Journal of Biomolecular NMR

, Volume 53, Issue 3, pp 167–180 | Cite as

Resolution-by-proxy: a simple measure for assessing and comparing the overall quality of NMR protein structures

  • Mark Berjanskii
  • Jianjun Zhou
  • Yongjie Liang
  • Guohui Lin
  • David S. WishartEmail author


In protein X-ray crystallography, resolution is often used as a good indicator of structural quality. Diffraction resolution of protein crystals correlates well with the number of X-ray observables that are used in structure generation and, therefore, with protein coordinate errors. In protein NMR, there is no parameter identical to X-ray resolution. Instead, resolution is often used as a synonym of NMR model quality. Resolution of NMR structures is often deduced from ensemble precision, torsion angle normality and number of distance restraints per residue. The lack of common techniques to assess the resolution of X-ray and NMR structures complicates the comparison of structures solved by these two methods. This problem is sometimes approached by calculating “equivalent resolution” from structure quality metrics. However, existing protocols do not offer a comprehensive assessment of protein structure as they calculate equivalent resolution from a relatively small number (<5) of protein parameters. Here, we report a development of a protocol that calculates equivalent resolution from 25 measurable protein features. This new method offers better performance (correlation coefficient of 0.92, mean absolute error of 0.28 Å) than existing predictors of equivalent resolution. Because the method uses coordinate data as a proxy for X-ray diffraction data, we call this measure “Resolution-by-Proxy” or ResProx. We demonstrate that ResProx can be used to identify under-restrained, poorly refined or inaccurate NMR structures, and can discover structural defects that the other equivalent resolution methods cannot detect. The ResProx web server is available at


Protein Structure Quality NMR X-ray resolution 



Funding for this project was provided by the Alberta Prion Research Institute (APRI), PrioNet, and the Natural Sciences and Engineering Research Council (NSERC).

Supplementary material

10858_2012_9637_MOESM1_ESM.doc (1.6 mb)
Supplementary material 1 (DOC 1587 kb)


  1. Andrec M, Snyder DA, Zhou Z, Young J, Montelione GT, Levy RM (2007) A large data set comparison of protein structures determined by crystallography and NMR: statistical test for structural differences and the effect of crystal packing. Proteins 69(3):449–465CrossRefGoogle Scholar
  2. Ban YE, Rudolph J, Zhou P, Edelsbrunner H (2006) Evaluating the quality of NMR structures by local density of protons. Proteins 62(4):852–864CrossRefGoogle Scholar
  3. Berjanskii M, Tang P, Liang J, Cruz JA, Zhou J, Zhou Y, Bassett E, MacDonell C, Lu P, Lin G, Wishart DS (2009) GeNMR: a web server for rapid NMR-based protein structure determination. Nucleic Acids Res 37(Web Server issue):W670–W677CrossRefGoogle Scholar
  4. Berjanskii M, Liang Y, Zhou J, Tang P, Stothard P, Zhou Y, Cruz J, MacDonell C, Lin G, Lu P, Wishart DS (2010) PROSESS: a protein structure evaluation suite and server. Nucleic Acids Res 38(Web Server issue):W633–W640CrossRefGoogle Scholar
  5. Bowers PM, Strauss CE, Baker D (2000) De novo protein structure determination using sparse NMR data. J Biomol NMR 18(4):311–318CrossRefGoogle Scholar
  6. Chen VB, Arendall WB 3rd, Headd JJ, Keedy DA, Immormino RM, Kapral GJ, Murray LW, Richardson JS, Richardson DC (2010) MolProbity: all-atom structure validation for macromolecular crystallography. Acta Crystallogr D Biol Crystallogr 66(Pt 1):12–21CrossRefGoogle Scholar
  7. Clore GM, Garrett DS (1999) R-factor, Free R, and complete cross-validation for dipolar coupling refinement of NMR structures. J Am Chem Soc 121(39):9008–9012CrossRefGoogle Scholar
  8. Cornilescu G, Marquardt JL, Ottiger M, Bax A (1998) Validation of protein structure from anisotropic carbonyl chemical shifts in a dilute liquid crystalline phase. J Am Chem Soc 120(27):6836–6837CrossRefGoogle Scholar
  9. Davis IW, Leaver-Fay A, Chen VB, Block JN, Kapral GJ, Wang X, Murray LW, Arendall WB III, Snoeyink J, Richardson JS, Richardson DC (2007) MolProbity: all-atom contacts and structure validation for proteins and nucleic acids. Nucleic Acids Res 35(Web Server issue):W375–W383CrossRefGoogle Scholar
  10. Eisenberg D, Luthy R, Bowie JU (1997) VERIFY3D: assessment of protein models with three-dimensional profiles. Methods Enzymol 277:396–404CrossRefGoogle Scholar
  11. Fan H, Mark AE (2003) Relative stability of protein structures determined by X-ray crystallography or NMR spectroscopy: a molecular dynamics simulation study. Proteins 53(1):111–120CrossRefGoogle Scholar
  12. Gronwald W, Kirchhofer R, Gorler A, Kremer W, Ganslmeier B, Neidig KP, Kalbitzer HR (2000) RFAC, a program for automated NMR R-factor estimation. J Biomol NMR 17(2):137–151CrossRefGoogle Scholar
  13. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. SIGKDD Explor Newsl 11(1):10–18CrossRefGoogle Scholar
  14. Han B, Liu Y, Ginzinger SW, Wishart DS (2011) SHIFTX2: significantly improved protein chemical shift prediction. J Biomol NMR 50(1):43–57CrossRefGoogle Scholar
  15. Hooft RW, Vriend G, Sander C, Abola EE (1996) Errors in protein structures. Nature 381(6580):272ADSCrossRefGoogle Scholar
  16. Huang YJ, Powers R, Montelione GT (2005) Protein NMR recall, precision, and F-measure scores (RPF scores): structure quality assessment measures based on information retrieval statistics. J Am Chem Soc 127(6):1665–1674CrossRefGoogle Scholar
  17. Koradi R, Billeter M, Wuthrich K (1996) MOLMOL: a program for display and analysis of macromolecular structures. J Mol Graph 14(1):51–55, 29–32Google Scholar
  18. Kwan AH, Mobli M, Gooley PR, King GF, Mackay JP (2011) Macromolecular NMR spectroscopy for the non-spectroscopist. FEBS J 278(5):687–703CrossRefGoogle Scholar
  19. Laskowski RA, MacArthur MW, Moss DS, Thornton JM (1993) PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Crystallogr 26(2):283–291CrossRefGoogle Scholar
  20. Laskowski RA, Rullmannn JA, MacArthur MW, Kaptein R, Thornton JM (1996) AQUA and PROCHECK-NMR: programs for checking the quality of protein structures solved by NMR. J Biomol NMR 8(4):477–486CrossRefGoogle Scholar
  21. Lindorff-Larsen K, Best RB, Depristo MA, Dobson CM, Vendruscolo M (2005) Simultaneous determination of protein structure and dynamics. Nature 433(7022):128–132ADSCrossRefGoogle Scholar
  22. Linge JP, Nilges M (1999) Influence of non-bonded parameters on the quality of NMR structures: a new force field for NMR structure calculation. J Biomol NMR 13(1):51–59CrossRefGoogle Scholar
  23. Linge JP, Williams MA, Spronk CA, Bonvin AM, Nilges M (2003) Refinement of protein structures in explicit solvent. Proteins 50(3):496–506CrossRefGoogle Scholar
  24. Minor DL Jr (2007) The neurobiologist’s guide to structural biology: a primer on why macromolecular structure matters and how to evaluate structural data. Neuron 54(4):511–533CrossRefGoogle Scholar
  25. Mitchell TM (1997) Machine learning. McGraw-Hill, New YorkzbMATHGoogle Scholar
  26. Nabuurs SB, Nederveen AJ, Vranken W, Doreleijers JF, Bonvin AM, Vuister GW, Vriend G, Spronk CA (2004) DRESS: a database of REfined solution NMR structures. Proteins 55(3):483–486CrossRefGoogle Scholar
  27. Nabuurs SB, Krieger E, Spronk CA, Nederveen AJ, Vriend G, Vuister GW (2005) Definition of a new information-based per-residue quality parameter. J Biomol NMR 33(2):123–134CrossRefGoogle Scholar
  28. Nabuurs SB, Spronk CA, Vuister GW, Vriend G (2006) Traditional biomolecular structure determination by NMR spectroscopy allows for major errors. PLoS Comput Biol 2(2):e9ADSCrossRefGoogle Scholar
  29. Neal S, Nip AM, Zhang H, Wishart DS (2003) Rapid and accurate calculation of protein 1H, 13C and 15 N chemical shifts. J Biomol NMR 26(3):215–240CrossRefGoogle Scholar
  30. Pontius J, Richelle J, Wodak SJ (1996) Deviations from standard atomic volumes as a quality measure for protein crystal structures. J Mol Biol 264(1):121–136CrossRefGoogle Scholar
  31. Raman S, Huang YJ, Mao B, Rossi P, Aramini JM, Liu G, Montelione GT, Baker D (2010) Accurate automated protein NMR structure determination using unassigned NOESY data. J Am Chem Soc 132(1):202–207CrossRefGoogle Scholar
  32. Ramelot TA, Raman S, Kuzin AP, Xiao R, Ma LC, Acton TB, Hunt JF, Montelione GT, Baker D, Kennedy MA (2009) Improving NMR protein structure quality by Rosetta refinement: a molecular replacement study. Proteins 75(1):147–167CrossRefGoogle Scholar
  33. Richards FM (1977) Areas, volumes, packing and protein structure. Annu Rev Biophys Bioeng 6:151–176CrossRefGoogle Scholar
  34. Schwieters CD, Kuszewski JJ, Tjandra N, Clore GM (2003) The Xplor-NIH NMR molecular structure determination package. J Magn Reson 160(1):65–73ADSCrossRefGoogle Scholar
  35. Seeliger D, de Groot BL (2007) Atomic contacts in protein structures. A detailed analysis of atomic radii, packing, and overlaps. Proteins 68(3):595–601CrossRefGoogle Scholar
  36. Sheffler W, Baker D (2009) RosettaHoles: rapid assessment of protein core packing for structure prediction, refinement, design, and validation. Protein Sci 18(1):229–239Google Scholar
  37. Sheffler W, Baker D (2010) RosettaHoles2: a volumetric packing measure for protein structure refinement and validation. Protein Sci 19(10):1991–1995CrossRefGoogle Scholar
  38. Shen Y, Vernon R, Baker D, Bax A (2009) De novo protein structure generation from incomplete chemical shift assignments. J Biomol NMR 43(2):63–78CrossRefGoogle Scholar
  39. Shevade SK, Keerthi SS, Bhattacharyya C, Murthy KK (2000) Improvements to the SMO algorithm for SVM regression. IEEE Trans Neural Netw 11(5):1188–1193CrossRefGoogle Scholar
  40. Sippl MJ (1993) Recognition of errors in three-dimensional structures of proteins. Proteins 17(4):355–362CrossRefGoogle Scholar
  41. Spronk CA, Linge JP, Hilbers CW, Vuister GW (2002) Improving the quality of protein structures derived by NMR spectroscopy. J Biomol NMR 22(3):281–289CrossRefGoogle Scholar
  42. Spronk CA, Nabuurs SB, Krieger E, Vriend G, Vuister GW (2004) Validation of protein structures derived by NMR spectroscopy. Prog Nucl Magn Reson Spectrosc 45(3):315–337CrossRefGoogle Scholar
  43. Vasilief I (2011) QtiPlot—data analysis and scientific visualisation., edn
  44. Vriend G (1990) WHAT IF: a molecular modeling and drug design program. J Mol Graph 8(1):52–56CrossRefGoogle Scholar
  45. Wiederstein M, Sippl MJ (2007) ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res 35(Web Server issue):W407–W410CrossRefGoogle Scholar
  46. Willard L, Ranjan A, Zhang H, Monzavi H, Boyko RF, Sykes BD, Wishart DS (2003) VADAR: a web server for quantitative evaluation of protein structure quality. Nucleic Acids Res 31(13):3316–3319CrossRefGoogle Scholar
  47. Wishart DS (2011) Interpreting protein chemical shift data. Prog Nucl Magn Reson Spectrosc 58(1):62–87CrossRefGoogle Scholar
  48. Wlodawer A, Minor W, Dauter Z, Jaskolski M (2008) Protein crystallography for non-crystallographers, or how to get the best (but not more) from published macromolecular structures. FEBS J 275(1):1–21CrossRefGoogle Scholar
  49. Word JM, Lovell SC, Richardson JS, Richardson DC (1999) Asparagine and glutamine: using hydrogen atom contacts in the choice of side-chain amide orientation. J Mol Biol 285(4):1735–1747CrossRefGoogle Scholar
  50. Xia B, Tsui V, Case DA, Dyson HJ, Wright PE (2002) Comparison of protein solution structures refined by molecular dynamics simulation in vacuum, with a generalized Born model, and with explicit water. J Biomol NMR 22(4):317–331CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Mark Berjanskii
    • 1
  • Jianjun Zhou
    • 1
  • Yongjie Liang
    • 1
  • Guohui Lin
    • 1
  • David S. Wishart
    • 1
    • 2
    • 3
    Email author
  1. 1.Department of Computing ScienceUniversity of AlbertaEdmontonCanada
  2. 2.Department of Biological SciencesUniversity of AlbertaEdmontonCanada
  3. 3.National Research CouncilNational Institute for Nanotechnology (NINT)EdmontonCanada

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