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

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.Comment: RECOMB 201

Protein side-chain placement through MAP estimation and problem-size reduction

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 subproblempruning 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 lowerbounds 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.

Protein Side-chain Placement through MAP Estimation and Problem-Size Reduction

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 subproblempruning 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 lowerbounds 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.