Population-based Modified Extremal Optimization for Contact Map Overlap Maximization Problem

Abstract

The three-dimensional structures of proteins provide biogenic functions for biological activities. Proteins that have similar three-dimensional structures usually have similar biological functions. Therefore, many researchers focus on the techniques for comparing the three-dimensional structures of proteins. Many of these techniques for comparing protein structures are based on protein structure alignment, which is one of the most effective methods for extracting similar strutures. The Contact Map Overlap (CMO) maximization problem (for short, the CMO problem) is formulated as a combinatorial optimization for finding the optimal structure alignments. In this paper, we propose a novel bio-inspired heuristic using Population-based Modified Extremal Optimization (PMEO) for the CMO problem. The proposed heuristic has two features. First, the proposed heuristic uses PMEO. There are multiple individuals in a population which repeat alternation of generations. Second, to improve the search efficiency, individuals copy a sub-structure of an individual with good sub-structures at each alternation of generations.