Journal of Biomolecular NMR

, Volume 38, Issue 3, pp 221–235 | Cite as

Predicting 13Cα chemical shifts for validation of protein structures

  • Jorge A. Vila
  • Myriam E. Villegas
  • Hector A. Baldoni
  • Harold A. Scheraga
Article

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.

Keywords

13C chemical shift prediction Solution structure Protein structure validation X-ray and NMR structures Ubiquitin 

Notes

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.

Supplementary material

References

  1. 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 12:1335–1241CrossRefGoogle Scholar
  2. Amann BT, Worthington MT, Berg JMA (2003) A Cys3His zinc-binding domain from Nup475/Tristetraprolin: a novel fold with a disklike structure. Biochem 42:217–221CrossRefGoogle Scholar
  3. 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 43:16076–16085CrossRefGoogle Scholar
  4. Ban Y-E, Rudolph J, Zhou P, Edelsbrunner H (2006) Evaluating the quality of NMR structures by local density of protons. Proteins 62:852–864CrossRefGoogle Scholar
  5. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The Protein Data Bank. Nucleic Acids Res 28:235–242CrossRefGoogle Scholar
  6. Biological Magnetic Resonance Data Bank (http://www.bmrb.wisc.edu)Google Scholar
  7. Case DA (2000) Interpretation of chemical shifts and coupling constants in macromolecules. Curr Opin Struct Biol 10:197–203CrossRefGoogle Scholar
  8. 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
  9. Celda B, Biamonti C, Arnau MJ, Tejero R, Montelione GT (1995) Combined use of 13C chemical shift and 1Hα13Cα heteronuclear NOE data in monitoring a protein NMR structure refinement. J Biomol NMR 5:161–172CrossRefGoogle Scholar
  10. Chakrabarti P, Pal D (1998) Main-chain conformational features at different conformations of the side-chains in proteins. Protein Eng 11:631–647CrossRefGoogle Scholar
  11. Chesnut DB, Moore KD (1989) Locally dense basis sets for chemical shift calculations. J Comp Chem 10:648–659CrossRefGoogle Scholar
  12. 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 120:6836–6837CrossRefGoogle Scholar
  13. Cornilescu G, Delaglio F, Bax A (1999) Protein backbone angle restraints from searching a database for chemical shift and sequence homology. J Biomol NMR 13:289–302CrossRefGoogle Scholar
  14. 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 115:9768–9773CrossRefGoogle Scholar
  15. de Dios AC, Pearson JG, Oldfield E (1993b) Secondary and tertiary structural effects on protein NMR chemical shifts: an ab initio approach. Science 260:1491–1496CrossRefADSGoogle Scholar
  16. Doreleijers JF, Rullmann JAC, Kaptein R (1998) Quality assessment of NMR structures: a statistical survey. J Mol Biol 281:149–164CrossRefGoogle Scholar
  17. Dunbrack RL Jr, Karplus M (1994) Conformational analysis of the backbone-dependent rotamer preferences of protein sidechains. Nat Struct Biol 1:334–340CrossRefGoogle Scholar
  18. Dyson HJ, Wright PE (2005) Elucidation of the protein folding landscape by NMR. Methods Enzymol 394:299–321CrossRefGoogle Scholar
  19. 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
  20. 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
  21. Hehre WJ, Radom L, Schleyer P, Pople JA (1986) Ab initio molecular orbital theory. Wiley, New YorkGoogle Scholar
  22. Howard OW, Lilley DMJ (1978) Carbon-13-NMR of peptides and proteins. Prog Nucl Magn Reson Spectrosc 12:1–40CrossRefGoogle Scholar
  23. Hunter C, Packer MJ, Zonta C (2005) From structure to chemical shift and vice-versa. Prog Nucl Magn Reson Spectrosc 47:27–39CrossRefGoogle Scholar
  24. Iwadate M, Asakura T, Williamson MP (1999) 13Cα and 13Cβ carbon-13 chemical shifs in protein from an empirical database. J Biomol NMR 13:199–211CrossRefGoogle Scholar
  25. Jameson CJ (1996) Understanding NMR chemical shifts. Annu Rev Phys Chem 47:135–169CrossRefGoogle Scholar
  26. 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 106:92–96CrossRefGoogle Scholar
  27. Laskowski RA, MacArthur MW, Moss DS, Thornton JM (1993) PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Cryst 26:283–291CrossRefGoogle Scholar
  28. 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 8:477–486CrossRefGoogle Scholar
  29. 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 117:9542–9546CrossRefGoogle Scholar
  30. Lindorff-Larsen K, Best RB, Depristo MA, Dobson CM, Vendruscolo M (2005) Simultaneous determination of protein structure and dynamics. Nature 433:128–132CrossRefADSGoogle Scholar
  31. 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
  32. Malthouse JPG (1985) 13C NMR of enzymes. Prog Nucl Magn Reson Spectrosc 18:1–59CrossRefGoogle Scholar
  33. Meiler JJ (2003) PROSHIFT: protein chemical shift prediction using artificial neural networks. J Biomol NMR 26:25–37CrossRefGoogle Scholar
  34. 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 39:113–122CrossRefGoogle Scholar
  35. 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 27:825–836CrossRefGoogle Scholar
  36. Morris AL, MacArthur MW, Hutchinson EG, Thornton JM (1992) Stereochemical quality of protein structure coordinates. Proteins 12:345–364CrossRefGoogle Scholar
  37. 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 55:483–486CrossRefGoogle Scholar
  38. 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 274:21776–21782CrossRefGoogle Scholar
  39. Neal S, Nip AM, Zhang H, Wishart DS (2003) Rapid and accurate calculation of protein 1H, 13C and 15N chemical shifts. J Biomol NMR 26:215–240CrossRefGoogle Scholar
  40. 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
  41. Oldfield E (2002) Chemical shifts in amino acids, peptides and proteins: from quantum chemistry to drug design. Annu Rev Phys Chem 53:349–378CrossRefGoogle Scholar
  42. 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 97:221–224CrossRefGoogle Scholar
  43. 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 119:11941–11950CrossRefGoogle Scholar
  44. 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 117:8823–8829CrossRefGoogle Scholar
  45. Pontius J, Richelle J, Wodak SJ (1996) Deviations from standard atomic volumes as a quality measure for protein crystal structures. J Mol Biol 264:121–136CrossRefGoogle Scholar
  46. 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
  47. 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 264:770–783CrossRefGoogle Scholar
  48. Ripoll DR, Vila JA, Scheraga HA (2005) On the Orientation of the Backbone Dipoles in Native Folds. Proc Natl Acad Sci USA 102:7559–7564CrossRefADSGoogle Scholar
  49. 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 339:915–925CrossRefGoogle Scholar
  50. Simon K, Xu J, Kim C, Skrynnikov NR (2005) Estimating the accuracy of protein structures using residual dipolar couplings. J Biomol NMR 33:83–93CrossRefGoogle Scholar
  51. 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 113:5490–5492CrossRefGoogle Scholar
  52. 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 24:149–154CrossRefGoogle Scholar
  53. 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 124:5486–5495CrossRefGoogle Scholar
  54. 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
  55. Vijay-Kumar S, Bugg CE, Cook WJ (1987) Structure of ubiquitin refined at 1.8 Å resolution. J Mol Biol 194:531–544CrossRefGoogle Scholar
  56. 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 26:113–130CrossRefGoogle Scholar
  57. 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 86:731–742CrossRefGoogle Scholar
  58. Vila JA, Baldoni HA, Ripoll DR, Scheraga HA (2004b) Fast and accurate computation of the 13C chemical shifts for an alanine-rich peptide. Proteins 57:87–98CrossRefGoogle Scholar
  59. Vila JA, Ripoll DR, Baldoni HA, Scheraga HA (2002) Unblocked statistical-coil tetrapeptides and pentapeptides in aqueous solution: a theoretical study. J Biomol NMR 24:245–262CrossRefGoogle Scholar
  60. Vila JA, Ripoll DR, Scheraga HA (2007) Use of 13Cα chemical shifts in protein structure determination. J Phys Chem B (in press)Google Scholar
  61. 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 37:137–146CrossRefGoogle Scholar
  62. Vriend GJ (1990) A molecular modeling and drug design. Mol Graph 8:52–56CrossRefGoogle Scholar
  63. Vriend G, Sander C (1993) Quality control of protein models: directional atomic contact analysis. J Appl Crystallogr 26:47–60CrossRefGoogle Scholar
  64. Wang Y, Jardetzky O (2002) Probability-based protein secondary structure identification using combined NMR chemical-shift data. Protein Sci 11:852–861CrossRefGoogle Scholar
  65. 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
  66. Wishart DS, Case DA (2001) Use of chemical shifts in macromolecular structure determination. Methods Enzymol 338:3–34CrossRefGoogle Scholar
  67. 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 6:135–140CrossRefGoogle Scholar
  68. Wishart DS, Nip AM (1998) Protein chemical shift analysis: a practical guide. Biochem Cell Biol 76:153–163CrossRefGoogle Scholar
  69. 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 21:321–333CrossRefGoogle Scholar
  70. Xu X-P, Case DA (2002) Probing multiple effects on 15N, 13Cα, 13Cβ and 13C′ chemical shifts in peptides using density functional theory. Biopolymers 65:408–423CrossRefGoogle Scholar
  71. 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 239:601–607CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2007

Authors and Affiliations

  • Jorge A. Vila
    • 1
    • 2
  • Myriam E. Villegas
    • 2
  • Hector A. Baldoni
    • 2
  • Harold A. Scheraga
    • 1
  1. 1.Baker Laboratory of Chemistry and Chemical BiologyCornell UniversityIthacaUSA
  2. 2.Instituto de Matemática Aplicada San Luis, CONICETUniversidad Nacional de San LuisEjército de Los AndesArgentina

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