A hybrid genetic algorithm approach for protein secondary structures

Abstract

The importance of secondary protein structures is to help us to recognize many biological features, such as the structure and function of a protein, the evolutionary relation between proteins, and protein classification. Unfortunately, the secondary protein structures are hard to get from experimental analysis, and most researchers usually use predictive information instead of real structures. However, we want to look at the natures of protein secondary structures rather than puffing too much focus on current approaches to protein secondary structure prediction. For the regularity of secondary protein structures, we define a schema and adopt an approach of evolutionary computation. The approach combines a clustering method and genetic algorithms to produce the schemata for the visible natures of protein secondary structures. There are two major roles of clustering algorithm, one is to generate part of the initial chromosomes in the genetic algorithms and the other is to assist schemata in predicting secondary protein structures. By performing the new approach, the accuracy of Q3 can be improved 12% more than the previous researches. Finally, we will demonstrate some schemata with interesting biological meaning.