Skip to main content

Advertisement

Log in

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

  • Article
  • Published:
Journal of Biomolecular NMR Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • 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 

  • Buckingham AD (1960) Chemical shifts in the nuclear magnetic resonance spectra of molecules containing polar groups. Can J Chem-Revue Canadienne De Chimie 38:300–307

    Article  Google Scholar 

  • Case DA (1995) Calibration of ring-current effects in proteins and nucleic acids. J Biomol NMR 6:341–346

    Article  Google Scholar 

  • Cavalli A, Salvatella X, Dobson CM, Vendruscolo M (2007) Protein structure determination from NMR chemical shifts. Proc Natl Acad Sci U S A 104:9615–9620

    Article  ADS  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 (1993) Secondary and tertiary structural effects on protein NMR chemical shifts—an ab initio approach. Science 260:1491–1496

    Article  ADS  Google Scholar 

  • 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 37:404–416

    Article  Google Scholar 

  • 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 32:1–12

    Article  Google Scholar 

  • Haigh CW, Mallion RB (1979) Ring current theories in nuclear magnetic resonance. Prog Nucl Magn Reson Spectrosc 13:303–344

    Article  Google Scholar 

  • 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 13:199–211

    Article  Google Scholar 

  • Karplus PA (1996) Experimentally observed conformation-dependent geometry and hidden strain in proteins. Protein Sci 5:1406–1420

    Article  Google Scholar 

  • Kay LE (1998) Protein dynamics from NMR. Nat Struct Biol 5:513–517

    Article  Google Scholar 

  • 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 131:13894–13895

    Article  Google Scholar 

  • Lee AL, Wand AJ (2001) Microscopic origins of entropy, heat capacity and the glass transition in proteins. Nature 411:501–504

    Article  ADS  Google Scholar 

  • Li DW, Brüschweiler R (2010) Certification of molecular dynamics trajectories with NMR chemical shifts. J Phys Chem Lett 1:246–248

    Article  Google Scholar 

  • 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 130:11097–11105

    Article  Google Scholar 

  • 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 109:229–233

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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 101:6946–6951

    Article  ADS  Google Scholar 

  • Mulder FAA (2009) Leucine side-chain conformation and dynamics in proteins from C-13 NMR chemical shifts. Chembiochem 10:1477–1479

    Article  Google Scholar 

  • 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 26:215–240

    Article  Google Scholar 

  • Palmer AG (1997) Probing molecular motion by NMR. Curr Opin Struct Biol 7:732–737

    Article  MathSciNet  Google Scholar 

  • 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 12:1556–1561

    Article  Google Scholar 

  • Ramirez BE, Voloshin ON, Camerini-Otero RD, Bax A (2000) Solution structure of DinI provides insight into its mode of RecA inactivation. Protein Sci 9:2161–2169

    Article  Google Scholar 

  • Rohl CA, Strauss CEM, Misura KMS, Baker D (2004) Protein structure prediction using rosetta. Meth Enzymol 383:66–93

    Article  Google Scholar 

  • 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 24:835–852

    Article  Google Scholar 

  • Shen Y, Bax A (2007) Protein backbone chemical shifts predicted from searching a database for torsion angle and sequence homology. J Biomol NMR 38:289–302

    Article  Google Scholar 

  • Shen Y, Bax A (2010) Prediction of Xaa-Pro peptide bond conformation from sequence and chemical shifts. J Biomol NMR 46:199–204

    Article  Google Scholar 

  • 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 105:4685–4690

    Article  ADS  Google Scholar 

  • 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 44:213–223

    Article  Google Scholar 

  • 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 113:5490–5492

    Article  Google Scholar 

  • 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 105:14389–14394

    Article  ADS  Google Scholar 

  • 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 106:16972–16977

    Article  ADS  Google Scholar 

  • 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 37:137–146

    Article  Google Scholar 

  • 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 28:327–340

    Article  Google Scholar 

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

    Google Scholar 

  • Wishart DS, Sykes BD, Richards FM (1991) Relationship between nuclear magnetic resonance chemical shift and protein secondary structure. J Mol Biol 222:311–333

    Article  Google Scholar 

  • 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 36:496–502

    Article  Google Scholar 

  • 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 21:321–333

    Article  Google Scholar 

  • 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 276:939–954

    Article  Google Scholar 

  • Zhang FL, Brüschweiler R (2002) Contact model for the prediction of NMR N–H order parameters in globular proteins. J Am Chem Soc 124:12654–12655

    Article  Google Scholar 

Download references

Acknowledgments

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ad Bax.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 233 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shen, Y., Bax, A. SPARTA+: a modest improvement in empirical NMR chemical shift prediction by means of an artificial neural network. J Biomol NMR 48, 13–22 (2010). https://doi.org/10.1007/s10858-010-9433-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10858-010-9433-9

Keywords

Navigation