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International Conference on Research in Computational Molecular Biology

RECOMB 2015: Research in Computational Molecular Biology pp 354–366Cite as

Computational Protein Design Using AND/OR Branch-and-Bound Search

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Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 9029))

Abstract

The computation of the global minimum energy conformation (GMEC) is an important and challenging topic in structure-based computational protein design. In this paper, we propose a new protein design algorithm based on the AND/OR branch-and-bound (AOBB) search, which is a variant of the traditional branch-and-bound search algorithm, to solve this combinatorial optimization problem. By integrating with a powerful heuristic function, AOBB is able to fully exploit the graph structure of the underlying residue interaction network of a backbone template to significantly accelerate the design process. Tests on real protein data show that our new protein design algorithm is able to solve many problems that were previously unsolvable by the traditional exact search algorithms, and for the problems that can be solved with traditional provable algorithms, our new method can provide a large speedup by several orders of magnitude while still guaranteeing to find the global minimum energy conformation (GMEC) solution.

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Author information

Authors and Affiliations

  1. Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, People’s Republic of China

    Yichao Zhou, Yuexin Wu & Jianyang Zeng

  2. MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, People’s Republic of China

    Jianyang Zeng

Authors
  1. Yichao Zhou
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  2. Yuexin Wu
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  3. Jianyang Zeng
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Corresponding author

Correspondence to Jianyang Zeng .

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Editors and Affiliations

  1. National Center of Biotechnology Information, Bethesda, Maryland, USA

    Teresa M. Przytycka

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Zhou, Y., Wu, Y., Zeng, J. (2015). Computational Protein Design Using AND/OR Branch-and-Bound Search. In: Przytycka, T. (eds) Research in Computational Molecular Biology. RECOMB 2015. Lecture Notes in Computer Science(), vol 9029. Springer, Cham. https://doi.org/10.1007/978-3-319-16706-0_36

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  • DOI: https://doi.org/10.1007/978-3-319-16706-0_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16705-3

  • Online ISBN: 978-3-319-16706-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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