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A Euclidean distance matrix model for protein molecular conformation

Author

Listed:
  • Fengzhen Zhai

    (Beijing Institute of Technology)

  • Qingna Li

    (Beijing Institute of Technology)

Abstract

Protein molecular conformation is an important and challenging problem in biophysics. It is to recover the structure of proteins based on limited information such as noised distances, lower and upper bounds on some distances between atoms. In this paper, based on the recent progress in numerical algorithms for Euclidean distance matrix (EDM) optimization problems, we propose a EDM model for protein molecular conformation. We reformulate the problem as a rank-constrained least squares problem with linear equality constraints, box constraints, as well as a cone constraint. Due to the nonconvexity of the problem, we develop a majorized penalty approach to solve the problem. We apply the accelerated block coordinate descent algorithm proposed in Sun et al. (SIAM J Optim 26(2):1072–1100, 2016) to solve the resulting subproblem. Extensive numerical results demonstrate the efficiency of the proposed model.

Suggested Citation

  • Fengzhen Zhai & Qingna Li, 2020. "A Euclidean distance matrix model for protein molecular conformation," Journal of Global Optimization, Springer, vol. 76(4), pages 709-728, April.
    • Handle: RePEc:spr:jglopt:v:76:y:2020:i:4:d:10.1007_s10898-019-00771-4
    DOI: 10.1007/s10898-019-00771-4
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    References listed on IDEAS

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    1. Panpan Yu & Qingna Li, 2018. "Ordinal Distance Metric Learning with MDS for Image Ranking," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 35(01), pages 1-19, February.
    2. Chao Ding & Hou-Duo Qi, 2017. "Convex Euclidean distance embedding for collaborative position localization with NLOS mitigation," Computational Optimization and Applications, Springer, vol. 66(1), pages 187-218, January.
    3. Gale Young & A. Householder, 1938. "Discussion of a set of points in terms of their mutual distances," Psychometrika, Springer;The Psychometric Society, vol. 3(1), pages 19-22, March.
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