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Optimal Protein Structure Alignment Using Maximum Cliques

Author

Listed:
  • Dawn M. Strickland

    (Department of Mathematics, Winthrop University, Rock Hill, South Carolina 29733)

  • Earl Barnes

    (School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332)

  • Joel S. Sokol

    (School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332)

Abstract

In biology, the protein structure alignment problem answers the question of how similar two proteins are. Proteins with strong physical similarities in their tertiary (folded) structure often have similar functions, so understanding physical similarity could be a key to developing protein-based medical treatments. One of the models for protein structure alignment is the maximum contact map overlap (CMO) model. The CMO model of protein structure alignment can be cast as a maximum clique problem on an appropriately defined graph. We exploit properties of these protein-based maximum clique problems to develop specialized preprocessing techniques and show how they can be used to more quickly solve contact map overlap instances to optimality.

Suggested Citation

  • Dawn M. Strickland & Earl Barnes & Joel S. Sokol, 2005. "Optimal Protein Structure Alignment Using Maximum Cliques," Operations Research, INFORMS, vol. 53(3), pages 389-402, June.
    • Handle: RePEc:inm:oropre:v:53:y:2005:i:3:p:389-402
    DOI: 10.1287/opre.1040.0189
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    Citations

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    Cited by:

    1. Chu-Min Li & Zhiwen Fang & Hua Jiang & Ke Xu, 2018. "Incremental Upper Bound for the Maximum Clique Problem," INFORMS Journal on Computing, INFORMS, vol. 30(1), pages 137-153, February.
    2. Peter Brown & Yuedong Yang & Yaoqi Zhou & Wayne Pullan, 2017. "A heuristic for the time constrained asymmetric linear sum assignment problem," Journal of Combinatorial Optimization, Springer, vol. 33(2), pages 551-566, February.
    3. Steffen Rebennack & Marcus Oswald & Dirk Oliver Theis & Hanna Seitz & Gerhard Reinelt & Panos M. Pardalos, 2011. "A Branch and Cut solver for the maximum stable set problem," Journal of Combinatorial Optimization, Springer, vol. 21(4), pages 434-457, May.
    4. Fan Zhou & Kunpeng Zhang & Shuying Xie & Xucheng Luo, 2020. "Learning to Correlate Accounts Across Online Social Networks: An Embedding-Based Approach," INFORMS Journal on Computing, INFORMS, vol. 32(3), pages 714-729, July.
    5. Constantine N. Goulimis, 2007. "ASP, The Art and Science of Practice: Appeal to NP-Completeness Considered Harmful: Does the Fact That a Problem Is NP-Complete Tell Us Anything?," Interfaces, INFORMS, vol. 37(6), pages 584-586, December.
    6. Douglas Altner & Özlem Ergun, 2011. "Rapidly computing robust minimum capacity s-t cuts: a case study in solving a sequence of maximum flow problems," Annals of Operations Research, Springer, vol. 184(1), pages 3-26, April.
    7. Piotr Łukasiak & Jacek Błażewicz & Maciej Miłostan, 2010. "Some operations research methods for analyzing protein sequences and structures," Annals of Operations Research, Springer, vol. 175(1), pages 9-35, March.
    8. Pattillo, Jeffrey & Youssef, Nataly & Butenko, Sergiy, 2013. "On clique relaxation models in network analysis," European Journal of Operational Research, Elsevier, vol. 226(1), pages 9-18.

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