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

TL;DR

This paper introduces a novel AND/OR branch-and-bound algorithm for computational protein design, significantly improving speed and solvability of the GMEC problem while guaranteeing optimal solutions.

Contribution

The paper presents a new AOBB-based algorithm that exploits graph structure for faster, exact protein design, outperforming traditional methods in speed and problem solvability.

Findings

Successfully solved previously unsolvable problems

Achieved several orders of magnitude speedup

Guaranteed to find the GMEC solution

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.