IDEAS home Printed from https://ideas.repec.org/a/spr/jglopt/v76y2020i4d10.1007_s10898-019-00771-4.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10898-019-00771-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10898-019-00771-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. Si-Tong Lu & Miao Zhang & Qing-Na Li, 2020. "Feasibility and a fast algorithm for Euclidean distance matrix optimization with ordinal constraints," Computational Optimization and Applications, Springer, vol. 76(2), pages 535-569, June.
    3. Qian Zhang & Xinyuan Zhao & Chao Ding, 2021. "Matrix optimization based Euclidean embedding with outliers," Computational Optimization and Applications, Springer, vol. 79(2), pages 235-271, June.
    4. 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.
    5. Zha, Hongyuan & Zhang, Zhenyue, 2007. "Continuum Isomap for manifold learnings," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 184-200, September.
    6. J. Carroll, 1985. "Review," Psychometrika, Springer;The Psychometric Society, vol. 50(1), pages 133-140, March.
    7. Małgorzata Poniatowska-Jaksch, 2021. "Energy Consumption in Central and Eastern Europe (CEE) Households in the Platform Economics," Energies, MDPI, vol. 14(4), pages 1-22, February.
    8. Francis Caillez & Pascale Kuntz, 1996. "A contribution to the study of the metric and Euclidean structures of dissimilarities," Psychometrika, Springer;The Psychometric Society, vol. 61(2), pages 241-253, June.
    9. Malone, Samuel W. & Tarazaga, Pablo & Trosset, Michael W., 2002. "Better initial configurations for metric multidimensional scaling," Computational Statistics & Data Analysis, Elsevier, vol. 41(1), pages 143-156, November.
    10. Aurea Grané & Rosario Romera, 2018. "On Visualizing Mixed-Type Data," Sociological Methods & Research, , vol. 47(2), pages 207-239, March.
    11. Joseph Zinnes & David MacKay, 1983. "Probabilistic multidimensional scaling: Complete and incomplete data," Psychometrika, Springer;The Psychometric Society, vol. 48(1), pages 27-48, March.
    12. Samuel Messick, 1956. "An empirical evaluation of multidimensional successive intervals," Psychometrika, Springer;The Psychometric Society, vol. 21(4), pages 367-375, December.
    13. Michael W. Trosset, 2002. "Extensions of Classical Multidimensional Scaling via Variable Reduction," Computational Statistics, Springer, vol. 17(2), pages 147-163, July.
    14. Sheng-Shiung Wu & Sing-Jie Jong & Kai Hu & Jiann-Ming Wu, 2021. "Learning Neural Representations and Local Embedding for Nonlinear Dimensionality Reduction Mapping," Mathematics, MDPI, vol. 9(9), pages 1-18, April.
    15. Kuroda, Kaori & Hashiguchi, Hiroki & Fujiwara, Kantaro & Ikeguchi, Tohru, 2014. "Reconstruction of network structures from marked point processes using multi-dimensional scaling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 415(C), pages 194-204.
    16. François Bavaud, 2011. "On the Schoenberg Transformations in Data Analysis: Theory and Illustrations," Journal of Classification, Springer;The Classification Society, vol. 28(3), pages 297-314, October.
    17. W. Alan Nicewander & Joseph Lee Rodgers, 2022. "Obituary: Bruce McArthur Bloxom 1938–2020," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 1042-1044, September.
    18. Cornelius Fritz & Göran Kauermann, 2022. "On the interplay of regional mobility, social connectedness and the spread of COVID‐19 in Germany," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 400-424, January.
    19. Maarten M. Kampert & Jacqueline J. Meulman & Jerome H. Friedman, 2017. "rCOSA: A Software Package for Clustering Objects on Subsets of Attributes," Journal of Classification, Springer;The Classification Society, vol. 34(3), pages 514-547, October.
    20. Nathanaël Randriamihamison & Nathalie Vialaneix & Pierre Neuvial, 2021. "Applicability and Interpretability of Ward’s Hierarchical Agglomerative Clustering With or Without Contiguity Constraints," Journal of Classification, Springer;The Classification Society, vol. 38(2), pages 363-389, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jglopt:v:76:y:2020:i:4:d:10.1007_s10898-019-00771-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.