Dsga And Ga From 'Niche Genetic Algorithms Are Better Than Traditional Genetic Algorithms For De Novo Protein Folding '

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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Dsga And Ga From 'Niche Genetic Algorithms Are Better Than Traditional Genetic Algorithms For De Novo Protein Folding '

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A complexity measurement for de novo protein folding

A complexity measurement for de novo protein folding

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

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