Protein Folding Optimization using Differential Evolution Extended with Local Search and Component Reinitialization (original) (raw)

2018

This paper presents a novel Differential Evolution algorithm for protein folding optimization that is applied to a three-dimensional AB off-lattice model. The proposed algorithm includes two new mechanisms. A local search is used to improve convergence speed and to reduce the runtime complexity of the energy calculation. For this purpose, a local movement is introduced within the local search. The designed evolutionary algorithm has fast convergence speed and, therefore, when it is trapped into the local optimum or a relatively good solution is located, it is hard to locate a better similar solution. The similar solution is different from the good solution in only a few components. A component reinitialization method is designed to mitigate this problem. Both the new mechanisms and the proposed algorithm were analyzed on well-known amino acid sequences that are used frequently in the literature. Experimental results show that the employed new mechanisms improve the efficiency of our...

Parallel Evolutionary Multi-Quenching Annealing for Protein Folding Problem

Int. J. Comb. Optim. Probl. Informatics, 2018

The Protein Folding Problem (PFP) consists in determining the functional three-dimensional structure or Native Structure (NS) of a protein, which normally has the lowest Gibbs energy. In this paper, a new hybrid Parallel Evolutionary Multi-Quenching Annealing Algorithm (PEMQA) is proposed to obtain high-quality solutions for the target proteins. PEMQA generates an initial population of solutions using a Genetic Algorithm (GA). Furthermore, a Multi-Quenching Algorithm (MQA) is executed in an independent core using each of these Genetic Algorithm (GA) solutions. A master process determines which MQA delivers the best solution. PEMQA uses shared memory parallel programming and is implemented in SMMP (Simple Molecular Mechanics for Proteins). The incorporation of evolutionary processes in a PEMQA algorithm allows an improvement in MQA capacity of exploration. Results obtained with PEMQA outperform most of those achieved by the classic SA reported in current state of the art literature.

Niche Genetic Algorithms are better than traditional Genetic Algorithms for de novo Protein Folding

F1000Research, 2014

Here we demonstrate that Niche Genetic Algorithms (NGA) are better at computing de novo protein folding than traditional Genetic Algorithms (GA). Previous research has shown that proteins can fold into their active forms in a limited number of ways; however, predicting how a set of amino acids will fold starting from the primary structure is still a mystery. GAs have a unique ability to solve these types of scientific problems because of their computational efficiency. Unfortunately, GAs are generally quite poor at solving problems with multiple optima. However, there is a special group of GAs called Niche Genetic Algorithms (NGA) that are quite good at solving problems with multiple optima. In this study, we use a specific NGA: the Dynamic-radius Species-conserving Genetic Algorithm (DSGA), and show that DSGA is very adept at predicting the folded state of proteins, and that DSGA is better than a traditional GA in deriving the correct folding pattern of a protein.

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