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

Abstract

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.

Dynamic Radius Species Conserving Genetic Algorithm for Test Generation for Structural Testing

“…They have been used for decades to solve problems with large complex domains. Not only have they been used for structural testing, they have been used for countless other problems like predicting stock prices [11], optimization of large complex functions [10] and determining how amino acids fold to create proteins [12].…”

“…While selecting stocks from the Dow Jones Industrial Index the stocks selected outperformed the underlying index by nearly 400%. Recently it has been used in de novo protein folding problem [12]. Research shows that DSGA is a very good optimization algorithm.…”