Journal of Biomolecular NMR

, Volume 57, Issue 3, pp 281–296 | Cite as

Resonance assignment of the NMR spectra of disordered proteins using a multi-objective non-dominated sorting genetic algorithm

  • Yu Yang
  • Keith J. Fritzsching
  • Mei Hong


A multi-objective genetic algorithm is introduced to predict the assignment of protein solid-state NMR (SSNMR) spectra with partial resonance overlap and missing peaks due to broad linewidths, molecular motion, and low sensitivity. This non-dominated sorting genetic algorithm II (NSGA-II) aims to identify all possible assignments that are consistent with the spectra and to compare the relative merit of these assignments. Our approach is modeled after the recently introduced Monte-Carlo simulated-annealing (MC/SA) protocol, with the key difference that NSGA-II simultaneously optimizes multiple assignment objectives instead of searching for possible assignments based on a single composite score. The multiple objectives include maximizing the number of consistently assigned peaks between multiple spectra (“good connections”), maximizing the number of used peaks, minimizing the number of inconsistently assigned peaks between spectra (“bad connections”), and minimizing the number of assigned peaks that have no matching peaks in the other spectra (“edges”). Using six SSNMR protein chemical shift datasets with varying levels of imperfection that was introduced by peak deletion, random chemical shift changes, and manual peak picking of spectra with moderately broad linewidths, we show that the NSGA-II algorithm produces a large number of valid and good assignments rapidly. For high-quality chemical shift peak lists, NSGA-II and MC/SA perform similarly well. However, when the peak lists contain many missing peaks that are uncorrelated between different spectra and have chemical shift deviations between spectra, the modified NSGA-II produces a larger number of valid solutions than MC/SA, and is more effective at distinguishing good from mediocre assignments by avoiding the hazard of suboptimal weighting factors for the various objectives. These two advantages, namely diversity and better evaluation, lead to a higher probability of predicting the correct assignment for a larger number of residues. On the other hand, when there are multiple equally good assignments that are significantly different from each other, the modified NSGA-II is less efficient than MC/SA in finding all the solutions. This problem is solved by a combined NSGA-II/MC algorithm, which appears to have the advantages of both NSGA-II and MC/SA. This combination algorithm is robust for the three most difficult chemical shift datasets examined here and is expected to give the highest-quality de novo assignment of challenging protein NMR spectra.


Sequential resonance assignment Protein structure determination Solid-state NMR Magic-angle spinning Linewidths 



This work is supported by NIH Grants GM088204 and GM066976. We thank Dr. Robert Tycko for helpful discussions and for making the MCASSIGN2 program available, and Myungwoon Lee for help in the initial phase of this work.

Supplementary material

10858_2013_9788_MOESM1_ESM.pdf (1.4 mb)
Supplementary material 1 (PDF 1426 kb)


  1. Baran MC, Huang YJ, Moseley HNB, Montelione GT (2004) Automated analysis of protein NMR assignments and structures. Chem Rev 104:3541–3555CrossRefGoogle Scholar
  2. Bartels C, Billeter M, Guntert P, Wuthrich K (1996) Automated sequence-specific NMR assignment of homologous proteins using the program GARANT. J Biomol NMR 7:207–213CrossRefGoogle Scholar
  3. Bertini I, Bhaumik A, De Paëpe G, Griffin RG, Lelli M, Lewandowski JR, Luchinat C (2010) High-resolution solid-state NMR structure of a 17.6 kDa protein. J Am Chem Soc 132:1032–1040CrossRefGoogle Scholar
  4. Böckmann A, Lange A, Galinier A, Luca S, Giraud N, Juy M, Heise H, Montserret R, Penin F, Baldus M (2003) Solid state NMR sequential resonance assignments and conformational analysis of the 2 × 10.4 kDa dimeric form of the Bacillus subtilis protein Crh. J Biomol NMR 27:323–339CrossRefGoogle Scholar
  5. Buchler NEG, Zuiderweg ERP, Wang H, Goldstein RA (1997) Protein heteronuclear NMR assignments using mean-field simulated annealing. J Magn Reson 125:34–42CrossRefADSGoogle Scholar
  6. Castellani F, vanRossum B, Diehl A, Schubert M, Rehbein K, Oschkinat H (2002) Structure of a protein determined by solid-state magic-angle spinning NMR spectroscopy. Nature 420:98–102CrossRefADSGoogle Scholar
  7. Coggins BE, Zhou P (2003) PACES: protein sequential assignment by computer-assisted exhaustive search. J Biomol NMR 26:93–111CrossRefGoogle Scholar
  8. Comellas G, Rienstra CM (2013) Protein structure determination by magic-angle spinning solid-state NMR, and insights into the formation, structure, and stability of amyloid fibrils. Annu Rev Biophys 42:515–536CrossRefGoogle Scholar
  9. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6:182–197CrossRefGoogle Scholar
  10. Franks WT, Zhou DH, Wylie BJ, Money BG, Graesser DT, Frericks HL, Sahota G, Rienstra CM (2005) Magic-angle spinning solid-state NMR spectroscopy of the beta1 immunoglobulin binding domain of protein G (GB1): 15N and 13C chemical shift assignments and conformational analysis. J Am Chem Soc 127:12291–12305CrossRefGoogle Scholar
  11. Franks WT, Wylie BJ, Schmidt HL, Nieuwkoop AJ, Mayrhofer RM, Shah GJ, Graesser DT, Rienstra CM (2008) Dipole tensor-based atomic-resolution structure determination of a nanocrystalline protein by solid-state NMR. Proc Natl Acad Sci USA 105:4621–4626CrossRefADSGoogle Scholar
  12. Fritzsching KJ, Yang Y, Schmidt-Rohr K, Hong M (2013) Practical use of chemical shift databases for protein solid-state NMR: 2D chemical shift maps and amino-acid assignment with secondary-structure information. J Biomol NMR 56:155–167CrossRefGoogle Scholar
  13. Hong M, Zhang Y, Hu F (2012) Membrane protein structure and dynamics from NMR spectroscopy. Annu Rev Phys Chem 63:1–24CrossRefADSGoogle Scholar
  14. Hu KN, McGlinchey RP, Wickner RB, Tycko R (2011a) Segmental polymorphism in a functional amyloid. Biophys J 101:2242–2250CrossRefGoogle Scholar
  15. Hu KN, Qiang W, Tycko R (2011b) A general Monte Carlo/simulated annealing algorithm for resonance assignment in NMR of uniformly labeled biopolymers. J Biomol NMR 50:267–276CrossRefGoogle Scholar
  16. 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
  17. Igumenova TI, McDermott AE, Zilm KW, Martin RW, Paulson EK, Wand AJ (2004) Assignments of carbon NMR resonances for microcrystalline ubiquitin. J Am Chem Soc 126:6720–6727CrossRefGoogle Scholar
  18. Knowles JD, Corne DW (2000) Approximating the nondominated front using the Pareto archived evolution strategy. Evol Comput 8:149–172CrossRefGoogle Scholar
  19. Lee W, Yu W, Kim S, Chang I, Lee W, Markley JL (2012) PACSY, a relational database management system for protein structure and chemical shift analysis. J Biomol NMR 54:169–179CrossRefGoogle Scholar
  20. Leutner M, Gschwind RM, Liermann J, Schwarz C, Gemmecker G, Kessler H (1998) Automated backbone assignment of labeled proteins using the threshold accepting algorithm. J Biomol NMR 11:31–43CrossRefGoogle Scholar
  21. Li Y, Berthold DA, Gennis RB, Rienstra CM (2008) Chemical shift assignment of the transmembrane helices of DsbB, a 20-kDa integral membrane enzyme, by 3D magic-angle spinning NMR spectroscopy. Protein Sci 17:199–204CrossRefGoogle Scholar
  22. Li S, Zhang Y, Hong M (2010) 3D 13C–13C–13C correlation NMR for de novo distance determination of solid proteins and application to a human alpha defensin. J Magn Reson 202:203–210CrossRefADSGoogle Scholar
  23. Loquet A, Sgourakis NG, Gupta R, Giller K, Riedel D, Goosmann C, Griesinger C, Kolbe M, Baker D, Becker S, Lange A (2012) Atomic model of the type III secretion system needle. Nature 486:276–279ADSGoogle Scholar
  24. Luca S, Heise H, Baldus M (2003) High-resolution solid-state NMR applied to polypeptides and membrane proteins. Acc Chem Res 36:858–865CrossRefGoogle Scholar
  25. McDermott AE (2009) Structure and dynamics of membrane proteins by magic angle spinning solid-state NMR. Annu Rev Biophys 38:385–403MathSciNetCrossRefGoogle Scholar
  26. 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
  27. Olson JB, Markley JL (1994) Evaluation of an algorithm for the automated sequential assignment of protein backbone resonances: a demonstration of the connectivity tracing assignment tools (CONTRAST) software package. J Biomol NMR 4:385–410CrossRefGoogle Scholar
  28. Schmidt E, Guntert P (2012) A new algorithm for reliable and general NMR resonance assignment. J Am Chem Soc 134:12817–12829CrossRefGoogle Scholar
  29. Shi L, Ahmed MA, Zhang W, Whited G, Brown LS, Ladizhansky V (2009) Three-dimensional solid-state NMR study of a seven-helical integral membrane proton pump—structural insights. J Mol Biol 386:1078–1093CrossRefGoogle Scholar
  30. Shi L, Kawamura I, Jung KH, Brown LS, Ladizhansky V (2011) Conformation of a seven-helical transmembrane photosensor in the lipid environment. Angew Chem Int Ed Engl 50:1302–1305CrossRefGoogle Scholar
  31. Tycko R (2011) Solid-state NMR studies of amyloid fibril structure. Annu Rev Phys Chem 62:279–299CrossRefADSGoogle Scholar
  32. Tycko R, Hu KN (2010) A Monte Carlo/simulated annealing algorithm for sequential resonance assignment in solid state NMR of uniformly labeled proteins with magic-angle spinning. J Magn Reson 205:304–314CrossRefADSGoogle Scholar
  33. Wasmer C, Lange A, Van Melckebeke H, Siemer AB, Riek R, Meier BH (2008) Amyloid fibrils of the HET-s(218–289) prion form a beta solenoid with a triangular hydrophobic core. Science 319:1523–1526CrossRefADSGoogle Scholar
  34. Zhang Y, Doherty T, Li J, Lu W, Barinka C, Lubkowski J, Hong M (2010) Resonance assignment and three-dimensional structure determination of a human alpha-defensin, HNP-1, by solid-state NMR. J Mol Biol 397:408–422CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Department of ChemistryIowa State UniversityAmesUSA

Personalised recommendations