Protein Structure Prediction with EPSO in Toy Model

摘要

Predicting the structure of protein through its sequence of amino acids is a complex and challenging problem in computational biology. Though toy model is one of the simplest and effective models, it is still extremely difficult to predict its structure as the increase of amino acids. Particle swarm optimization (PSO) is a swarm intelligence algorithm, has been successfully applied to many optimization problems and shown its high search speed in these applications. However, as the dimension and the number of local optima of problems increase, PSO is easily trapped in local optima. We have proposed an improved PSO algorithm is called EPSO in the other paper, which has greatly improved the ability of escaping form local optima. In this paper we applied EPSO to the structure prediction of toy model both on artificial and real protein sequences and compared with the results reported in other literatures. The experimental results demonstrated that EPSO was efficient in protein structure prediction problem in toy model.