Software Approaches for Determination of 3-Dimensional Molecular Structures from Multi-Dimensional NMR

  • George C. Levy
  • Sophia Wang
  • Pankaj Kumar
  • Gwang-woo Jeong
  • Philip N. Borer
Chapter
Part of the NATO ASI Series book series (NSSA, volume 225)

Abstract

Two and higher dimensional NMR spectroscopies offer extraordinary power for detailed structure elucidation of proteins, nucleic acids and other important biomolecules. The methodology of elucidating biopolymer structures at atomic resolution from NMR spectroscopic data incorporates primarily NOESY experiments, but also may add spin-spin coupling constants and other information measured from COSY and other NMR experimentation. There are several important challenges that must be overcome in order for this methodology to be generally applicable to a broad range of biomolecules. One of the most important long-term goals of this research arena is to be able to determine structures with a confidence level sufficient to allow utilization of the structural information without confirmatory experimentation such as single X-ray structures.

In order to achieve this long-term goal, a number of issues must be dealt with: 1) primary 2D (nD) data reduction must incorporate techniques to allow accurate determination of sufficient NOESY cross-peak volumes; 2) computational schemes must be developed which not only determine refined molecular structures from the experimental information, but which also reflect confidence levels in the determined structures based on intelligent error analysis through all procedures; 3) corrections for these calculations must include, at a minimum, correction for dynamics variations, correction and recovery for missed spectral assignments, and wide sampling of possible molecular geometries.

Development of automated and assisted multi-dimensional NMR spectral assignment techniques is critical for many of these studies, where hundreds or even thousands of cross peaks may be significant for analysis. Techniques incorporating automated NOESY walks, pattern recognition for identification of specific sites, and other techniques will have to be used together for optimal spectral assignment. Of course 3- and higher dimensional spectroscopy will also assist in this area.

At Syracuse University, one of the primary goals realized at this time, is optimal preparation of the data for analysis. Use of non-linear processing techniques based on the maximum likelihood method (MLM) and specialized protocols increases the number of cross peaks that can be used for 3D structure determination. Experiments and computation underway indicates that these non-linear techniques have broad applicability and that, across a range of spectral conditions, they are robust and quantitative (or where dynamic range is too high, corrections may be possible to quantitate the smallest peaks). Preliminary results on synthetic and mixed data show superior quantification of cross-peak volumes over a range of peaks sizes exceeding 50:1.

A second area of investigation at Syracuse University involves utilization of parallel and distributed computing methods. These are initially being applied to two applications: 1) 3D NMR data processing and 2) using a genetic algorithm for NMR molecular modeling.

The basic idea is to utilize, in parallel, workstation and other computers coexistent on local and wide-area computer networks. In cases where specialized computing hardware such as MIMD parallel computers (examples: Alliant FX/80, Hypercubes, etc.) or SIMD architectures (example: Connection Machine) are available, an additional opportunity is present to dissect a computational application and allocate appropriate portions to that specialized hardware. This type of distribution of processing tasks is included in the work underway. Thus, on a computational network such as the one existing at Syracuse University which incorporates a large number of Sun work stations, IBM RISC System 6000’s, and a large configuration Connection Machine 2, as well as an Alliant FX/80, more than an order of magnitude speedup in realization of compute and I/O applications such as 3D NMR data processing and matrix manipulation found in aspects of NMR molecular modeling.

Keywords

Maximum Likelihood Method Cross Peak Dynamic Load Balance Spectral Assignment Load Balance Scheme 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    B. R. Frieden, J. Opt. Soc. Am. 62, 511 (1972).CrossRefPubMedGoogle Scholar
  2. 2.
    B. R. Frieden, in “Picture Processing and Digital Filtering”, T. S. Huang, ed., 177–248, Springer-Verlag, Berlin/New York (1975).Google Scholar
  3. 3.
    S. F. Gull and G. J. Daniell, Nature (London), 272, 686 (1978).CrossRefGoogle Scholar
  4. 4.
    R. K. Bryan, Ph.D. thesis, University of Cambridge (1981).Google Scholar
  5. 5.
    S. Sibisi, Nature 301, 134 (1983).CrossRefGoogle Scholar
  6. 6.
    J. Skilling, Nature 309, 748 (1984).CrossRefGoogle Scholar
  7. 7.
    S. Sibisi, J. Skilling, R G. Brereton, E. D. Laue, and J. Staunton, Nature 311, 446 (1984).CrossRefGoogle Scholar
  8. 8.
    J. Skilling and R. K. Bryan, Mon. Not. R. Astron. Soc. 211, 111 (1984).Google Scholar
  9. 9.
    J. C. Hoch, J. Magn. Reson. 64, 436 (1985).Google Scholar
  10. 10.
    E. D. Laue, J. Skilling, J. Staunton, S. Sibisi, and R. B. Brereton, J. Magn. Reson. 62, 437 (1985).Google Scholar
  11. 11.
    P. J. Hore, J. Magn. Reson. 62, 561 (1985).Google Scholar
  12. 12.
    J. F. Martin, J. Magn. Reson. 65, 291 (1985).Google Scholar
  13. 13.
    E. D. Laue, J. Skilling, R. B. Brereton, S. Sibisi, and J. Staunton, J. Magn. Reson. 62, 446 (1985).Google Scholar
  14. 14.
    E. D. Laue, M. R. Mayger, J. Skilling, and J. Staunton, J. Magn. Reson. 68, 14 (1986).Google Scholar
  15. 15.
    J. Capon, Proc. IEEE 57, 1408 (1969).CrossRefGoogle Scholar
  16. 16.
    F. Ni and H. A. Scheraga, QCPE Documentation, 573 (1988).Google Scholar
  17. 17.
    F. Ni and H. A. Scheraga, J. Magn. Reson. 82, 413–418 (1989).Google Scholar
  18. 18.
    R. E. Hoffman, A. Kumar, K. D. Bishop, P. N. Borer, and G. C. Levy, J. Magn. Reson. 83, 586–594 (1989).Google Scholar
  19. 19.
    R. H. Newman, J. Magn. Reson. 79, 448 (1988).Google Scholar
  20. 20.
    P. A. Jansson, R. H. Hunt, and E. K. Plyler, J. Opt. Soc. Am. 60, 596 (1970).CrossRefGoogle Scholar
  21. 21.
    W. E. Blass and G. W. Halsey, “Deconvolution of Absorption Spectra,” Academic Press, New York (1981).Google Scholar
  22. 22.
    P. A. Jansson, in “Deconvolution with Applications in Spectroscopy”, (P.A. Jansson, ed.), 96–132, Academic Press, New York/Orlando (1984).Google Scholar
  23. 23.
    G. W. Halsey and W. E. Blass, in “Deconvolution with Applications in Spectroscopy, (P.A. Jansson, ed.), 188–225, Academic Press, New York/Orlando (1984).Google Scholar
  24. 24.
    G. J. Thomas, Jr. and D. A. Agard, Biophys. J. 46, 763 (1984).CrossRefPubMedGoogle Scholar
  25. 25.
    B. P. Medoff, “Proceedings, IEEE International Conference on Acoust., Speech, Signal Processing”, Tampa, FL, 1073-1076 (1985).Google Scholar
  26. 26.
    B. P. Medoff, in “Image Recovery: Theory and Application”, H. Stark, ed., 321–368, Academic Press, New York/Orlando (1987).Google Scholar
  27. 27.
    F. Ni and H. A. Scheraga, J. RamanSpectrosc. 16, 337 (1985).CrossRefGoogle Scholar
  28. 28.
    F. Ni, G. C. Levy, and H. A. Scheraga, J. Magn. Reson. 66, 385 (1986).Google Scholar
  29. 29.
    A. R. Mazzeo and G. C. Levy, Comput. Enhanced Spectrosc. 3, 165, (1986).Google Scholar
  30. 30.
    A. A. Bothner-By and J. Dadok, J. Magn. Reson. 72, 540 (1987).Google Scholar
  31. 31.
    M. A. Delsuc and G. C. Levy, J. Magn. Reson. 76, 306 (1988).Google Scholar
  32. 32.
    A. R. Mazzeo, M. A. Delsuc, A. Kumar, and G. C. Levy, J. Magn. Reson. 81, 512–519 (1989).Google Scholar
  33. 33.
    H. Barkhuusen, R. De Beer, W. M. M. J. Bovée, and D. Van Ormondt, J. Magn. Reson. 61, 465 (1985).Google Scholar
  34. 34.
    J. Tang and J. R. Norris, J. Magn. Reson. 69, 180 (1986).Google Scholar
  35. 35.
    J. Tang and J. R. Norris, J. Chem. Phys. 84, 5210 (1986).CrossRefGoogle Scholar
  36. 36.
    A. E. Schussheim and D. Cowburn, J. Magn. Reson. 71, 371 (1987).Google Scholar
  37. 37.
    H. Gesmar and J. J. Led, “Spectral Estimation of Two-dimensional NMR Signals Applying Linear Prediction to Both Dimensions”, Thesis, Univ. of Copenhagen (1987).Google Scholar
  38. 38.
    F. Ni and H. A. Scheraga, J. Magn. Reson. 70, 506 (1987).Google Scholar
  39. 39.
    M. A. Delsuc, F. Ni, and G. C. Levy, J. Magn. Reson. 73, 548 (1987).Google Scholar
  40. 40.
    G. L. Bretthorst, “Bayesian Spectrum Analysis and Parameter Estimation”, Ph.D. thesis, Department of Physics, Washington University, St. Louis, Missouri, August (1987).Google Scholar
  41. 41.
    E. T. Jaynes, in “Maximum-Energy and Bayesian Spectral Analysis and Estimation Problems”, C. R. Smith and G. J. Erickson, eds., p. 1, Reidel, Dordecht, Holland (1987).CrossRefGoogle Scholar
  42. 42.
    G. L. Bretthorst, C. C. Hung, D. A. D’Avignon, and J. J. H. Ackerman, J. Magn. Reson. 79, 369–376 (1988).Google Scholar
  43. 43.
    J. H. Holland, K. J. Holyoad, R. E. Nisbett, and P. R. Thagard, Induction: Process of Inference, Learning and Discovery The MIT Press (1986).Google Scholar

Copyright information

© Springer Science+Business Media New York 1991

Authors and Affiliations

  • George C. Levy
    • 1
  • Sophia Wang
    • 1
  • Pankaj Kumar
    • 1
  • Gwang-woo Jeong
    • 1
  • Philip N. Borer
    • 1
  1. 1.NMR and Data Processing LaboratorySyracuse UniversitySyracuseUSA

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