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Protein structure optimization by side-chain positioning via beta-complex

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  • Joonghyun Ryu
  • Deok-Soo Kim

Abstract

A molecular structure determines a molecular function(s) and a correct understanding of molecular structure is important for biotechnology. The computational prediction of molecular structure is a frequent requirement for important biomolecular applications such as a homology modeling, a docking simulation, a protein design, etc. where the optimization of molecular structure is fundamental. One of the core problems in the optimization of protein structure is the optimization of side-chains called the side-chain positioning problem. The side-chain positioning problem, assuming the rigidity of backbone and a rotamer library, attempts to optimally assign a rotamer to each residue so that the potential energy of protein is minimized in its entirety. The optimal solution approach using (mixed) integer linear programming, with the dead-end elimination technique, suffers even for moderate-sized proteins because the side-chain positioning problem is NP-hard. On the other hand, popular heuristic approaches focusing on speed produce solutions of low quality. This paper presents an efficient algorithm, called the BetaSCP, for the side-chain positioning problem based on the beta-complex which is a derivative geometric construct of the Voronoi diagram. Placing a higher priority on solution quality, the BetaSCP algorithm produces a solution very close to the optima within a reasonable computation time. The effectiveness and efficiency of the BetaSCP are experimentally shown via a benchmark test against well-known algorithms using twenty test models selected from Protein Data Bank. Copyright Springer Science+Business Media, LLC. 2013

Suggested Citation

  • Joonghyun Ryu & Deok-Soo Kim, 2013. "Protein structure optimization by side-chain positioning via beta-complex," Journal of Global Optimization, Springer, vol. 57(1), pages 217-250, September.
    • Handle: RePEc:spr:jglopt:v:57:y:2013:i:1:p:217-250
    DOI: 10.1007/s10898-012-9886-3
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    References listed on IDEAS

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    1. H. K. Fung & S. Rao & C. A. Floudas & O. Prokopyev & P. M. Pardalos & F. Rendl, 2005. "Computational Comparison Studies of Quadratic Assignment Like Formulations for the In Silico Sequence Selection Problem in De Novo Protein Design," Journal of Combinatorial Optimization, Springer, vol. 10(1), pages 41-60, August.
    2. Bernard Chazelle & Carl Kingsford & Mona Singh, 2004. "A Semidefinite Programming Approach to Side Chain Positioning with New Rounding Strategies," INFORMS Journal on Computing, INFORMS, vol. 16(4), pages 380-392, November.
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    Cited by:

    1. Douglas S. Gonçalves & Antonio Mucherino & Carlile Lavor & Leo Liberti, 2017. "Recent advances on the interval distance geometry problem," Journal of Global Optimization, Springer, vol. 69(3), pages 525-545, November.
    2. Saeed Asaeedi & Farzad Didehvar & Ali Mohades, 2018. "NLP Formulation for Polygon Optimization Problems," Mathematics, MDPI, vol. 7(1), pages 1-25, December.

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