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NMR assignment through linear programming

Author

Listed:
  • José F. S. Bravo-Ferreira

    (PACM, Princeton University)

  • David Cowburn

    (Albert Einstein College of Medicine
    Albert Einstein College of Medicine)

  • Yuehaw Khoo

    (University of Chicago)

  • Amit Singer

    (Princeton University)

Abstract

Nuclear Magnetic Resonance (NMR) Spectroscopy is the second most used technique (after X-ray crystallography) for structural determination of proteins. A computational challenge in this technique involves solving a discrete optimization problem that assigns the resonance frequency to each atom in the protein. This paper introduces LIAN (LInear programming Assignment for NMR), a novel linear programming formulation of the problem which yields state-of-the-art results in simulated and experimental datasets.

Suggested Citation

  • José F. S. Bravo-Ferreira & David Cowburn & Yuehaw Khoo & Amit Singer, 2022. "NMR assignment through linear programming," Journal of Global Optimization, Springer, vol. 83(1), pages 3-28, May.
    • Handle: RePEc:spr:jglopt:v:83:y:2022:i:1:d:10.1007_s10898-021-01004-3
    DOI: 10.1007/s10898-021-01004-3
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    References listed on IDEAS

    as
    1. Arash Bahrami & Amir H Assadi & John L Markley & Hamid R Eghbalnia, 2009. "Probabilistic Interaction Network of Evidence Algorithm and its Application to Complete Labeling of Peak Lists from Protein NMR Spectroscopy," PLOS Computational Biology, Public Library of Science, vol. 5(3), pages 1-15, March.
    2. José F. S. Bravo Ferreira & Yuehaw Khoo & Amit Singer, 2018. "Semidefinite programming approach for the quadratic assignment problem with a sparse graph," Computational Optimization and Applications, Springer, vol. 69(3), pages 677-712, April.
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