This paper presents a two-level protein folding optimization on a three-dimensional AB off-lattice model. The first level is responsible for forming conformations with a good hydrophobic core or a set of compact hydrophobic amino acid positions. These conformations are forwarded to the second level, where an accurate search is performed with the aim of locating conformations with the best energy value. The optimization process switches between these two levels until the stopping condition is satisfied. An auxiliary fitness function was designed for the first level, while the original fitness function is used in the second level. The auxiliary fitness function includes expression about the quality of the hydrophobic core. This expression is crucial for leading the search process to the promising solutions that have a good hydrophobic core and, consequently, improves the efficiency of the whole optimization process. Our differential evolution algorithm was used for demonstrating the efficiency of the two-level optimization. It was analyzed on well-known amino acid sequences that are used frequently in the literature. The obtained experimental results show that the employed two-level optimization improves the efficiency of our algorithm significantly, and that the proposed algorithm is superior to other state-of-the-art algorithms.
Abstract Protein structure prediction (PSP) with ab initio model keeps a challenge in bioinformatics on account of high computational complexity. To solve the problem within a limited time and resource, the parallel capacity and search efficiency are of significance for a successful algorithm. Traditional simulated annealing (SA) algorithm is extremely slow in convergence, and the implementation and efficiency of parallel SA algorithms are typically problem-dependent. To overcome such intrinsic limitation, in this paper a multi-agent simulated annealing (MASA) algorithm with parallel adaptive multiple sampling (MASA-PAMS) that features better search ability is proposed. The MASA-PAMS contains two main issues. First, a parallel elitist sampling strategy overcomes the inherent serialization of the original SA, provides benefit information for the iteration which is helpful for the convergence. Then an adaptive neighborhood search and a parallel multiple move mechanism displace the random sampling scheme which balance the intensification and diversification iteratively. Conducted experiments with 2D and 3D AB off-lattice models indicate that the MASA-PAMS performs better than, or at least comparable to other MASAs with different sampling schemes and several state-of-the-art algorithms for PSP.