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Can We Determine a Protein Structure Quickly?

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Abstract

Can we determine a high resolution protein structure quickly, say, in a week? I will show this is possible by the current technologies together with new computational tools discussed in this article. We have three potential paths to explore:

  • X-ray crystallography. While this method has produced the most protein structures in the PDB (Protein Data Bank), the nasty trial-and-error crystallization step remains to be an inhibitive obstacle.

  • NMR (Nuclear Magnetic Resonance) spectroscopy. While the NMR experiments are relatively easy to do, the interpretation of the NMR data for structure calculation takes several months on average.

  • In silico protein structure prediction. Can we actually predict high resolution structures consistently? If the predicted models remain to be labeled as “predicted”, and these structures still need to be experimentally verified by the wet lab methods, then this method at best can serve only as a screening tool.

I investigate the question of “quick protein structure Determination” from a computer scientist point of view and actually answer the more relevant question “what can a computer scientist effectively contribute to this goal”.

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References

  1. Wooley J, Ye Y. A Historical Perspective and Overview of Protein Structure Prediction. Computational Methods for Protein Structure Prediction and Modeling, Xu Y et al. (eds.), Springer, 2007, pp.1–44.

  2. Hiraki M et al. Development of an automated large-scale protein-crystallization and monitoring system for high-throughput protein-structure analyses. Acta Crystallogr. D. Biol. Crystallogr., 2006, 62(9): 1058–1065.

    Article  Google Scholar 

  3. Chandonia J M, Brenner S E. The impact of structural genomics: Expectations and outcomes. Science, Jan. 20, 2006, 311(5759): 347–351.

    Article  Google Scholar 

  4. Hamelryck T, Kent J T, Krogh A. Sampling realistic protein conformations using local structural bias. PLoS Comput. Biol., 2006, 2(9): e131.

    Article  Google Scholar 

  5. Kim D, Xu D, Guo J, Ellrott K, Xu Y. PROSPECT II: Protein structure prediction program for genome-scale applications. Protein Eng., 2003, 16(9): 641–650.

    Article  Google Scholar 

  6. Bradley P, Misura K M S, Baker D. Toward high-resolution de novo structure prediction for small proteins. Science, 2005, 309(5742): 1868–1871.

    Article  Google Scholar 

  7. Zhang Y, Arakaki A, Skolnick J. TASSER: An automated method for the prediction of protein tertiary structures in CASP6. Proteins, 2005, 61(S7): 91–98.

    Article  Google Scholar 

  8. Zhang Y. Template-based modeling and free modeling by I-TASSER in CASP7. Proteins, 2007, 69(Suppl. 8): 108–117.

    Article  Google Scholar 

  9. Xu J, Li M, Kim D, Xu Y. RAPTOR: Optimal protein threading by linear programming. Journal of Bioinformatics and Computational Biology, 2003, 1(1): 95–117.

    Article  Google Scholar 

  10. Zhang J, Wang Q, Barz B, He Z, Kosztin I, Shang Y, Xu D. MUFOLD: A new solution for protein 3D structure prediction. DOI: 10.1002/prot.22634, Proteins: Structure, Function and Bioinformatics, 2009, DOI:10.1002/prot.22634.

  11. Li S C, Bu D, Xu J, Li M. Fragment-HMM: A new approach to protein structure prediction. Protein Science, 2008, 17: 1925–1934.

    Article  Google Scholar 

  12. Li S C. New approaches to protein structure prediction [Ph.D. Dissertation]. University of Waterloo, Waterloo, Canada, 2009.

  13. Li S C, Bu D B, Li M. ONION: Quality assessment of ab initio decoys. Manuscript, 2009.

  14. Kurt Wüthrich. NMR of Proteins and Nucleic Acids. John Wiley & Sons, 1986.

  15. Güntert P. Automated structure determination from NMR spectra. European Biophysics Journal, 2009, 38(2): 129–143.

    Article  Google Scholar 

  16. Williamson M P, Craven C J. Automated protein structure calculation from NMR data. Journal of Biomolecular NMR, 2009, 43(3): 131–143.

    Article  Google Scholar 

  17. Alipanahi B, Gao X, Karakoc E, Li S C, Bu D, Feng G, Donaldson L, Li M. An automated protocol for NMR protein structure determination, Manuscript, 2009.

  18. Koradi R, Billeter M, Engeli M, Güntert P, Wüthrich K. Automated peak picking and peak integration in macromolecular NMR spectra using AUTOPSY. Journal of Magnetic Resonance, 1998, 135(2): 288–297.

    Article  Google Scholar 

  19. Altieri A S, Byrd R A. Automation of NMR structure determination of proteins. Current Opinion in Structural Biology, 2004, 14(5): 547–553.

    Article  Google Scholar 

  20. Corne S A, Johnson P. An artificial neural network for classifying cross peaks in two-dimensional NMR spectra. Journal of Magnetic Resonance, 1992, 100(2): 256–266.

    Google Scholar 

  21. Carrara E A, Pagliari F, Nicolini C. Neural networks for the peak-picking of nuclear magnetic resonance spectra. Neural Networks, 1993, 6(7): 1023–1032.

    Article  Google Scholar 

  22. Rouh A, Louis-Joseph A, Lallemand J Y. Bayesian signal extraction from noisy FT NMR spectra. Journal of Biomolecular NMR, 1994, 4(4): 505–518.

    Article  Google Scholar 

  23. Antz C, Neidig K P, Kalbitzer H R. A general Bayesian method for an automated signal class recognition in 2D NMR spectra combined with a multivariate discriminant analysis. Journal of Biomolecular NMR, 1995, 5(3): 287–296.

    Article  Google Scholar 

  24. Orekhov V Y, Ibraghimov I V, Billeter M. MUNIN: A new approach to multi-dimensional NMR spectra interpretation. Journal of Biomolecular NMR, 2001, 20(1): 49–60.

    Article  Google Scholar 

  25. Korzhnev D M, Ibraghimov I V, Billeter M, Orekhov V Y. MUNIN: Application of three-way decomposition to the analysis of heteronuclear NMR relaxation data. Journal of Biomolecular NMR, 2001, 21(3): 263–268.

    Article  Google Scholar 

  26. Kleywegt G, Boelens R, Kaptein R. A versatile approach toward the partially automatic recognition of cross peaks in 2D 1 H NMR spectra. Journal of Magnetic Resonance, 1990, 88(3): 601–608.

    Google Scholar 

  27. Garret D S, Powers R, Gronenborn A M, Clore G M. A common sense approach to peak picking in two-, three-, and four-dimensional spectra using automatic computer analysis of contour diagrams. Journal of Magnetic Resonance, 1991, 95: 214–220.

    Google Scholar 

  28. Johnson B A, Blevins R A. MR view: A computer program for the visualization and analysis of NMR data. Journal of Biomolecular NMR, 1994, 4(5): 603–614.

    Article  Google Scholar 

  29. Herrmann T, Güntert P, Wüthrich K. Protein NMR structure determination with automated NOE-identification in the NOESY spectra using the new software ATNOS. Journal of Biomolecular NMR, 2002, 24(3): 171–189.

    Article  Google Scholar 

  30. Goddard T D, Kneller D G. SPARKY 3. University of California, San Francisco, USA, 2008.

    Google Scholar 

  31. Alipanahi B, Gao X, Karakoc E, Donaldson L, Li M. PICKY: A novel SVD-based NMR spectra peak picking method. Bioinformatics, 2009, 25(12): i268–i275.

    Article  Google Scholar 

  32. Bartels C, Billeter M, Güntert P, Wüthrich K. Automated sequence-specific NMR assignment of homologous proteins using the program GARANT. Journal of Biomolecular NMR, 1996, 7(3):207–213.

    Article  Google Scholar 

  33. Zimmerman D E, Kulikowski C A, Huang Y, Feng W, Tashiro M, Shimotakahara S, Chien C, Powers R, Montelione G T. Automated analysis of protein NMR assignments using methods from artificial intelligence. Journal of Molecular Biology, 1997, 269(4): 592–610.

    Article  Google Scholar 

  34. Gronwald W, Willard L, Jellard T, Boyko R F, Rajarathnam K, Wishart D S, Sönnichsen F D, Sykes B D. Camra: Chemical shift based computer aided protein NMR assignments. Journal of Biomolecular NMR, 1998, 12(3): 395–405.

    Article  Google Scholar 

  35. Bailey-Kellogg C, Widge A, Kelly J, Brushweller J, Donald B R. The NOESY jigsaw: Automated protein secondary structure and main-chain assignment from sparse, unassigned NMR data. Journal of Computational Biology, 2000, 7(3/4): 537–558.

    Article  Google Scholar 

  36. Güntert P, Salzmann M, Braun D, Wüthrich K. Sequence-specific NMR assignment of proteins by global fragment mapping with the program MAPPER. Journal of Biomolecular NMR, 2000, 18(2): 129–137.

    Article  Google Scholar 

  37. Hus J C, Prompers J, Brüschweiler R. Assignment strategy for proteins with known structure. Journal of Magnetic Resonance, 2002, 157(1): 119–123.

    Article  Google Scholar 

  38. Erdmann M A, Rule G S. Rapid protein structure detection and assignment using residual dipolar couplings. Technical Report CMU-CS-02-195, School of Computer Science, Carnegie Mellon University, Pittsburgh, USA, 2002.

  39. Pristovsek P, Rüterjans H, Jerala R. Semiautomatic sequence-specific assignment of proteins based on the tertiary structure — The program st2nmr. Journal of Computational Chemistry, 2002, 23(3): 335–340.

    Article  Google Scholar 

  40. Coggins B, Zhou P. PACES: Protein sequential assignment by computer-assisted exhaustive search. Journal of Biomolecular NMR, 2003, 26(2): 93–111.

    Article  Google Scholar 

  41. Jung Y, Zweckstetter M. Mars — Robust automatic backbone assignment of proteins. Journal of Biomolecular NMR, 2004, 30(1): 11–23.

    Article  Google Scholar 

  42. Langmead C J, Donald B R. An expectation/maximization nuclear vector replacement algorithm for automated NMR resonance assignments. Journal of Biomolecular NMR, 2004, 29(2): 111–138.

    Article  Google Scholar 

  43. Langmead C J, Yan A, Lilien R, Wang L, Donald B R. A polynomial-time nuclear vector replacement algorithm for automated NMR resonance assignment. Journal of Computational Biology, 2004, 11(2/3): 277–298.

    Article  Google Scholar 

  44. Masse J E, Keller R. Autolink: Automated sequential resonance assignment of biopolymers from NMR data by relative-hypothesis-prioritization-based simulated logic. Journal of Magnetic Resonance, 2005, 174: 133–151.

    Article  Google Scholar 

  45. Pristovsek P, Franzoni L. Stereospecific assignments of protein NMR resonances based on the tertiary structure and 2D/3D NOE data. Journal of Computational Chemistry, 2004, 27(6): 791–797.

    Article  Google Scholar 

  46. Wu K, Chang J, Chen J, Chang C, Wu W, Huang T, Sung T, Hsu W. RIBRA: An error-tolerant algorithm for the NMR backbone assignment problem. Journal of Computational Biology, 2006, 13(2): 229–244.

    Article  MathSciNet  Google Scholar 

  47. Wan X, Lin G. CISA: Combined NMR resonance connectivity information determination and sequential assignment. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2007, 4(3): 336–348.

    Article  Google Scholar 

  48. Lemak A, Steren C A, Arrowsmith C H, Llinás, M. Sequence specific resonance assignment via Multicanonical Monte Carlo search using an ABACUS approach. Journal of Biomolecular NMR, 2008, 41(1): 29–41.

    Article  Google Scholar 

  49. Volk J, Herrmann T, Wüthrich K. Automated sequence-specific protein NMR assignment using the memetic algorithm MATCH. Journal of Biomolecular NMR, 2008, 41(3): 127–138.

    Article  Google Scholar 

  50. Xiong F, Bailey-Kellogg C. A hierarchical grow-and-match algorithm for backbone resonance assignments given 3D structure. In Proc. The 7th IEEE International Conference on Bioinformatics and Bioengineering, Boston, MA, Oct. 14–17, 2007, pp.403–410.

  51. Xiong F, Pandurangan G, Bailey-KelloggC. Contact replacement for NMR resonance assignment. Bioinformatics, 2008, 24(13): i205–i213.

    Google Scholar 

  52. Fiorito F, Herrmann T, Damberger F F, Wüthrich K. Automated amino acid side-chain NMR assignment of proteins using 13 C and 15 N-resolved 3D [1 H, 1 H]-NOESY. Journal of Biomolecular NMR, 2008, 42(1): 23–33.

    Article  Google Scholar 

  53. Apaydin M S, Conitzer V, Donald B R. Structure-based protein NMR assignments using native structural ensembles. Journal of Biomolecular NMR, 2008, 40(4): 263–276.

    Article  Google Scholar 

  54. Stratmann D, Heijenoort C, Guittet E. NOEnet — Use of NOE networks for NMR resonance assignment of proteins with known 3D structure. Bioinformatics, 2009, 25(4): 474–481.

    Article  Google Scholar 

  55. Alipanahi B, Gao X, Karakoc E, Balbach F, Donaldson L, Arrowsmith C, Li M. IPASS: Error tolerant NMR backbone resonance assignment by linear programming. Technical Report, No. CS-2009-16, 2009, University of Waterloo, http://www.cs.uwaterloo.ca/research/tr/2009/.

  56. Seavey B R, Farr E A, Westler W M, Markley J. A relational database for sequence-specific protein NMR data. Journal of Biomolecular NMR, 1991, 1(3): 217–236.

    Article  Google Scholar 

  57. Li S C, Bu D, Gao X, Xu J, Li M. Designing succinct structural alphabets. Bioinformatics, 2008, 24(13): i182–i189.

    Article  Google Scholar 

  58. Shen Y, Lange O, Delaglio F, Rossi P, Aramini J M, Liu G, Eletsky A, Wu B, Singarapu K K, Lemak A, Ignatchenko A, Arrowsmith C, Szyperski T, Montelione G T, Baker D, Bax A. Consistent blind protein structure generation from NMR chemical shift data. Proc. the National Academy of Sciences, 2008, 105(12): 4685–4690.

    Article  Google Scholar 

  59. Gao X. Towards automating protein structure determination from NMR data [Ph.D. Dissertation]. University of Waterloo, Waterloo, Canada, 2009.

  60. Jang R, Gao X, Li M. Towards automated structure-based NMR assignment. Manuscript, 2009.

  61. Zhao Y, Alipanahi B, Li S C, Li M. Protein secondary structure prediction using NMR chemical shift data. Manuscript, 2009.

  62. Mobli M, Maciejewski M W, Gryk M R, Hoch J C. Au automated tool for maximum entropy reconstruction of biomolecular NMR spectra. Nature Methods, 2007, 4(6): 467–468.

    Article  Google Scholar 

  63. Maciejewski M W, Qui H Z, Rujan I, Mobli M, Hoch J C. Nonuniform sampling and spectral aliasing. Journal of Magnetic Resonance, 2009, 199(1): 88–93.

    Article  Google Scholar 

  64. Xu R, Ayers B, Cowburn D, Muir T W. Chemical ligation of folded recombinant proteins: Segmental isotopic labeling of domains for NMR studies. Proc. Natl. Acad. Sci. USA, 1999, 96(2): 388–393.

    Article  Google Scholar 

  65. Yu H. Extending the size limit of protein nuclear magnetic resonance. Proc. Natl. Acad. Sci. USA, 1999, 96(2): 332–334.

    Article  Google Scholar 

  66. Ozawa K, Wu P S C, Dixon N E, Otting G. 15N-labelled proteins by cell-free protein synthesis — Strategies for high-throughput NMR studies of proteins and protein-ligand complexes. The FEBS Journal, 2006, 273(18): 4154–4159.

    Article  Google Scholar 

  67. Torizawa T, Ono A M, Terauchi T, Kainosho M. NMR assignment methods for the aromatic ring resonances of phenylalanine and tyrosine residues in proteins. J. Am. Chem. Soc., 2005, 127(36): 12620–12626.

    Article  Google Scholar 

  68. Kainosho M, Trizawa T, Ono A M, Guntert P. Optimal isotope labelling for NMR protein structure determination. Nature, 2006, 440: 52–57.

    Article  Google Scholar 

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Correspondence to Ming Li.

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This work was partially supported by the National High Tech Research and Development 863 Program under Grant No. 2008AA02Z313 from China’s Ministry of Science and Technology, Canada’s NSERC under Grant No. OGP0046506, Canada Research Chair Program, an NSERC Collaborative Grant, and Ontario’s Premier’s Discovery Award.

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Li, M. Can We Determine a Protein Structure Quickly?. J. Comput. Sci. Technol. 25, 95–106 (2010). https://doi.org/10.1007/s11390-010-9308-2

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