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.
The authors in @cite_2 have shown that the differential evolution algorithm converges to better solutions when the initial population is created by using trained neural networks. The neural networks were trained successfully using the reinforcement learning method, by knowing only the fitness function of the class of optimization problems.
@cite_2
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Deep neural networks are constructed that are able to partially solve a protein structure optimization problem. The networks are trained using reinforcement learning approach so that free energy of predicted protein structure is minimized. Free energy of a protein structure is calculated using generalized three-dimensional AB off-lattice protein model. This methodology can be applied to other classes of optimization problems and represents a step toward automatic heuristic construction using deep neural networks. Trained networks can be used to construct better initial populations for optimization. It is shown that differential evolution applied to protein structure optimization problem converges to better solutions when initial population is constructed in this way.