13C Chemical Shifts in Proteins: A Rich Source of Encoded Structural Information

  • Jorge A. VilaEmail author
  • Yelena A. Arnautova
Part of the Springer Series on Bio- and Neurosystems book series (SSBN, volume 8)


Despite the formidable progress in Nuclear Magnetic Resonance (NMR) spectroscopy, quality assessment of NMR-derived structures remains as an important problem. Thus, validation of protein structures is essential for the spectroscopists, since it could enable them to detect structural flaws and potentially guide their efforts in further refinement. Moreover, availability of accurate and efficient validation tools would help molecular biologists and computational chemists to evaluate quality of available experimental structures and to select a protein model which is the most suitable for a given scientific problem. The 13Cα nuclei are ubiquitous in proteins, moreover, their shieldings are easily obtainable from NMR experiments and represent a rich source of encoded structural information that makes 13Cα chemical shifts an attractive candidate for use in computational methods aimed at determination and validation of protein structures. In this chapter, the basis of a novel methodology of computing, at the quantum chemical level of theory, the 13Cα shielding for the amino acid residues in proteins is described. We also identify and examine the main factors affecting the 13Cα-shielding computation. Finally, we illustrate how the information encoded in the 13C chemical shifts can be used for a number of applications, viz., from protein structure prediction of both α-helical and β-sheet conformations, to determination of the fraction of the tautomeric forms of the imidazole ring of histidine in proteins as a function of pH or to accurate detection of structural flaws, at a residue-level, in NMR-determined protein models.


  1. 1.
    Bhattacharya, A., Tejero, R., Montelione, G.T.: Evaluating protein structures determined by structural genomics consortia. Proteins 66, 778–795 (2007)CrossRefGoogle Scholar
  2. 2.
    Billeter, M., Wagner, G., Wüthrich, K.: Solution NMR structure determination of proteins revisited. J. Biomol. NMR 42, 155–158 (2008)CrossRefGoogle Scholar
  3. 3.
    Williamson, M.P., Craven, C.J.: Automated protein structure calculation from NMR data. J. Biomol. NMR 43, 131–143 (2009)CrossRefGoogle Scholar
  4. 4.
    Williamson, M.P., Kikuchi, J., Asajura, T.: Application of 1H-NMR chemical-shifts to measure the quality of protein structures. J. Mol. Biol. 247, 541–546 (1995)Google Scholar
  5. 5.
    Davis, I.W., Leaver-Fay, A., Chen, V.B., Block, J.N., Kapral, G.J., Wang, X., Murray, L.W., Arendall III, W.B., Snoeyink, J., Richardson, J.S., Richardson, D.C.: MolProbity: all-atom contacts and structure validation for proteins and nucleic acids. Nucleic Acids Res. 35, W375–W383 (2007)CrossRefGoogle Scholar
  6. 6.
    Huang, Y.J., Powers, R., Montelione, G.T.: Protein NMR Recall, Precision, and F-measure scores (RPF scores): Structure quality assessment measures based on information retrieval statistics. J. Am. Chem. Soc. 127, 1665–1674 (2005)CrossRefGoogle Scholar
  7. 7.
    Huang, Y.J., Tejero, R., Powers, R., Montelione, G.T.: A topology-constrained distance network algorithm for protein structure determination from NOESY data. Proteins 62, 587–603 (2006)CrossRefGoogle Scholar
  8. 8.
    Laskowski, R.A., MacArthur, M.W., Moss, D.S., Thornton, J.: PROCHECK—a program to check the stereochemical quality of protein structures. J. Appl. Cryst. 26, 283–291 (1993)CrossRefGoogle Scholar
  9. 9.
    Lovell, S.C., Davis, I.W., Arendall III, W.B., de Bakker, P.I.W., Word, J.M., Prisant, M.G., Richardson, J.S., Richardson, D.C.: Structure validation by Cα geometry: ϕ, ψ, and Cβ deviation. Proteins 50, 437–450 (2003)CrossRefGoogle Scholar
  10. 10.
    Lüthy, R., Bowie, J.U., Eisenberg, D.: Assessment of protein models with three-dimensional profiles. Nature 356, 83–85 (1992)CrossRefGoogle Scholar
  11. 11.
    Nabuurs, S.B., Spronk, C.A.E.M., Vuister, G.W., Vriend, G.: Tradional biomolecular structure determination by NMR spectroscopy allows for major errors PLOS. Comp. Biol. 2, 71–79 (2006)Google Scholar
  12. 12.
    Vriend, G.: WHAT IF: a molecular modeling and drug design program. J. Mol. Graph. 8, 52–56 (1990)CrossRefGoogle Scholar
  13. 13.
    Berjanskii, M., Wishart, D.S.: A simple method to predict protein flexibility using secondary chemical shifts. J. Am. Chem. Soc. 127, 14970–14971 (2005)CrossRefGoogle Scholar
  14. 14.
    Berjanskii, M., Wishart, D.S.: The RCI server: rapid and accurate calculation of protein flexibility using chemical shifts. Nucleic Acids Res. 35, W531–W537 (2007)CrossRefGoogle Scholar
  15. 15.
    Cornilescu, G., Delaglio, F., Bax, A.: Protein backbone angle restraints from searching a database for chemical shift and sequence homology. J. Biomol. NMR 13, 289–302 (1999)CrossRefGoogle Scholar
  16. 16.
    de Dios, A.C., Pearson, J.G., Oldfield, E.: Chemical shifts in proteins: An ab initio study of carbon-13 nuclear magnetic resonance chemical shielding in glycine alanine and valine residues. J. Am. Chem. Soc. 115, 9768–9773 (1993)CrossRefGoogle Scholar
  17. 17.
    de Dios, A.C., Pearson, J.G., Oldfield, E.: Secondary and tertiary structural effects on protein NMR chemical shifts: An ab initio approach. Science 260, 1491–1496 (1993)CrossRefGoogle Scholar
  18. 18.
    Frank, A., Möller, H.M., Exner, T.H.: Toward the quantum chemical calculation of NMR chemical shifts of proteins. 2 Level of theory, basis set, and solvent model dependence. J. Chem. Theory Comput. 8, 1480–1492 (2012)CrossRefGoogle Scholar
  19. 19.
    Havlin, R.H., Le, H., Laws, D.D., de Dios, A.C., Oldfield, E.: 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 (1997)CrossRefGoogle Scholar
  20. 20.
    Iwadate, M., Asakura, T., Williamson, M.P.: Cα and Cβ carbon-13 chemical shifts in proteins from an empirical database. J. Biomol. NMR 13, 199–211 (1999)CrossRefGoogle Scholar
  21. 21.
    Kuszewski, J., Qin, J., Gronenborn, A.M., Clore, M.: The impact of direct refinement against 13Cα and 13Cβ chemical shifts on protein structure determination by NMR. J. Magn. Reson. Ser. B 106, 92–96 (1995)CrossRefGoogle Scholar
  22. 22.
    Luginbühl, P., Szyperski, T., Wüthrich, K.: Statistical basis for the use of 13Cα chemical shift in protein structure determination. J. Magn. Reson. 109, 229–233 (1995)CrossRefGoogle Scholar
  23. 23.
    Meiler, J.: PROSHIFT: protein chemical shift prediction using artificial neural networks. J. Biomol. NMR 26, 25–37 (2003)CrossRefGoogle Scholar
  24. 24.
    Neal, S., Nip, A.M., Zhang, H., Wishart, D.S.: Rapid and accurate calculation of protein 1H, 13C and 15 N chemical shifts. J. Biomol. NMR 26, 215–240 (2003)CrossRefGoogle Scholar
  25. 25.
    Shen, Y., Bax. Ad.: Protein backbone chemical shifts predicted from searching a database for torsional angle and sequence homology. J. Biomol. NMR, 38, 289–302 (2007)CrossRefGoogle Scholar
  26. 26.
    Shen, Y., Lange, O., Delaglio, F., Rossi, P., Aramini, J.M., Liu, G., Eletsky, A., Wu, Y., Singarapu, K.K., Lemak, A., et al.: Consistent blind protein structure generation from NMR chemical shift data. Proc. Natl. Acad. Sci. U. S. A. 105, 4685–4690 (2008)CrossRefGoogle Scholar
  27. 27.
    Spera, S., Bax, A.: Empirical correlation between protein backbone conformation and Cα and Cβ 13C nuclear magnetic resonance chemical shifts. J. Am. Chem. Soc. 113, 5490–5492 (1991)CrossRefGoogle Scholar
  28. 28.
    Vila, J.A., Arnautova, Y.A., Martin, O.A., Scheraga, H.A.: Quantum-mechanics-derived 13Cα chemical shift server (CheShift) for Protein Structure validation. Proc. Natl. Acad. Sci. U. S. A 106, 16972–16977 (2009)CrossRefGoogle Scholar
  29. 29.
    Vila, J.A., Arnautova, Y.A., Scheraga, H.A.: Use of 13Cα chemical shifts for accurate determination of β-sheet structures in solution. Proc. Natl. Acad. Sci. U. S. A. 105, 1891–1896 (2008)CrossRefGoogle Scholar
  30. 30.
    Vila, J.A., Aramini, J.M., Rossi, P., Kuzin, A., Su, M., Seetharaman, J., Xiao, R., Tong, L., Montelione, G.T., Scheraga, H.A.: Quantum chemical 13Cα chemical shift calculations for protein NMR structure determination. refinement, and validation. Proc. Natl. Acad. Sci. U. S. A. 105, 14389–14394 (2008)CrossRefGoogle Scholar
  31. 31.
    Vila, J.A., Baldoni, H.A., Ripoll, D.R., Ghosh, A., Scheraga, H.A.: Polyproline II helix conformation in a proline-rich environment: a theoretical Study. Biophys. J. 86, 731–742 (2004)CrossRefGoogle Scholar
  32. 32.
    Vila, J.A., Baldoni, H.A., Ripoll, D.R., Scheraga, H.A.: Unblocked statistical-coil tetrapeptides in aqueous solution: quantum-chemical computation of the carbon-13 NMR chemical shifts. J. Biomol. NMR 26, 113–130 (2003)CrossRefGoogle Scholar
  33. 33.
    Vila, J.A., Villegas, M.E., Baldoni, H.A., Scheraga, H.A.: Predicting 13Cα chemical shifts for validation of protein structures. J. Biomol. NMR 38, 221–235 (2007)CrossRefGoogle Scholar
  34. 34.
    Vila, J.A., Scheraga, H.A.: Assessing the accuracy of protein structures by quantum mechanical computations of 13Cα chemical shifts. Acc. Chem. Res. 42, 1545–1553 (2009)CrossRefGoogle Scholar
  35. 35.
    Villegas, M.E., Vila, J.A., Scheraga, H.A.: Effects of side-chain orientation on the 13C chemical shifts of antiparallel β-sheet model peptides. J. Biomol. NMR 37, 137–146 (2007)CrossRefGoogle Scholar
  36. 36.
    Wishart, D., Bigam, C.G., Yao, J., Abildgaard, F., Dyson, H., Oldfield, E., Markley, J., Sykes, B.: 1H, 13C and 15 N chemical shift referencing in biomolecular NMR. J. Biomol. NMR 6, 135–140 (1995)CrossRefGoogle Scholar
  37. 37.
    Wishart, D., Bigam, C.G., Holm, A., Hodges, R.S., Sykes, B.D.: 1H, 13C and 15 N random coil NMR chemical shifts of the common amino acids. I Investigation of nearest-neigbor effects. J. Biomol. NMR 5, 67–81 (1995)CrossRefGoogle Scholar
  38. 38.
    Xu, X.-P., Case, D.A.: Probing multiple effects on 15 N, 13Cα, 13Cβ and 13C′ chemical shifts in peptides using density functional theory. Biopolymers 65, 408–423 (2002)CrossRefGoogle Scholar
  39. 39.
    Xu, X.-P., Case, D.A.: Automated prediction of 15 N, 13Cα, 13Cβ and 13C’ chemical shifts in proteins using a density functional database. J. Biomol. NMR 21, 321–333 (2001)CrossRefGoogle Scholar
  40. 40.
    Parr, R.G., Yang, W.: Density functional theory of atoms and molecules. Oxford University Press, New York (1989)Google Scholar
  41. 41.
    Arnautova, Y.A., Vila, J.A., Martin, O.A., Scheraga, H.A.: What can we learn by computing 13Cα chemical shifts for X-ray protein models? Acta Crystallogr. D D65, 697–703 (2009)CrossRefGoogle Scholar
  42. 42.
    Martin, O.A., Villegas, M.E., Vila, J.A., Scheraga, H.A.: Analysis of 13Cα and 13Cβ chemical shifts of cysteine and cystine residues in proteins: A quantum chemical approach. J. Biomol. NMR 46, 217–225 (2010)CrossRefGoogle Scholar
  43. 43.
    Vila, J.A., Arnautova, Y.A.: Vorobjev and Scheraga HA. Assessing the fractions of tautomeric forms of the imidazole ring of histidine in proteins as a function of pH. Proc. Natl. Acad. Sci. U. S. A. 108, 5602–5607 (2011)CrossRefGoogle Scholar
  44. 44.
    Vila, J.A., Ripoll, D.R., Scheraga, H.A.: Use of 13Cα chemical shifts in protein structure determination. J. Phys. Chem. B 111, 6577–6585 (2007)CrossRefGoogle Scholar
  45. 45.
    Vila, J.A., Scheraga, H.A.: Factors affecting the use of 13Cα chemical shifts to determine, refine, and validate protein structures. Proteins: structure. Funct. Bioinformatics 71, 641–654 (2008)CrossRefGoogle Scholar
  46. 46.
    Wüthrich, K.: NMR of Proteins and Nucleic Acids. Wiley, New York, NY, U. S. A. (1986)Google Scholar
  47. 47.
    Sun, H., Sanders, L.K., Oldfield, E.: Carbon-13 NMR shielding in the twenty common amino acids: comparisons with experimental results in proteins. J. Am. Chem. Soc. 124, 5486–5495 (2002)CrossRefGoogle Scholar
  48. 48.
    Vila, J.A., Serrano, P., Wüthrich, K., Scheraga, H.A.: Sequential nearest-neighbor effects on computed 13Cα chemical shifts. J. Biomol. NMR 48, 23–30 (2010)CrossRefGoogle Scholar
  49. 49.
    Martin, O.A., Vila, J.A., Scheraga, H.A.: CheShift-2: graphic validation of protein structures. Bioinformatics 28, 1538–1539 (2012)CrossRefGoogle Scholar
  50. 50.
    Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N., Bourne, P.E.: Protein Data Bank Nucleic Acids Res. 28, 235–242 (2000)CrossRefGoogle Scholar
  51. 51.
    Brünger, A.T., Adams, P.D., Clore, G.M., DeLano, W.L., Gros, P., Grosse-Kunstleve, R.W., Jiang, J.-S., Kuszewski, J., Nilges, M., Pannu, N.S., Read, R.J., Rice, L.M., Simonson, T., Warren, G.L.: Crystallography and NMR system: a new software suite for macromolecular structure determination. Acta Crystallogr D 54, 905–921 (1998)CrossRefGoogle Scholar
  52. 52.
    Brünger, A.T.: Version 1.2 of the Crystallography and NMR system. Nat. Protoc. 2, 2728–2733 (2007)CrossRefGoogle Scholar
  53. 53.
    Cavalli, A., Salvatella, X., Dobson, C.M., Vendruscolo, M.: Protein structure determination from NMR chemical shifts. Proc. Natl. Acad. Sci. U.S.A. 104, 9615–9620 (2007)CrossRefGoogle Scholar
  54. 54.
    Cornilescu, G., Marquardt, J.L., Ottiger, M., Bax, A.: Validation of protein structure from anisotropic carbonyl chemical shifts in a dilute liquid crystalline phase. J. Am. Chem. Soc. 120, 6836–6837 (1998)CrossRefGoogle Scholar
  55. 55.
    Frank, A., Onila, I., Moller, H.M., Exner, T.E.: Toward the quantum chemical calculation of nuclear magnetic resonance chemical shifts of proteins. Proteins 79(2189), 2202 (2011)Google Scholar
  56. 56.
    Guerry, P., Herrmann, T.: Advances in automated NMR protein structure determination. Q. Rev. Biophys. 44, 257–309 (2011)CrossRefGoogle Scholar
  57. 57.
    Güntert, P.: Structure calculation of biological macromolecules from NMR data. Q. Rev. Biophys. 31, 145–237 (1998)CrossRefGoogle Scholar
  58. 58.
    Güntert, P.: Automated structure determination from NMR spectra. Eur. Biophys. J. 38, 129–143 (2009)CrossRefGoogle Scholar
  59. 59.
    Güntert, P., Braun, W., Wüthrich, K.: Efficient computation of threedimensional protein structures in solution from nuclear magnetic resonance data using the program DIANA and the supporting programs CALIBA, HABAS and GLOMSA. J. Mol. Biol. 217, 517–530 (1991)CrossRefGoogle Scholar
  60. 60.
    Rosato, A., Aramini, J.M., Arrowsmith, C., Bagaria, A., Baker, D., Cavalli, A., Doreleijers, J.F., Eletsky, A., Giachetti, A., Guerry, P., et al.: Blind testing of routine, fully automated determination of protein structures from NMR data. Structure 20, 227–236 (2012)CrossRefGoogle Scholar
  61. 61.
    Rosato, A., Bagaria, A., Baker, D., Bardiaux, B., Cavalli, A., Doreleijers, J.F., Giachetti, A., Guerry, P., Guntert, P., Herrmann, T., et al.: CASDNMR: critical assessment of automated structure determination by NMR. Nat. Methods 6, 625–626 (2009)CrossRefGoogle Scholar
  62. 62.
    Némethy, G., Gibson, K.D., Palmer, K.A., Yoon, C.N., Paterlini, G., Zagari, A., Rumsey, S., Scheraga, H.A.: Energy parameters in polypeptides. 10. Improved geometrical parameters and nonbonded interactions for use in the ECEPP/3 algorithm, with application to praline-containing peptides. J. Phys. Chem. 96, 6472–6484 (1992)CrossRefGoogle Scholar
  63. 63.
    Frisch, M.J., Trucks, G.W., Schlegel, H.B., Scuseria, G.E., Robb, M.A., Cheeseman, J.R., Zakrzewski, V.G., Montgomery, J.A., Jr Stratmann, R.E., Burant, J.C., et al.: Gaussian 03, Revision E.01, Gaussian, Inc., Wallingford CT (2003)Google Scholar
  64. 64.
    Chesnut, D.B., Moore, K.D.: Locally dense basis-sets for chemical-shift calculations. J. Comp. Chem. 10, 648–659 (1989)CrossRefGoogle Scholar
  65. 65.
    Jameson, A.K., Jameson, C.J.: Gas-phase 13C chemical shifts in the zero-pressure limit: Refinements to the absolute shielding scale for 13C J. Chem. Phys. Lett. 134, 461–466 (1997)CrossRefGoogle Scholar
  66. 66.
    Vásquez, M., Scheraga, H.A.: Variable-target-function and buildup procedures for the calculation of protein conformation—application to bovine pancreatic trypsin-inhibitor using limited simulated nuclear magnetic-resonance data. J. Biomol. Struct. Dyn. 5, 757–784 (1988)CrossRefGoogle Scholar
  67. 67.
    Kruskal Jr., J.B.: On the shortest spanning subtree of a graph and the traveling salesman problem. Proc. Am. Math. Soc. 7, 48–50 (1956)MathSciNetzbMATHCrossRefGoogle Scholar
  68. 68.
    Li, Z., Scheraga, H.A.: Monte Carlo minimization approach to the multiple minima problem in protein folding. Proc. Natl. Acad. Sci. U. S. A. 84, 6611–6615 (1987)MathSciNetCrossRefGoogle Scholar
  69. 69.
    Li, Z., Scheraga, H.A.: Structure and free energy of complex thermodynamic systems. J. Molec. Str. (Theochem) 179, 333–352 (1998)CrossRefGoogle Scholar
  70. 70.
    Arnautova, Y.A., Jagielska, A., Scheraga, H.A.: A new force field (ECEPP05) for peptides proteins and organic molecules. J. Phys. Chem. B 110, 5025–5044 (2006)CrossRefGoogle Scholar
  71. 71.
    Vila, J., Williams, R.L., Vásquez, M., Scheraga, H.A.: Empirical solvation models can be used to differentiate native from near-native conformations of bovine pancreatic trypsin inhibitor Proteins: structure. Funct. Genet. 10, 199–218 (1991)CrossRefGoogle Scholar
  72. 72.
    Ripoll, D.R., Ni, F.: Refinement of the thrombin-bound structure of a hirudin peptide by a restrained electrostatically driven monte-carlo method. Biopolymers 32, 359–365 (1992)CrossRefGoogle Scholar
  73. 73.
    Vorobjev, Y.N., Scheraga, H.A.: A fast adaptive multigrid boundary element method for macromolecule electrostatic computations in solvent. J. Comp. Chem. 18, 569–583 (1997)CrossRefGoogle Scholar
  74. 74.
    Vorobjev, Y.N., Vila, J.A., Scheraga, H.A.: FAMBE-pH: a fast and accurate method to compute the total solvation free energies of proteins. J. Phys. Chem. B 112, 11122–11136 (2008)CrossRefGoogle Scholar
  75. 75.
    Ripoll, D.R., Vorobjev, Y.N., Liwo, A., Vila, J.A., Scheraga, H.A.: Coupling between folding and ionization equilibria: Effects of pH on the conformational preferences of polypeptides. J. Mol. Biol. 264, 770–783 (1996)CrossRefGoogle Scholar
  76. 76.
    Vila, J.A., Ripoll, D.R., Arnaturova, Y.A., Vorobjev, Y.N., Scheraga, H.A.: Coupling between conformation and proton binding in proteins. Proteins 61, 56–68 (2005)CrossRefGoogle Scholar
  77. 77.
    Sitkoff, D., Sharp, K.A., Honig, B.: Accurate calculation of hydration free energies using macroscopic solvent models. J. Phys. Chem. 98, 1978–1988 (1994)CrossRefGoogle Scholar
  78. 78.
    Barth, P., Alber, T., Harbury, P.B.: Accurate, conformation-dependent predictions of solvent effects on protein ionization constants. Proc. Natl. Acad. Sci. U. S.A. 104, 4898–4903 (2007)CrossRefGoogle Scholar
  79. 79.
    Hass, M.A.S., Hansen, D.F., Christensen, H.E.M., Led, J.J., Kay, L.E.: Characterization of conformational exchange of a histidine side chain: protonation, rotamerization, and tautomerization of His61 plastocyanin from Anabaena variabilis. J. Am. Chem. Soc. 130, 8460–8470 (2008)CrossRefGoogle Scholar
  80. 80.
    Serrano, P., Johnson, M.A., Chatterjee, A., Neuman, B., Joseph, J.S., Buchmeier, M.J., Kuhn, P., Wüthrich, K.: NMR structure of the nucleic acid-binding domain of the SARS coronavirus nonstructural protein 3. J. Virol. 83, 12998–13008 (2009)CrossRefGoogle Scholar
  81. 81.
    Schwarzinger, S., Kroon, G.J.A., Foss, T.R., Chung, J., Wright, P.E., Dyson, H.J.: Sequence-dependent correction of random coil NMR chemical shifts. J. Am. Chem. Soc. 123, 2970–2978 (2001)CrossRefGoogle Scholar
  82. 82.
    Wang, Y., Jardetzky, O.: Investigation of the neighboring residue effects on protein chemical shifts. J. Am. Chem. Soc. 12, 14075–14084 (2002)CrossRefGoogle Scholar
  83. 83.
    Vijay-Kumar, S., Bugg, C.E., Cook, W.J.: Structure of ubiquitin refined at 1.8 Å resolution. J. Mol. Biol. 194, 531–544 (1987)CrossRefGoogle Scholar
  84. 84.
    Quirt, A.R., Lyerla Jr., J.R., Peat, I.R., Cohen, J.S.: Reynolds WF and freedman MH Carbon-13 nuclear magnetic resonance titration shifts in amino acids. J. Am. Chem. Soc. 96, 570–574 (1974)CrossRefGoogle Scholar
  85. 85.
    Rabenstein, D.L., Sayer, T.L.: Carbon-13 shifts parameters for amines, carboxylic acids and amino acids. J. Magn. Res. 24, 27–39 (1976)Google Scholar
  86. 86.
    Sayer, T.L., Rabenstein, D.L.: Nuclear magnetic resonance studies of the acid-base chemistry of amino acids and peptides. III Determination of the microscopic and macroscopic acid dissociation constants of α, ω-diaminocarboxylic acids Can. J. Chem. 54, 3392–3400 (1976)Google Scholar
  87. 87.
    Surprenant, H.L., Sarneski, J.E., Key, R.R., Byrd, J.T., Reilley, C.N.: Carbon-13 studies of amino acids: chemical shifts, protonation shifts, microscopic protonation behavior. J. Magn. Res. 40, 231–243 (1980)CrossRefGoogle Scholar
  88. 88.
    Lindorff-Larsen, K., Best, R.B., Depristo, M.A., Dobson, C.M., Vendruscolo, M.: Simultaneous determination of protein structure and dynamics. Nature 433, 128–132 (2005)CrossRefGoogle Scholar
  89. 89.
    Chakrabarti, P., Pal, D.: Main-chain conformational features at different conformations of the side-chains in proteins. Protein Eng. 11, 631–647 (1998)CrossRefGoogle Scholar
  90. 90.
    Dumbrack Jr., R.L., Karplus, M.: Conformational analysis of the backbone-dependent rotamer preferences of protein sidechains. J. Mol. Biol. 230, 543–574 (1993)CrossRefGoogle Scholar
  91. 91.
    Chothia, C., Levitt, M., Richardson, D.: Structure of proteins: packing of α-helices and β-sheets. Proc. Natl. Acad. Sci. U. S. A. 74, 4130–4134 (1977)CrossRefGoogle Scholar
  92. 92.
    Chou, K.-C., Pottle, M., Némethy, G., Ueda, Y., Scheraga, H.A.: Structure of β sheets. Origin of the right handed twist and of the increased stability of antiparallel over parallel sheets. J. Mol. Biol. 162, 89–112 (1982)Google Scholar
  93. 93.
    Chou, K.-C., Scheraga, H.A.: Origin of the right handed twist of β sheets of poly(L Val) chains. Proc. Natl. Acad. Sci. USA 79, 7047–7051 (1982)CrossRefGoogle Scholar
  94. 94.
    Creighton, T.E.: Proteins: Structure and Molecular Properties, pp. 186, 223. W.E. Freeman and Company, New York (1984)Google Scholar
  95. 95.
    Karplus, M.: Contact electron-spin coupling of nuclear magnetic moments. J. Chem. Phys. 30, 11–15 (1959)CrossRefGoogle Scholar
  96. 96.
    Mandel, M.: Proton Magnetic resonance spectra of some proteins: I. Ribonuclease, oxidized ribonuclease, lysozyme, and cytochrome c. J. Biol Chem. 240, 1586–1592 (1965)Google Scholar
  97. 97.
    Bradbury, J.H., Scheraga, H.A.: Structural studies of ribonuclease. XXIV. The application of nuclear magnetic resonance spectroscopy to distinguish between the histidine residues of ribonuclease. J. Am. Chem. Soc. 88, 4240–4246 (1966)CrossRefGoogle Scholar
  98. 98.
    Bachovchin, W.W.: 15 N NMR spectroscopy of hydrogen-bonding interactions in the active site of serine proteases: evidence for a moving histidine mechanism. Biochemistry 25, 7751–7759 (1986)CrossRefGoogle Scholar
  99. 99.
    Cheng, F., Sun, H., Zhang, Y., Mukkamala, D., Oldfield, E.: A solid state 13C NMR, crystallographic, and quantum chemical investigation of chemical shifts and hydrogen bonding in histidine dipeptides. J. Am. Chem. Soc. 127, 12544–12554 (2005)CrossRefGoogle Scholar
  100. 100.
    Farr-Jones, S., Wong, W.Y.L., Gutheil, W.G., Bachovchin, W.W.: Direct observation of the tautomeric forms of histidine in 15 N NMR spectra at low temperatures. Comments on intramolecular hydrogen bonding on tautomeric equilibrium. J. Am. Chem. Soc. 115, 6813–6819 (1993)CrossRefGoogle Scholar
  101. 101.
    Harbison, G., Herzfeld, J.: Griffin RGJ Nitrogen-15 chemical shifts tensors in L-histidine hydrochloride monohydrate. J. Am. Chem. Soc. 103, 4752–4754 (1981)CrossRefGoogle Scholar
  102. 102.
    Hass, M.A.S., Yilmaz, A., Christensen, H.E.M., Led, J.J.: Histidine side-chain dynamics and protonation monitored by 13C CPMG NMR relaxation dispersion. J. Biomol. NMR 44, 225–233 (2009)CrossRefGoogle Scholar
  103. 103.
    Hu, F., Wenbin, L., Hong, M.: Mechanism of proton conduction and gating in influenza M2 proton channels from solid-state NMR. Science 330, 505–508 (2010)CrossRefGoogle Scholar
  104. 104.
    Jensen, M.R., Has, M.A.S., Hansen, D.F., Led, J.J.: Investigating metal-binding in proteins by nuclear magnetic resonance. Cell. Mol. Life Sci. 64, 1085–1104 (2007)CrossRefGoogle Scholar
  105. 105.
    Markley, J.L.: Observation of histidine residues in proteins by means of nuclear magnetic resonance spectroscopy. Acc. Chem. Res. 8, 70–80 (1974)CrossRefGoogle Scholar
  106. 106.
    Meadows, D.H., Jardetzky, O., Epand, R.M., Ruterjans, H.H., Scheraga, H.A.: Proc. Natl. Acad. Sci. U.S.A. 60, 766–772 (1968)CrossRefGoogle Scholar
  107. 107.
    Pelton, J.G., Torchia, D.A., Meadow, N.D., Roseman, S.: Tautomeric states of the active-site histidine of phosphorylated and unphosphorylated IIIGlc, a signal-transducing protein from Escherichia coli, using two-dimensional heteronuclear NMR techniques ProtSci 2, 543–558 (1993)Google Scholar
  108. 108.
    Reynolds, W.F., Peat, I.R., Freedman, M.H., LyerlaJr, J.R.: Determination of the tautomeric form of the imidazole ring of L-Histidine in basic solution by carbon-13 magnetic resonance spectroscopy. J. Am. Chem. Soc. 95, 328–331 (1973)CrossRefGoogle Scholar
  109. 109.
    Schuster, I.I., Roberts, J.D.: Nitrogen-15 nuclear magnetic resonance spectroscopy. Effects of hydrogen bonding and protonation on nitrogen chemical shifts in imidazoles. J. Org. Chem. 44, 3864–3867 (1979)CrossRefGoogle Scholar
  110. 110.
    Shimba, N., Serber, Z., Lewidge, R., Miller, S.M., Craik, C.S., Dotsch, V.: Quantitative identification of the protonation state of histidine in vitro and in vivo. Biochem 42, 9227–9234 (2003)CrossRefGoogle Scholar
  111. 111.
    Shimba, N., Takahashi, H., Sakakura, M., Fuji, I., Shimada, I.: Determination of protonation and deprotonation forms and tautomeric states of histidine residues in large proteins using nitrogen-carbon J couplings in imidazole ring. J. Am. Chem. Soc. 120, 10988–10989 (1998)CrossRefGoogle Scholar
  112. 112.
    Steiner, T.: L-Histidyl-L-alanine dehydrate. Acta. Cryst. C 52, 2554–2556 (1996)CrossRefGoogle Scholar
  113. 113.
    Steiner, T., Koellner, G.: Coexistence of both histidines tautomers in the solid state and stabilization of the unfavorable Nδ-H form by intramolecular hydrogen bonding: rystalline L-His-Gly hemihydrates. Chem. Commun. 13, 1207–1208 (1997)CrossRefGoogle Scholar
  114. 114.
    Strohmeier, M., Stueber, D., Grant, D.M.: Accurate 13C and 15 N chemical shift and 14 N quadrupolar coupling constant calculations in amino acid crystals: Zwitterionic, hydrogen-bonded systems. J. Phys. Chem. A 107, 7629–7642 (2003)CrossRefGoogle Scholar
  115. 115.
    Sudmeier, J.L., Bradshaw, E.M., Coffman Haddad, K.E., Day, R.M., Thalhauser, C.J., Bullock, P.A., Bachovchin, W.W.: Identification of histidine tautomers in proteins by 2D 1H/13Cδ2 one-bond correlated NMR. J. Am. Chem. Soc. 125, 8430–8431 (2003)CrossRefGoogle Scholar
  116. 116.
    Wüthrich, K.: NMR in Biological Research: Peptides and Proteins. North-Holland, Amsterdam (1976)Google Scholar
  117. 117.
    Ulrich, E.L., Akutsu, H., Doreleijers, J.F., Harano, Y., Ioannidis, Y.E., Lin, J., Livny, M., Mading, S., Maziuk, D., Miller, Z., Nakatani, E., Schulte, C.F., Tolmie, D.E., Wenger, R.K., Yao, H., Markley, J.L.: BioMagResBank nucleic. Acids Res. 36, D402–D408 (2008)CrossRefGoogle Scholar
  118. 118.
    Demchuk, E., Wade, R.C.: Improving the continuum dielectric approach to calculating pKas of ionizeable groups in proteins. J. Phys. Chem. 100, 17373–17387 (1996)CrossRefGoogle Scholar
  119. 119.
    DePristo, M.A., de Bakker, P.I.W., Blundell, T.L.: Heterogeneity and inaccuracy in protein structures solved by X-ray crystallography. Structure 12, 831–838 (2004)CrossRefGoogle Scholar
  120. 120.
    Ringe, D., Petsko, G.A.: Study of protein dynamics by X-ray diffraction Methods in Emzymology 131, 389–433 (1986)Google Scholar
  121. 121.
    Furnham, N., Blundell, T.L., DePristo, M.A., Terwilliger, T.C.: Is one solution good enough? Nature Struct. Mol. Biol. 13, 184–185 (2006)CrossRefGoogle Scholar
  122. 122.
    Wang, Y., Jardetzky, O.: Probability-based protein secondary structure identification using combined NMR chemical-shift data. Prot Sci 11, 852–861 (2002)CrossRefGoogle Scholar
  123. 123.
    Höfinger, S., Almeida, B., Hansmann, U.H.E.: Parallel tempering molecular dynamics folding simulation of a signal peptide in explicit water. Proteins 68, 662–669 (2007)CrossRefGoogle Scholar
  124. 124.
    Jang, S., Kim, E., Pak, Y.: Free energy surfaces of miniproteins with a beta beta alpha motif: replica exchange molecular dynamics simulation with an implicit solvation model. Proteins 62, 663–671 (2006)CrossRefGoogle Scholar
  125. 125.
    Mohanty, S., Hansmann, U.H.E.: Folding of proteins with diverse folds. Biophy. J. 91, 3573–3578 (2006)CrossRefGoogle Scholar
  126. 126.
    Zhou, R.: Free energy landscape of protein folding in water: Explicit versus implicit solvent. Proteins 53, 148–161 (2003)CrossRefGoogle Scholar
  127. 127.
    Santiveri, C.M., Santoro, J., Rico, M., Jiménez, M.A.: Factors involved in the stability of isolated beta-sheets: turn sequence, beta-sheet twisting, and hydrophobic surface burial. Prot. Sci. 13, 1134–1147 (2004)CrossRefGoogle Scholar
  128. 128.
    Zhao, D., Jardetzky, O.: An assessment of the precision and accuracy of protein structures determined by NMR–dependence on distance errors. J. Mol. Biol. 239, 601–607 (1994)CrossRefGoogle Scholar
  129. 129.
    Korzhnev, D.M., Orekhov, V.Y., Arseniev, A.S.: Model-free approach beyond the borders of its applicability. J. Mag. Res. 127, 184–191 (1997)CrossRefGoogle Scholar
  130. 130.
    Palmer III, A.G.: NMR characterization of the dynamics of biomacromolecules. Chem. Rev. 104, 3623–3640 (2004)CrossRefGoogle Scholar
  131. 131.
    Case, D.A., Darden, T.A., Cheatham, T.E., III, Simmerling, C.L., Wang, J., Duke, R.E., Luo, R., Merz, K.M., Wang, B., Pearlman, D.A., et al.: AMBER 8 University of California, San Francisco (2004)Google Scholar
  132. 132.
    Zhou, Y., Vitkup, D., Karplus, M.: Native proteins are surface-molten solids: Application of the Lindemann criterion for the solid versus liquid state. J. Mol. Biol. 285, 1371–1375 (1999)CrossRefGoogle Scholar
  133. 133.
    Kuzin, A.P., Su M., Seetharaman, J., Janjua, H., Cunningham, K., Maglaqui, M., Owens, L.A., Zhao, L., Xiao, R., Baran, M.C., Acton, T.B., Rost, B., Montelione, G.T., Hunt, J.F., Tong, L.: Crystal structure of UPF0291 protein ynzC from Bacillus subtilis at resolution 2.0 A. (2008) Northeast Structural Genomics Consortium target SR384.
  134. 134.
    Kawai, Y., Moriya, S., Ogasawara, N.: Identification of a protein YneA, responsible for cell division suppression during the SOS response in Bacillus subtilis. Mol. Microbiol. 47, 1113–1122 (2003)CrossRefGoogle Scholar
  135. 135.
    Aramini, J.M., Sharma, S., Huang, Y.J., Swapna, G.V.T., Ho, C.K., Shetty, K., Cunningham, K., Ma, L.-C., Zhao, L., Owens, L.A., Jiang, M., Xiao, R., Liu, J., Baran, M.C., Acton, T.B., Rost, B., Montelione, G.T.: Solution NMR structure of the SOS response protein YnzC from Bacillus subtilis Proteins: Structure. Funct. Bioinformatics 72, 526–530 (2008)CrossRefGoogle Scholar
  136. 136.
    Vila, J. A., Baldoni, H. A., Scheraga, H. A.: performance of density functional models to reproduce observed 13Cα chemical shifts of proteins in solution. J. Comp. Chem. 38, 884–892 (2008b)CrossRefGoogle Scholar
  137. 137.
    Sippl, M.J.: Recognition of errors in three-dimensional structures of proteins. Proteins 17, 355–362 (1993)CrossRefGoogle Scholar
  138. 138.
    Kleywegt, G.J.: On vital aid: the why, what and how of validation Acta. Cryst, D 65, 134–139 (2009)CrossRefGoogle Scholar
  139. 139.
    Sevcik, J., Dauter, Z., Lamzin, V.S., Wilson, K.S.: Ribonuclease from streptomyces aureofaciens at atomic resolution. Acta Cryst D D52, 327–344 (1996)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.IMASL-CONICET, Universidad Nacional de San LuisSan LuisArgentina
  2. 2.Baker Laboratory of Chemistry and Chemical BiologyCornell UniversityIthacaUSA
  3. 3.Molsoft L.L.CSan DiegoUSA

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