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Protein Side-Chain Placement Through MAP Estimation and Problem-Size Reduction

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Algorithms in Bioinformatics (WABI 2006)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 4175))

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Abstract

We present an exact method for the global minimum energy conformation (GMEC) search of protein side-chains. Our method consists of a branch-and-bound (B&B) framework and a new subproblem-pruning scheme. The pruning scheme consists of upper/lower-bounding methods and problem-size reduction techniques. We explore a way of using the tree-reweighted max-product algorithm for computing lower-bounds of the GMEC energy. The problem-size reduction techniques are necessary when the size of the subproblem is too large to rely on more accurate yet expensive bounding methods. The experimental results show our pruning scheme is effective and our B&B method exactly solves protein sequence design cases that are very hard to solve with the dead-end elimination.

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Hong, EJ., Lozano-Pérez, T. (2006). Protein Side-Chain Placement Through MAP Estimation and Problem-Size Reduction. In: Bücher, P., Moret, B.M.E. (eds) Algorithms in Bioinformatics. WABI 2006. Lecture Notes in Computer Science(), vol 4175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11851561_21

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  • DOI: https://doi.org/10.1007/11851561_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-39583-6

  • Online ISBN: 978-3-540-39584-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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