English

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

Artificial Intelligence 2015-01-16 v2 Computational Engineering, Finance, and Science Data Structures and Algorithms

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 prob- lems 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.

Keywords

Cite

@article{arxiv.1412.3138,
  title  = {Computational Protein Design Using AND/OR Branch-and-Bound Search},
  author = {Yichao Zhou and Yuexin Wu and Jianyang Zeng},
  journal= {arXiv preprint arXiv:1412.3138},
  year   = {2015}
}

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