Journal of Biomolecular NMR

, Volume 48, Issue 1, pp 13–22 | Cite as

SPARTA+: a modest improvement in empirical NMR chemical shift prediction by means of an artificial neural network

  • Yang Shen
  • Ad BaxEmail author


NMR chemical shifts provide important local structural information for proteins and are key in recently described protein structure generation protocols. We describe a new chemical shift prediction program, SPARTA+, which is based on artificial neural networking. The neural network is trained on a large carefully pruned database, containing 580 proteins for which high-resolution X-ray structures and nearly complete backbone and 13Cβ chemical shifts are available. The neural network is trained to establish quantitative relations between chemical shifts and protein structures, including backbone and side-chain conformation, H-bonding, electric fields and ring-current effects. The trained neural network yields rapid chemical shift prediction for backbone and 13Cβ atoms, with standard deviations of 2.45, 1.09, 0.94, 1.14, 0.25 and 0.49 ppm for δ15N, δ13C’, δ13Cα, δ13Cβ, δ1Hα and δ1HN, respectively, between the SPARTA+ predicted and experimental shifts for a set of eleven validation proteins. These results represent a modest but consistent improvement (2–10%) over the best programs available to date, and appear to be approaching the limit at which empirical approaches can predict chemical shifts.


Electric field Hydrogen bonding Torsion angles SHIFTX Structure database Camshift SPARTA 



This work was supported by the Intramural Research Program of the NIDDK, NIH, and by the Intramural AIDS-Targeted Antiviral Program of the Office of the Director of the NIH.

Supplementary material

10858_2010_9433_MOESM1_ESM.pdf (234 kb)
Supplementary material 1 (PDF 233 kb)


  1. 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
  2. Buckingham AD (1960) Chemical shifts in the nuclear magnetic resonance spectra of molecules containing polar groups. Can J Chem-Revue Canadienne De Chimie 38:300–307CrossRefGoogle Scholar
  3. Case DA (1995) Calibration of ring-current effects in proteins and nucleic acids. J Biomol NMR 6:341–346CrossRefGoogle Scholar
  4. Cavalli A, Salvatella X, Dobson CM, Vendruscolo M (2007) Protein structure determination from NMR chemical shifts. Proc Natl Acad Sci U S A 104:9615–9620CrossRefADSGoogle Scholar
  5. 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
  6. de Dios AC, Pearson JG, Oldfield E (1993) Secondary and tertiary structural effects on protein NMR chemical shifts—an ab initio approach. Science 260:1491–1496CrossRefADSGoogle Scholar
  7. Doreleijers JF, Vriend G, Raves ML, Kaptein R (1999) Validation of nuclear magnetic resonance structures of proteins and nucleic acids: hydrogen geometry and nomenclature. Proteins-Struct Funct Genet 37:404–416CrossRefGoogle Scholar
  8. Doreleijers JF, Nederveen AJ, Vranken W, Lin JD, Bonvin A, Kaptein R, Markley JL, Ulrich EL (2005) BioMagResBank databases DOCR and FRED containing converted and filtered sets of experimental NMR restraints and coordinates from over 500 protein PDB structures. J Biomol NMR 32:1–12CrossRefGoogle Scholar
  9. Haigh CW, Mallion RB (1979) Ring current theories in nuclear magnetic resonance. Prog Nucl Magn Reson Spectrosc 13:303–344CrossRefGoogle Scholar
  10. Iwadate M, Asakura T, Williamson MP (1999) C-alpha and C-beta carbon-13 chemical shifts in proteins from an empirical database. J Biomol NMR 13:199–211CrossRefGoogle Scholar
  11. Karplus PA (1996) Experimentally observed conformation-dependent geometry and hidden strain in proteins. Protein Sci 5:1406–1420CrossRefGoogle Scholar
  12. Kay LE (1998) Protein dynamics from NMR. Nat Struct Biol 5:513–517CrossRefGoogle Scholar
  13. Kohlhoff KJ, Robustelli P, Cavalli A, Salvatella X, Vendruscolo M (2009) Fast and accurate predictions of protein NMR chemical shifts from interatomic distances. J Am Chem Soc 131:13894–13895CrossRefGoogle Scholar
  14. Lee AL, Wand AJ (2001) Microscopic origins of entropy, heat capacity and the glass transition in proteins. Nature 411:501–504CrossRefADSGoogle Scholar
  15. Li DW, Brüschweiler R (2010) Certification of molecular dynamics trajectories with NMR chemical shifts. J Phys Chem Lett 1:246–248CrossRefGoogle Scholar
  16. London RE, Wingad BD, Mueller GA (2008) Dependence of amino acid side chain C-13 shifts on dihedral angle: application to conformational analysis. J Am Chem Soc 130:11097–11105CrossRefGoogle Scholar
  17. Luginbühl P, Szyperski T, Wüthrich K (1995) Statistical basis for the use of 13Cα chemical shifts in protein structure determination. J Magn Reson Ser B 109:229–233CrossRefGoogle Scholar
  18. Meiler J (2003) PROSHIFT: protein chemical shift prediction using artificial neural networks. J Biomol NMR 26:25–37CrossRefGoogle Scholar
  19. Morozov AV, Kortemme T, Tsemekhman K, Baker D (2004) Close agreement between the orientation dependence of hydrogen bonds observed in protein structures and quantum mechanical calculations. Proc Natl Acad Sci U S A 101:6946–6951CrossRefADSGoogle Scholar
  20. Mulder FAA (2009) Leucine side-chain conformation and dynamics in proteins from C-13 NMR chemical shifts. Chembiochem 10:1477–1479CrossRefGoogle Scholar
  21. Neal S, Nip AM, Zhang HY, Wishart DS (2003) Rapid and accurate calculation of protein H-1, C-13 and N-15 chemical shifts. J Biomol NMR 26:215–240CrossRefGoogle Scholar
  22. Palmer AG (1997) Probing molecular motion by NMR. Curr Opin Struct Biol 7:732–737CrossRefMathSciNetGoogle Scholar
  23. Ramelot TA, Ni SS, Goldsmith-Fischman S, Cort JR, Honig B, Kennedy MA (2003) Solution structure of Vibrio cholerae protein VC0424: a variation of the ferredoxin-like fold. Protein Sci 12:1556–1561CrossRefGoogle Scholar
  24. Ramirez BE, Voloshin ON, Camerini-Otero RD, Bax A (2000) Solution structure of DinI provides insight into its mode of RecA inactivation. Protein Sci 9:2161–2169CrossRefGoogle Scholar
  25. Rohl CA, Strauss CEM, Misura KMS, Baker D (2004) Protein structure prediction using rosetta. Meth Enzymol 383:66–93CrossRefGoogle Scholar
  26. Saito H (1986) Conformation-dependent C13 chemical shifts—a new means of conformational characterization as obtained by high resolution solid state C13 NMR. Magn Reson Chem 24:835–852CrossRefGoogle Scholar
  27. Shen Y, Bax A (2007) Protein backbone chemical shifts predicted from searching a database for torsion angle and sequence homology. J Biomol NMR 38:289–302CrossRefGoogle Scholar
  28. Shen Y, Bax A (2010) Prediction of Xaa-Pro peptide bond conformation from sequence and chemical shifts. J Biomol NMR 46:199–204CrossRefGoogle Scholar
  29. Shen Y, Lange O, Delaglio F, Rossi P, Aramini JM, Liu GH, Eletsky A, Wu YB, Singarapu KK, Lemak A, Ignatchenko A, Arrowsmith CH, Szyperski T, Montelione GT, Baker D, Bax A (2008) Consistent blind protein structure generation from NMR chemical shift data. Proc Natl Acad Sci U S A 105:4685–4690CrossRefADSGoogle Scholar
  30. Shen Y, Delaglio F, Cornilescu G, Bax A (2009) TALOS+: a hybrid method for predicting protein backbone torsion angles from NMR chemical shifts. J Biomol NMR 44:213–223CrossRefGoogle Scholar
  31. Spera S, Bax A (1991) Empirical correlation between protein backbone conformation and Ca and Cb 13C nuclear magnetic resonance chemical shifts. J Am Chem Soc 113:5490–5492CrossRefGoogle Scholar
  32. Vila JA, Aramini JM, Rossi P, Kuzin A, Su M, Seetharaman J, Xiao R, Tong L, Montelione GT, Scheraga HA (2008) Quantum chemical C-13(alpha) chemical shift calculations for protein NMR structure determination, refinement, and validation. Proc Natl Acad Sci U S A 105:14389–14394CrossRefADSGoogle Scholar
  33. Vila JA, Arnautova YA, Martin OA, Scheraga HA (2009) Quantum-mechanics-derived C-13(alpha) chemical shift server (CheShift) for protein structure validation. Proc Natl Acad Sci U S A 106:16972–16977CrossRefADSGoogle Scholar
  34. Villegas ME, Vila JA, Scheraga HA (2007) Effects of side-chain orientation on the C-13 chemical shifts of antiparallel beta-sheet model peptides. J Biomol NMR 37:137–146CrossRefGoogle Scholar
  35. Wang YJ, Jardetzky O (2004) Predicting N-15 chemical shifts in proteins using the preceding residue-specific individual shielding surfaces from phi, psi(i-1), and chi1 torsion angles. J Biomol NMR 28:327–340CrossRefGoogle Scholar
  36. Wishart DS, Case DA (2001) Use of chemical shifts in macromolecular structure determination. Methods Enzymol 338:3–34Google Scholar
  37. Wishart DS, Sykes BD, Richards FM (1991) Relationship between nuclear magnetic resonance chemical shift and protein secondary structure. J Mol Biol 222:311–333CrossRefGoogle Scholar
  38. Wishart DS, Arndt D, Berjanskii M, Tang P, Zhou J, Lin G (2008) CS23D: a web server for rapid protein structure generation using NMR chemical shifts and sequence data. Nucleic Acids Res 36:496–502CrossRefGoogle Scholar
  39. Xu XP, Case DA (2001) Automated prediction of N-15, C-13(alpha), C-13(beta) and C-13′ chemical shifts in proteins using a density functional database. J Biomol NMR 21:321–333CrossRefGoogle Scholar
  40. Yang DW, Mittermaier A, Mok YK, Kay LE (1998) A study of protein side-chain dynamics from new H-2 auto-correlation and C-13 cross-correlation NMR experiments: application to the N-terminal SH3 domain from drk. J Mol Biol 276:939–954CrossRefGoogle Scholar
  41. Zhang FL, Brüschweiler R (2002) Contact model for the prediction of NMR N–H order parameters in globular proteins. J Am Chem Soc 124:12654–12655CrossRefGoogle Scholar

Copyright information

© US Government 2010

Authors and Affiliations

  1. 1.Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney DiseasesNational Institutes of HealthBethesdaUSA

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