Computational Protein Design Using AND/OR Branch-and-Bound Search
This addresses the combinatorial optimization challenge in protein design for researchers, offering significant performance improvements over traditional methods.
The paper tackled the problem of computing the global minimum energy conformation (GMEC) in computational protein design by proposing a new algorithm based on AND/OR branch-and-bound search, which solved previously unsolvable problems and achieved speedups of several orders of magnitude while guaranteeing the GMEC solution.
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.