Journal of Biomolecular NMR

, Volume 42, Issue 1, pp 23–33 | Cite as

Automated amino acid side-chain NMR assignment of proteins using 13C- and 15N-resolved 3D [1H,1H]-NOESY

  • Francesco Fiorito
  • Torsten Herrmann
  • Fred F. Damberger
  • Kurt WüthrichEmail author


ASCAN is a new algorithm for automatic sequence-specific NMR assignment of amino acid side-chains in proteins, which uses as input the primary structure of the protein, chemical shift lists of 1HN, 15N, 13Cα, 13Cβ and possibly 1Hα from the previous polypeptide backbone assignment, and one or several 3D 13C- or 15N-resolved [1H,1H]-NOESY spectra. ASCAN has also been laid out for the use of TOCSY-type data sets as supplementary input. The program assigns new resonances based on comparison of the NMR signals expected from the chemical structure with the experimentally observed NOESY peak patterns. The core parts of the algorithm are a procedure for generating expected peak positions, which is based on variable combinations of assigned and unassigned resonances that arise for the different amino acid types during the assignment procedure, and a corresponding set of acceptance criteria for assignments based on the NMR experiments used. Expected patterns of NOESY cross peaks involving unassigned resonances are generated using the list of previously assigned resonances, and tentative chemical shift values for the unassigned signals taken from the BMRB statistics for globular proteins. Use of this approach with the 101-amino acid residue protein FimD(25–125) resulted in 84% of the hydrogen atoms and their covalently bound heavy atoms being assigned with a correctness rate of 90%. Use of these side-chain assignments as input for automated NOE assignment and structure calculation with the ATNOS/CANDID/DYANA program suite yielded structure bundles of comparable quality, in terms of precision and accuracy of the atomic coordinates, as those of a reference structure determined with interactive assignment procedures. A rationale for the high quality of the ASCAN-based structure determination results from an analysis of the distribution of the assigned side chains, which revealed near-complete assignments in the core of the protein, with most of the incompletely assigned residues located at or near the protein surface.


Amino acid side-chain NMR assignment ASCAN Nuclear Overhauser effect NOESY Automation of protein structure determination 



We thank Dr. B. Pedrini for sharing his experience with applications of ASCAN for side-chain resonance assignments in a variety of proteins selected as targets in a structural genomics project. Financial support by the Schweizerischer Nationalfonds (project 3100-AO-113838) is gratefully acknowledged. Kurt Wüthrich is the Cecil H. and Ida M. Green Professor of Structural Biology at the Scripps Research Institute, and a member of the Skaggs Institute of Chemical Biology.


  1. Altieri AS, Byrd RA (2004) Automation of NMR structure determination of proteins. Curr Opin Struct Biol 14:547–553CrossRefGoogle Scholar
  2. Atreya HS, Sahu SC, Chary KV, Govil G (2000) A tracked approach for automated NMR assignments in proteins (TATAPRO). J Biomol NMR 17:125–136CrossRefGoogle Scholar
  3. Baran MC, Huang YJ, Moseley HNB, Montelione GT (2004) Automated analysis of protein NMR assignments and structures. Chem Rev 104:3541–3555CrossRefGoogle Scholar
  4. Bartels C, Güntert P, Billeter M, Wüthrich K (1997) Automated sequence-specific NMR assignment of homologous proteins using the program GARANT. J Comp Chem 18:139–149CrossRefGoogle Scholar
  5. Buchler NE, Zuiderweg ER, Wang H, Goldstein RA (1997) Protein heteronuclear NMR assignments using mean-field simulated annealing. J Magn Reson 125:34–42CrossRefADSGoogle Scholar
  6. Coggins BE, Zhou P (2003) PACES: protein sequential assignment by computer-assisted exhaustive search. J Biomol NMR 26:93–111CrossRefGoogle Scholar
  7. Eghbalnia HR, Bahrami A, Tonelli M, Hallenga K, Markley JL (2005) Probabilistic identification of spin systems and their assignments including coil-helix inference output (PISTACHIO). J Am Chem Soc 127:12528–12536CrossRefGoogle Scholar
  8. Etezady-Esfarjabi T, Placzek WJ, Herrmann T, Wüthrich K (2006) Solution structures of the putative anti-sigma-factor antagonist TM1442 from Thermotoga maritima in the free and phosphorylated states. Magn Reson Chem 44:61–70CrossRefGoogle Scholar
  9. Garrett DS, Powers R, Gronenborn AM, Clore GM (1991) A common sense approach to pick-peaking two-, three- and four-dimensional spectra using automatic computer analysis of contour diagrams. J Magn Reson 95:214–220Google Scholar
  10. Gronwald W, Kalbitzer HR (2004) Automated structure determination of proteins by NMR spectroscopy. Prog Nucl Magn Reson Spectrosc 44:33–96CrossRefGoogle Scholar
  11. Gronwald W, Kirchfofer R, Gorler A, Kremer W, Gansmeier B, Neidig KP, Kalbitzer HR (1998) CAMRA: chemical shift based computer aided protein NMR assignments. J Biomol NMR 12:395–405CrossRefGoogle Scholar
  12. Güntert P (2003) Automated NMR protein structure calculation. Prog Nucl Magn Reson Spectrosc 43:105–125CrossRefGoogle Scholar
  13. Güntert P, Braun W, Wüthrich K (1991) Efficient computation of three-dimensional protein structures in solution from nuclear magnetic resonance data using the program DIANA and the supporting programs CALIBA, HABAS and GLOMSA. J Mol Biol 217:517–530CrossRefGoogle Scholar
  14. Güntert P, Mumenthaler C, Wüthrich K (1997) Torsion angle dynamics for NMR structure calculation with the new program DYANA. J Mol Biol 273:283–298CrossRefGoogle Scholar
  15. Herrmann T, Güntert P, Wüthrich K (2002a) Protein NMR structure determination with automated NOE assignment using the new software CANDID and the torsion angle dynamics algorithm DYANA. J Mol Biol 319:209–227CrossRefGoogle Scholar
  16. Herrmann T, Güntert P, Wüthrich K (2002b) Protein NMR structure determination with automated NOE-identification in the NOESY spectra using the new software ATNOS. J Biomol NMR 24:171–189CrossRefGoogle Scholar
  17. Hyberts SG, Wagner G (2003) IBIS—a tool for automated sequential assignment of protein spectra from triple resonance experiments. J Biomol NMR 26:335–344CrossRefGoogle Scholar
  18. Koradi R, Billeter M, Wüthrich K (1996) MOLMOL: a program for display and analysis of macromolecular structures. J Mol Graph 14:51–55CrossRefGoogle Scholar
  19. Koradi R, Billeter M, Engeli M, Güntert P, Wüthrich K (1998) Automated peak picking and peak integration in macromolecular NMR spectra using AUTOPSY. J Magn Reson 135:288–297CrossRefADSGoogle Scholar
  20. Kraulis PJ (1989) ANSIG—a program for the assignment of protein H-1 2D NMR spectra by interactive computer graphics. J Magn Reson 24:627–633Google Scholar
  21. Malmodin D, Papavoine CHM, Billeter M (2003) Fully automated sequence-specific resonance assignments using multi-way decomposition. J Biomol NMR 27:69–79CrossRefGoogle Scholar
  22. Michel E, Damberger FF, Chen AM, Ishida Y, Leal WS, Wüthrich K (2005) Assignments for the Bombyx mori pheromone-binding protein fragment BmPBP (1–128) at pH 6.5. J Biomol NMR 31:65CrossRefGoogle Scholar
  23. Moseley HNB, Montelione GT (1999) Automated analysis of NMR assignments and structures of proteins. Curr Opin Struct Biol 9:635–642CrossRefGoogle Scholar
  24. Moseley HNB, Monleon D, Montelione GT (2001) Automatic determination of protein backbone resonance assignments from triple resonance nuclear magnetic resonance data. Methods Enzymol 339:91–108CrossRefGoogle Scholar
  25. Moseley HNB, Riaz N, Aramini JM, Szyperski T, Montelione GT (2004) A generalized approach to automated NMR peak list editing: application to reduced dimensionality triple resonance spectra. J Magn Reson 170:263–277CrossRefADSGoogle Scholar
  26. Nishiyama M, Horst R, Eidam O, Herrmann T, Ignatov O, Vetsch M, Bettendorff P, Jelesarov I, Grütter MG, Wüthrich K, Glockshuber R, Capitani G (2005) Structural basis of chaperone-subunit complex recognition by the type 1 pilus assembly platform FimD. EMBO J 24:2075–2086CrossRefGoogle Scholar
  27. Orekhov VY, Ibraghimov VI, Billeter M (2001) MUNIN: a new approach to multi-dimensional NMR spectra interpretation. J Biomol NMR 20:49–60CrossRefGoogle Scholar
  28. Seavey BR, Farr EA, Westler WM, Markley JL (1991) A relational database for sequence-specific protein NMR data. J Biomol NMR 1:217–236CrossRefGoogle Scholar
  29. Slupsky CM, Boyko RF, Booth VK, Sykes BD (2003) Smartnotebook: a semi-automated approach to protein sequential NMR resonance assignments. J Biomol NMR 27:313–321CrossRefGoogle Scholar
  30. Wüthrich K (1986) NMR of proteins and nucleic acids. Wiley, New YorkGoogle Scholar
  31. Wüthrich K, Billeter M, Braun W (1983) Pseudo-structures for the 20 common amino acids for use in studies of protein conformations by measurements of intramolecular proton–proton distance constraints with nuclear magnetic resonance. J Mol Biol 169:949–961CrossRefGoogle Scholar
  32. Zimmerman DE, Kulikowski CA, Huang Y, Feng W, Tashiro M, Shimotakahara S, Chien C, Powers R, Montelione GT (1997) Automated analysis of protein NMR assignments using methods from artificial intelligence. J Mol Biol 269:592–610CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • Francesco Fiorito
    • 1
  • Torsten Herrmann
    • 1
    • 2
  • Fred F. Damberger
    • 1
  • Kurt Wüthrich
    • 1
    • 3
    Email author
  1. 1.Institut für Molekularbiologie und Biophysik, ETH ZürichZurichSwitzerland
  2. 2.Université de Lyon, CNRS/ENS Lyon/UCB Lyon-1, Centre Européen de RMN à Très Hauts Champs de LyonVilleurbanneFrance
  3. 3.Department of Molecular Biology and Skaggs Institute of Chemical BiologyThe Scripps Research InstituteLa JollaUSA

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