An Efficient Algorithm for Computational Protein Design Problem

TL;DR

This paper introduces a novel, efficient algorithm for the computational protein design problem, leveraging a continuous relaxation and penalty method to outperform existing solvers in speed and solution quality.

Contribution

The paper presents a new continuous relaxation-based penalty algorithm for CPD, significantly reducing computational complexity and improving solution times over traditional methods.

Findings

Outperforms branch-and-cut solvers by three orders of magnitude in CPU time.

Achieves high-quality solutions comparable to state-of-the-art methods.

Effective for large-scale protein design problems.

Abstract

In this paper, we consider the computational protein design (CPD) problem, which is usually modeled as a 0/1 programming and is extremely challenging due to its combinatorial properties. We propose an efficient algorithm for solving it. Specifically, we study the quadratic semi-assignment problem formulation (QSAP) of the CPD problem, and show that it is equivalent to its continuous relaxation problem (RQSAP), in terms of sharing the same optimal objective value. Then, we propose an efficient penalty method to solve the QSAP based on the proposed formulations, which is guaranteed to converge to a global solution of the QSAP under certain conditions. Compared with existing branch-and-bound approaches that suffer from high computational complexity, the proposed algorithm is based on a continuous problem and enjoys a low per-iteration complexity, which makes it particularly suitable for solving large-scale CPD problems. Numerical results on benchmark instances verify the superior performance of our approach over the state-of-the-art branch-and-cut solvers. In particular, the proposed algorithm outperforms the state-of-the-art solvers by three order of magnitude in CPU time in most cases, while it still returns high-quality solutions.