamo C, Clifford S, Ochterski J, Petersson GA, Ayala PY, Cui Q, Morokuma K, Malick DK, Rabuck AD, Raghavachari K, Foresman JB, Cioslowski J, Ortiz JV, Baboul AG, Stefanov BB, Liu G, Liashenko A, Piskorz P, Komaromi I, Gomperts R, Martin RL, Fox DJ, Keith T, Al-Laham MA, Peng CY, Nanayakkara A, Gonzalez C, Challacombe M, Gill PMW, Johnson B, Chen W, Wong MW, Andres JL, Gonzalez C, Head-Gordon M, Replogle ES, Pople JA (1998) Gaussian 98. Revision A.7, Inc., Pittsburgh, PAGoogle Scholar
- Havlin RH, Le H, Laws DD, de Dios AC, Oldfield E (1997) An ab initio quantum chemical investigation of carbon-13 NMR shielding tensors in glycine, alanine, valine, isoleucine, serine, and threonine: comparisons between helical and sheet tensors, and effects of χ1 on shielding. J Am Chem Soc 119:11951–11958CrossRefGoogle Scholar
- Hehre WJ, Radom L, Schleyer P, Pople JA (1986) Ab initio molecular orbital theory. Wiley, New YorkGoogle Scholar
- Luginbühl P, Szyperski T, Wüthrich KJ (1995) Statistical basis for the use of 13Cα chemical shifts in protein structure determination. Magn Resn B 109:220–233Google Scholar
- Némethy G, Gibson KD, Palmer KA, Yoon CN, Paterlini G, Zagari A, Rumsey S, Scheraga HA (1992) Energy parameters in polypeptides. 10. Improved geometrical parameters and nonbonded interactions for use in the ECEPP/3 algorithm, with application to proline-containing peptides. J Phys Chem 96:6472–6484CrossRefGoogle Scholar
- Press HW, Teukolsky SA, Vetterling WT, Flannery BP (1992) In: Numerical recipes in Fortran 77. The art of scientific computing, 2nd edn. Cambridge University Press, Ch. 14, pp 630–633Google Scholar
- van Nuland NAJ, Hangyi IW, van Schaik RC, Berendsen HJC, van Gunsteren WF, Scheek RM, Robillard GT (1994) The high-resolution structure of the histidine-containing phosphocarrier protein HPr from Escherichia coli determined by restrained molecular dynamics from nuclear magnetic resonance nuclear Overhauser effect data. J Mol Biol 237:544–559CrossRefGoogle Scholar
- Vila JA, Ripoll DR, Scheraga HA (2007) Use of 13Cα chemical shifts in protein structure determination. J Phys Chem B (in press)Google Scholar
- Wilson KS, Dauter Z, Lamzin VS, Walsh M, Wodak S, Richelle J, Pontius J, Vaguine A, Laskowski JM, MacArthur MW, Dodson E, Murshudov G, Oldfield TJ, Kaptein R, Rullmann JAC (1998) Who checks the checkers? Four validation tools applied to eight atomic resolution structures. J Mol Biol 276:417–436CrossRefGoogle Scholar