Research On Algorithms Of Protein Structure Prediction

The protein natural structure is decided by its amino-acid sequence, and its biological functions are dependent on this structure extensively. So prediction of protein structure is a long historic task, and still a challenge in the research of protein, which is becoming an important research domain in the life science on the post-genome era.Researches have shown that protein's natural structure is decided by their amino-acid sequences, which is theoretical computation base of the protein's structure prediction. Many algorithms have been proposed for the protein structure prediction problem in simple HP model, and have get some fruits, such as Monte Carlo algorithm, the genetic algorithm, approximate algorithm, SISPER and PERM. Shown from the efficiency of solving this problem, those algorithms need to advance. In this paper, we apply improved Ant Colony Optimization Algorithm (ACO) and improved Simulated Annealing Algorithm (SA), to the prediction of protein structure in two kinds of simple HP model respectively, advancing the efficiency of solving this problem.For the protein structure prediction problem based on lattice model, we apply improved ACO to solve it. In old algorithm, the infeasible structures are frequently encountered and required long CPU time. For those problems, propose a"Clone"method to deal with the infeasible structures, and propose a"Point mutation and Reconstruction"method to reduce CPU time in local search phase. Shown from the empirical results, two methods are accurate and feasible.For the protein structure prediction problem based on off-lattice model, it can be treated as a continuous function optimization problem, we apply improved SA to solve it. For the characteristic of SA, propose three improvements in this paper: increasing memorial function; restrained accepting exasperate solution; search in neighborhood. Numerical results illustrate that this algorithm has simple configuration and high efficiency to get optimization solution, and is very suitable for continuous global optimization.Shown from the model of the structure which is get from the algorithm, although HP model is simple, it shows the structure's characters. It's that in the natural structure, the hydrophobic residues are always surrounded by the polar residues, forming a hydrophobic core. It illustrate that two improved algorithms are feasible and effective for the protein structure prediction.In the end, we realize the simple graphics simulation system for protein folding structures, farther it demonstrates the feasibility and validity of the algorithms in this paper.