Selection of appropriate metaheuristic algorithms for protein structure prediction in AB off-lattice model: a perspective from fitness landscape analysis

Abstract

Protein structure prediction (PSP) from its primary sequence is a challenging task in computational biology. PSP is an optimization problem that determines the stable or native structure with minimum free energy. Several researchers have applied various heuristic algorithms and/or their variants to solve this problem. However, the mechanism to select a particular algorithm is not known a priori. Fitness landscape analysis (FLA) is a technique to determine the characteristics of a problem or its structural features based on which the most appropriate algorithm can be recommended for solving the problem. The aim of this study is two-fold while considering the PSP problem. Firstly, the structural features are determined by using the standard FLA techniques and secondly, the performance of some of the well-known optimization algorithms are analyzed based on the structural features as an illustration of the usefulness of the former research agenda. In this paper, we determine structural features of the PSP problem by analyzing the landscapes generated by using the quasi-random sampling technique and city block distance. Comprehensive simulations are carried out on both artificial and real protein sequences in 2D and 3D AB off-lattice model. Numerical results indicate that the complexity of the PSP problem increases with protein sequence length. We calculate the Pearson correlation coefficient between the FLA measures, separately for 2D and 3D off-lattice model and significant differences are identified among the measures. Six well-known real-coded optimization algorithms are evaluated over the same set of protein sequences and the performances are subsequently analyzed based on the structural features. Finally, we suggest the most appropriate algorithms for solving different classes of PSP problem.