Protein Structural Information Derived from NMR Chemical Shift with the Neural Network Program TALOS-N

Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1260)

Abstract

Chemical shifts are obtained at the first stage of any protein structural study by NMR spectroscopy. Chemical shifts are known to be impacted by a wide range of structural factors, and the artificial neural network based TALOS-N program has been trained to extract backbone and side-chain torsion angles from 1H, 15N, and 13C shifts. The program is quite robust and typically yields backbone torsion angles for more than 90 % of the residues and side-chain χ1 rotamer information for about half of these, in addition to reliably predicting secondary structure. The use of TALOS-N is illustrated for the protein DinI, and torsion angles obtained by TALOS-N analysis from the measured chemical shifts of its backbone and 13Cβ nuclei are compared to those seen in a prior, experimentally determined structure. The program is also particularly useful for generating torsion angle restraints, which then can be used during standard NMR protein structure calculations.

Key words

NMR Chemical shifts Protein structure Side-chain conformation Artificial neural network Secondary structure Backbone torsion angle 

References

  1. 1.
    Wüthrich K (1986) NMR of proteins and nucleic acids. Wiley, New YorkGoogle Scholar
  2. 2.
    Englander SW, Wand AJ (1987) Main-chain-directed strategy for the assignment of 1H NMR spectra of proteins. Biochemistry 26:5953–5958PubMedCrossRefGoogle Scholar
  3. 3.
    Oh BH, Westler WM, Darba P et al (1988) Protein 13C spin systems by a single two-dimensional nuclear magnetic resonance experiment. Science 240:908–911PubMedCrossRefGoogle Scholar
  4. 4.
    Ikura M, Kay LE, Bax A (1990) A novel approach for sequential assignment of 1H, 13C, and 15N spectra of larger proteins: heteronuclear triple-resonance three-dimensional NMR spectroscopy. Application to calmodulin. Biochemistry 29:4659–4667PubMedCrossRefGoogle Scholar
  5. 5.
    Wagner G (1993) Prospects for NMR of large proteins. J Biomol NMR 3:375–385PubMedCrossRefGoogle Scholar
  6. 6.
    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
  7. 7.
    Wishart DS, Sykes BD, Richards FM (1991) Relationship between nuclear magnetic resonance chemical shift and protein secondary structure. J Mol Biol 222:311–333PubMedCrossRefGoogle Scholar
  8. 8.
    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
  9. 9.
    Haigh CW, Mallion RB (1979) Ring current theories in nuclear magnetic resonance. Prog Nucl Magn Reson Spectrosc 13:303–344CrossRefGoogle Scholar
  10. 10.
    Avbelj F, Kocjan D, Baldwin RL (2004) Protein chemical shifts arising from alpha-helices and beta-sheets depend on solvent exposure. Proc Natl Acad Sci U S A 101:17394–17397PubMedCentralPubMedCrossRefGoogle Scholar
  11. 11.
    de Dios AC, Pearson JG, Oldfield E (1993) Secondary and tertiary structural effects on protein NMR chemical shifts—an ab initio approach. Science 260:1491–1496PubMedCrossRefGoogle Scholar
  12. 12.
    Case DA (1998) The use of chemical shifts and their anisotropies in biomolecular structure determination. Curr Opin Struct Biol 8:624–630PubMedCrossRefGoogle Scholar
  13. 13.
    Vila JA, Aramini JM, Rossi P et al (2008) Quantum chemical C-13(alpha) chemical shift calculations for protein NMR structure determination, refinement, and validation. Proc Natl Acad Sci U S A 105:14389–14394PubMedCentralPubMedCrossRefGoogle Scholar
  14. 14.
    Kohlhoff KJ, Robustelli P, Cavalli A et al (2009) Fast and accurate predictions of protein NMR chemical shifts from interatomic distances. J Am Chem Soc 131:13894–13895PubMedCrossRefGoogle Scholar
  15. 15.
    Asakura T, Taoka K, Demura M et al (1995) The relationship between amide proton chemical shifts and secondary structure in proteins. J Biomol NMR 6:227–236PubMedCrossRefGoogle Scholar
  16. 16.
    Bax A, Grzesiek S (1993) Methodological advances in protein NMR. Acc Chem Res 26:131–138CrossRefGoogle Scholar
  17. 17.
    Sattler M, Schleucher J, Griesinger C (1999) Heteronuclear multidimensional NMR experiments for the structure determination of proteins in solution employing pulsed field gradients. Prog Nucl Magn Reson Spectrosc 34:93–158CrossRefGoogle Scholar
  18. 18.
    Salzmann M, Wider G, Pervushin K et al (1999) TROSY-type triple-resonance experiments for sequential NMR assignments of large proteins. J Am Chem Soc 121:844–848CrossRefGoogle Scholar
  19. 19.
    Wagner G, Pardi A, Wuthrich K (1983) Hydrogen-bond length and H-1-NMR chemical-shifts in proteins. J Am Chem Soc 105:5948–5949CrossRefGoogle Scholar
  20. 20.
    Cornilescu G, Delaglio F, Bax A (1999) Protein backbone angle restraints from searching a database for chemical shift and sequence homology. J Biomol NMR 13:289–302PubMedCrossRefGoogle Scholar
  21. 21.
    Berman HM, Kleywegt GJ, Nakamura H et al (2012) The Protein Data Bank at 40: reflecting on the past to prepare for the future. Structure 20:391–396PubMedCentralPubMedCrossRefGoogle Scholar
  22. 22.
    Markley JL, Ulrich EL, Berman HM et al (2008) BioMagResBank (BMRB) as a partner in the Worldwide Protein Data Bank (wwPDB): new policies affecting biomolecular NMR depositions. J Biomol NMR 40:153–155PubMedCentralPubMedCrossRefGoogle Scholar
  23. 23.
    Wishart DS (2011) Interpreting protein chemical shift data. Prog Nucl Magn Reson Spectrosc 58:62–87PubMedCrossRefGoogle Scholar
  24. 24.
    Shen Y, Delaglio F, Cornilescu G et al (2009) TALOS+: a hybrid method for predicting protein backbone torsion angles from NMR chemical shifts. J Biomol NMR 44:213–223PubMedCentralPubMedCrossRefGoogle Scholar
  25. 25.
    Shen Y, Bax A (2013) Protein backbone and sidechain torsion angles predicted from NMR chemical shifts using artificial neural networks. J Biomol NMR 56:227–241PubMedCentralPubMedCrossRefGoogle Scholar
  26. 26.
    Shen Y, Bax A (2010) SPARTA plus: a modest improvement in empirical NMR chemical shift prediction by means of an artificial neural network. J Biomol NMR 48:13–22PubMedCentralPubMedCrossRefGoogle Scholar
  27. 27.
    Jones DT (1999) Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 292:195–202PubMedCrossRefGoogle Scholar
  28. 28.
    Rost B, Sander C (1993) Prediction of protein secondary structure at better than 70 percent accuracy. J Mol Biol 232:584–599PubMedCrossRefGoogle Scholar
  29. 29.
    Ramirez BE, Voloshin ON, Camerini-Otero RD et al (2000) Solution structure of DinI provides insight into its mode of RecA inactivation. Protein Sci 9:2161–2169PubMedCentralPubMedCrossRefGoogle Scholar
  30. 30.
    Maltsev AS, Ying JF, Bax A (2012) Deuterium isotope shifts for backbone 1H, 15N and 13C nuclei in intrinsically disordered protein alpha-synuclein. J Biomol NMR 54:181–191PubMedCentralPubMedCrossRefGoogle Scholar
  31. 31.
    Berjanskii MV, Wishart DS (2005) A simple method to predict protein flexibility using secondary chemical shifts. J Am Chem Soc 127:14970–14971PubMedCrossRefGoogle Scholar
  32. 32.
    Kabsch W, Sander C (1983) Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22:2577–2637PubMedCrossRefGoogle Scholar
  33. 33.
    Schwieters CD, Kuszewski JJ, Tjandra N et al (2003) The Xplor-NIH NMR molecular structure determination package. J Magn Reson 160:65–73PubMedCrossRefGoogle Scholar
  34. 34.
    Herrmann T, Guntert P, Wuthrich K (2002) Protein NMR structure determination with automated NOE assignment using the new software CANDID and the torsion angle dynamics algorithm DYANA. J Mol Biol 319:209–227PubMedCrossRefGoogle Scholar
  35. 35.
    Markley JL, Bax A, Arata Y et al (1998) Recommendations for the presentation of NMR structures of proteins and nucleic acids (Reprinted from Pure and Applied Chemistry, vol 70, pp. 117–142, 1998). J Mol Biol 280:933–952PubMedCrossRefGoogle Scholar
  36. 36.
    Wang LY, Eghbalnia HR, Bahrami A et al (2005) Linear analysis of carbon-13 chemical shift differences and its application to the detection and correction of errors in referencing and spin system identifications. J Biomol NMR 32:13–22PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Laboratory of Chemical PhysicsNational Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of HealthBethesdaUSA

Personalised recommendations