A branch-and-bound approach to knowledge-based protein structure assembly

With the unprecedented growth in the size of sequence and structure databases, knowledge-based methods have become increasingly feasible for protein structure prediction. We developed a branch-and-bound method for structlets-based protein structure assembly. We explore the effectiveness of this approach by examining its capability to reconstruct the 3D structure of some proteins with known 3D structures. Although our algorithm involves exhaustive search, our BestFirst implementation of a branch-and bound strategy is able to eliminate around 2/3 of the total search space in order to find the optimal 3D assembly for a protein of interest.