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 paper @cite_34 presents the basic and adaptive versions of the DE algorithm with parallel architecture (master-slave). With this architecture, the computational load is divided and the overall performance is improved. An explosion and mirror mutation operators were also included into DE. The explosion is a mechanism that reinitializes the population when the stagnation has occurred, and, thus, it is responsible for preventing premature convergence. The second mechanism, the mirror mutation, was designed to perform a local search by using mirror angles within the sequence.
@cite_34
2085602712
Protein structure prediction (PSP) is a well-known problem in bioinformatics. Identifying protein native conformations makes it possible to predict its function within the organism. Knowing this also helps the development of new drugs and the comprehension of some biological mechanisms. During years some techniques have been developed for this purpose but, due to their high cost, it is necessary to use simplified models of protein structures. However, even the simplest models, with low biological plausibility, are excessively complex from the computational point of view. This paper reports the application of Differential Evolution (DE) to solve the PSP problem using a Toy Model (also known as the AB Model) in both 2D and 3D to represent the protein structure. This work presents two different versions of the DE algorithm (basic and adaptive) using a parallel architecture (master-slave) based on Message Passing Interface in a cluster. Some special operators for DE were developed: explosion and mirror mutation. All tests executed in this work used four benchmark sequences, ranging from 13 to 55 amino acids. The results for both parallel DE algorithms using both 2D and 3D models were compared with other works in the literature. The DE algorithm achieved excellent results. Overall results encourage further research towards the use of knowledge-based operators to improve further the performance of DE.