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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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References

  • Allerhand A, Childers RF, Oldfield E (1973) Natural-abundance carbon-13 nuclear magnetic resonance studies in 20-mm sample tubes. Observation of numerous single-carbon resonances of Hen Egg-White Lysozyme. Biochem 12:1335–1241

    Article  Google Scholar 

  • Amann BT, Worthington MT, Berg JMA (2003) A Cys3His zinc-binding domain from Nup475/Tristetraprolin: a novel fold with a disklike structure. Biochem 42:217–221

    Article  Google Scholar 

  • Babini E, Bertini I, Capozzi F, Del Bianco C, Hollender D, Kiss T, Luchinat C, Quattrone A (2004) Solution structure of human β-parvalbumin and structural comparison with its paralog α-parvalbumin and with their rat orthologs. Biochem 43:16076–16085

    Article  Google Scholar 

  • Ban Y-E, Rudolph J, Zhou P, Edelsbrunner H (2006) Evaluating the quality of NMR structures by local density of protons. Proteins 62:852–864

    Article  Google Scholar 

  • Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The Protein Data Bank. Nucleic Acids Res 28:235–242

    Article  Google Scholar 

  • Biological Magnetic Resonance Data Bank (http://www.bmrb.wisc.edu)

  • Case DA (2000) Interpretation of chemical shifts and coupling constants in macromolecules. Curr Opin Struct Biol 10:197–203

    Article  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 239:392–416

    Google Scholar 

  • Celda B, Biamonti C, Arnau MJ, Tejero R, Montelione GT (1995) Combined use of 13C chemical shift and 1Hα–13Cα heteronuclear NOE data in monitoring a protein NMR structure refinement. J Biomol NMR 5:161–172

    Article  Google Scholar 

  • Chakrabarti P, Pal D (1998) Main-chain conformational features at different conformations of the side-chains in proteins. Protein Eng 11:631–647

    Article  Google Scholar 

  • Chesnut DB, Moore KD (1989) Locally dense basis sets for chemical shift calculations. J Comp Chem 10:648–659

    Article  Google Scholar 

  • Cornilescu G, Marquardt JL, Ottiger M, Bax A (1998) Validation of protein structure from anisotropic carbonyl chemical shifts in a diluite liquid crystalline phase. J Am Chem Soc 120:6836–6837

    Article  Google Scholar 

  • Cornilescu G, Delaglio F, Bax A (1999) Protein backbone angle restraints from searching a database for chemical shift and sequence homology. J Biomol NMR 13:289–302

    Article  Google Scholar 

  • de Dios AC, Pearson JG, Oldfield E (1993a) Chemical shifts in proteins: ab initio study of carbon-13 nuclear magnetic resonance chemical shielding in glycine, alanine and valine residues. J Am Chem Soc 115:9768–9773

    Article  Google Scholar 

  • de Dios AC, Pearson JG, Oldfield E (1993b) Secondary and tertiary structural effects on protein NMR chemical shifts: an ab initio approach. Science 260:1491–1496

    Article  ADS  Google Scholar 

  • Doreleijers JF, Rullmann JAC, Kaptein R (1998) Quality assessment of NMR structures: a statistical survey. J Mol Biol 281:149–164

    Article  Google Scholar 

  • Dunbrack RL Jr, Karplus M (1994) Conformational analysis of the backbone-dependent rotamer preferences of protein sidechains. Nat Struct Biol 1:334–340

    Article  Google Scholar 

  • Dyson HJ, Wright PE (2005) Elucidation of the protein folding landscape by NMR. Methods Enzymol 394:299–321

    Article  Google 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, PA

  • 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 119:11951–11958

    Article  Google Scholar 

  • Hehre WJ, Radom L, Schleyer P, Pople JA (1986) Ab initio molecular orbital theory. Wiley, New York

    Google Scholar 

  • Howard OW, Lilley DMJ (1978) Carbon-13-NMR of peptides and proteins. Prog Nucl Magn Reson Spectrosc 12:1–40

    Article  Google Scholar 

  • Hunter C, Packer MJ, Zonta C (2005) From structure to chemical shift and vice-versa. Prog Nucl Magn Reson Spectrosc 47:27–39

    Article  Google Scholar 

  • Iwadate M, Asakura T, Williamson MP (1999) 13Cα and 13Cβ carbon-13 chemical shifs in protein from an empirical database. J Biomol NMR 13:199–211

    Article  Google Scholar 

  • Jameson CJ (1996) Understanding NMR chemical shifts. Annu Rev Phys Chem 47:135–169

    Article  Google Scholar 

  • Kuszewski J, Qin JA, Gronenborn AM, Clore GM (1995) The impact on direct refinement against 13Cα and 13Cβ chemical shifts on protein structure determination by NMR. J Magn Reson Ser B 106:92–96

    Article  Google Scholar 

  • Laskowski RA, MacArthur MW, Moss DS, Thornton JM (1993) PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Cryst 26:283–291

    Article  Google Scholar 

  • Laskowski RA, Rullmann JAC, MacArthur MW, Kaptein R, Thornton J (1996) AQUA and PROCHECK-NMR: programs for checking the quality of protein structures solved by NMR. J Biomol NMR 8:477–486

    Article  Google Scholar 

  • Laws DD, Le H, de Dios AC, Havlin RH, Oldfield E (1995) A basis size dependence study of Carbon-13 nuclear magnetic resonance spectroscopic shielding in Alanyl and Valyl fragments: toward protein shielding hypersurfaces. J Am Chem Soc 117:9542–9546

    Article  Google Scholar 

  • Lindorff-Larsen K, Best RB, Depristo MA, Dobson CM, Vendruscolo M (2005) Simultaneous determination of protein structure and dynamics. Nature 433:128–132

    Article  ADS  Google Scholar 

  • Luginbühl P, Szyperski T, Wüthrich KJ (1995) Statistical basis for the use of 13Cα chemical shifts in protein structure determination. Magn Resn B 109:220–233

    Google Scholar 

  • Malthouse JPG (1985) 13C NMR of enzymes. Prog Nucl Magn Reson Spectrosc 18:1–59

    Article  Google Scholar 

  • Meiler JJ (2003) PROSHIFT: protein chemical shift prediction using artificial neural networks. J Biomol NMR 26:25–37

    Article  Google Scholar 

  • Melnik BS, Garbuzynskiy SO, Lobanov MYu, Galzitskaya OV (2005) The difference between protein structures obtained by X-ray analysis and nuclear magnetic resonance. J Mol Biol 39:113–122

    Article  Google Scholar 

  • Moon S, Case DA (2006) A comparison of quantum chemical models for calculating NMR shielding parameters in peptides: mixed basis set and ONION methods combined with a complete basis set extrapolation. J Comp Chem 27:825–836

    Article  Google Scholar 

  • Morris AL, MacArthur MW, Hutchinson EG, Thornton JM (1992) Stereochemical quality of protein structure coordinates. Proteins 12:345–364

    Article  Google Scholar 

  • Nabuurs SB, Nederveen AJ, Vranken W, Doreleijers JF, Bonvin AMJJ, Vuister GW, Vriend G, Spronk CAEM (2004) DRESS: a database of Refined solution NMR structures. Proteins 55:483–486

    Article  Google Scholar 

  • Napper S, Delbaere LTJ, Waygood BEJ (1999) Histidine-containing protein, HPr, of the Escherichia coli phosphoenolpyruvate:sugar phosphotransferase system can accept and donate a phosphoryl group. J Biol Chem 274:21776–21782

    Article  Google Scholar 

  • Neal S, Nip AM, Zhang H, Wishart DS (2003) Rapid and accurate calculation of protein 1H, 13C and 15N chemical shifts. J Biomol NMR 26:215–240

    Article  Google 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 96:6472–6484

    Article  Google Scholar 

  • Oldfield E (2002) Chemical shifts in amino acids, peptides and proteins: from quantum chemistry to drug design. Annu Rev Phys Chem 53:349–378

    Article  Google Scholar 

  • Oldfield E, Allerhand A (1975) Identification of tryptophan resonances in natural abundance C-13 nuclear magnetic-resonance spectra of protein. Application of partially relaxed fourier-transform spectroscopy. J Am Chem Soc 97:221–224

    Article  Google Scholar 

  • Pearson JG, Le H, Sanders LK, Godbout N, Havlin RH, Oldfield EJ (1997) Predicting chemical shifts in proteins: structure refinement of valine residues by using ab initio and empirical geometry optimizations. J Am Chem Soc 119:11941–11950

    Article  Google Scholar 

  • Pearson JG, Wang J-F, Markley JL, Le H, Oldfield E (1995) Protein structure refinement using carbon-13 nuclear magnetic resonance spectroscopic chemical shifts and quantum chemistry. J Am Chem Soc 117:8823–8829

    Article  Google Scholar 

  • Pontius J, Richelle J, Wodak SJ (1996) Deviations from standard atomic volumes as a quality measure for protein crystal structures. J Mol Biol 264:121–136

    Article  Google 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 630–633

  • Ripoll DR, Vorobjev YN, Liwo A, Vila JA, Scheraga HA (1996) Coupling between folding and ionization equilibria: effects of pH on the conformational preferences of polypeptides. J Mol Biol 264:770–783

    Article  Google Scholar 

  • Ripoll DR, Vila JA, Scheraga HA (2005) On the Orientation of the Backbone Dipoles in Native Folds. Proc Natl Acad Sci USA 102:7559–7564

    Article  ADS  Google Scholar 

  • Ripoll DR, Vila JA, Scheraga HA (2004) Folding of the Villin headpiece subdomain from random structures. Análisis of the charge distribution as function of pH. J Mol Biol 339:915–925

    Article  Google Scholar 

  • Simon K, Xu J, Kim C, Skrynnikov NR (2005) Estimating the accuracy of protein structures using residual dipolar couplings. J Biomol NMR 33:83–93

    Article  Google Scholar 

  • Spera S, Bax A (1991) Empirical correlation between protein backbone conformation and Cα and Cβ 13C nuclear magnetic resonance chemical shifts. J Am Chem Soc 113:5490–5492

    Article  Google Scholar 

  • Schubert M, Laudde D, Oschkinat H, Schmieder P (2002) A software tool for the prediction of Xaa-Pro peptide bond conformations in proteins based on 13C chemical shift statistic. J Biomol NMR 24:149–154

    Article  Google Scholar 

  • Sun H, Sanders LK, Oldfield E (2002) Carbon-13 NMR shielding in the twenty common amino acids: comparisons with experimental results in proteins. J Am Chem Soc 124:5486–5495

    Article  Google 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 237:544–559

    Article  Google Scholar 

  • Vijay-Kumar S, Bugg CE, Cook WJ (1987) Structure of ubiquitin refined at 1.8 Å resolution. J Mol Biol 194:531–544

    Article  Google Scholar 

  • Vila JA, Baldoni HA, Ripoll DR, Scheraga HA (2003) Unblocked statistical-coil tetrapeptides in aqueous solution: quantum-chemical computation of the carbon-13 NMR chemical shifts. J Biomol NMR 26:113–130

    Article  Google Scholar 

  • Vila JA, Baldoni HA, Ripoll DR, Ghosh A, Scheraga HA (2004a) Polyproline II helix conformation in a proline-rich enviroment: a theoretical study. Biophys J 86:731–742

    Article  Google Scholar 

  • Vila JA, Baldoni HA, Ripoll DR, Scheraga HA (2004b) Fast and accurate computation of the 13C chemical shifts for an alanine-rich peptide. Proteins 57:87–98

    Article  Google Scholar 

  • Vila JA, Ripoll DR, Baldoni HA, Scheraga HA (2002) Unblocked statistical-coil tetrapeptides and pentapeptides in aqueous solution: a theoretical study. J Biomol NMR 24:245–262

    Article  Google Scholar 

  • Vila JA, Ripoll DR, Scheraga HA (2007) Use of 13Cα chemical shifts in protein structure determination. J Phys Chem B (in press)

  • Villegas ME, Vila JA, Scheraga HA (2007) Effects of side-chain orientation on the 13C chemical shifts of antiparallel β-sheet model peptides. J Biomol NMR 37:137–146

    Article  Google Scholar 

  • Vriend GJ (1990) A molecular modeling and drug design. Mol Graph 8:52–56

    Article  Google Scholar 

  • Vriend G, Sander C (1993) Quality control of protein models: directional atomic contact analysis. J Appl Crystallogr 26:47–60

    Article  Google Scholar 

  • Wang Y, Jardetzky O (2002) Probability-based protein secondary structure identification using combined NMR chemical-shift data. Protein Sci 11:852–861

    Article  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 276:417–436

    Article  Google Scholar 

  • Wishart DS, Case DA (2001) Use of chemical shifts in macromolecular structure determination. Methods Enzymol 338:3–34

    Article  Google Scholar 

  • Wishart DS, Bigam CG, Yao J, Abildgaard F, Dyson HJ, Oldfield E, Markley JL, Sykes BD (1995) 1H, 13C and 15N chemical shift referencing in biomolecular NMR. J Biomol NMR 6:135–140

    Article  Google Scholar 

  • Wishart DS, Nip AM (1998) Protein chemical shift analysis: a practical guide. Biochem Cell Biol 76:153–163

    Article  Google Scholar 

  • Xu X-P, Case DAJ (2001) Automatic prediction of 15N, 13Cα, 13Cβ and 13C′ chemical shifts in proteins using a density functional database. J Biomol NMR 21:321–333

    Article  Google Scholar 

  • Xu X-P, Case DA (2002) Probing multiple effects on 15N, 13Cα, 13Cβ and 13C′ chemical shifts in peptides using density functional theory. Biopolymers 65:408–423

    Article  Google Scholar 

  • Zhao D, Jardetzky O (1994) An assessment of the precision and accuracy of protein structures determined by NMR. Dependence on distance erros. J Mol Biol 239:601–607

    Article  Google Scholar 

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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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