The Research Of Protein Structure Prediction Algorithms Based On Improved Particle Swarm

With the successful implementation of the genome project and the rapid development ofcomputer technology, large-scale genome sequencing, gene prediction and annotation havebeen completed, leading to the establishment of a large number of bioinformatics databases,making the research of protein structure prediction become increasingly urgent. When theresearch of bioinformatics enter into the post-genomic era, protein engineering which is animportant part of bioinformatics becomes the forefront development field of modernbiotechnology. life activities is reflected by the protein, and life activities are an importantfeature of the organism. Each protein has its own unique amino acid composition andsequence, and it can play a normal biological function only when it is folded into the correctstructure. Otherwise it may cause diseases. Therefore, the research of stability of the proteinstructure is especially important. How to find a more stable protein structure becomes animportant issue of protein engineering, computer technology has become an importantprocessing tool of large data of protein molecules.The prediction of the spatial structure of the protein has two major difficulties:One is how to establish an energy function of computation simply and distinguish naturalprotein structure correctly; the other is to find the global optimization methods which iscorresponding to the model of minimum energy function. This paper mainly focuses on thelatter. Based on improved particle swarm optimization algorithm, making the protein structureprediction as the research object, the paper proposed three different optimization methods forHP lattice model and AB-lattice model respectively.The first kind of method is a multi-crossover and mutation Particle Swarm Optimization-Tabu Search algorithm (MCMPSO-TS). The algorithm combined specific crossover andmutation techniques of genetic algorithm and particle swarm optimization which is of thecharacteristics of simple and universal and strong local search, the strong climbing ability oftabu search that can realize the advantages of global optimization based on3D HP latticemodel. Furthermore it also proposed the computation of the standard Fibonacci sequence andprotein sequence, and achieved a better results.The second kind of method is the improved Genetic-Particle Swarm and Tabu Searchalgorithm (PGATS). The algorithm is based on the3D Toy (AB) off-lattice model. It is newalgorithm that used genetic algorithm of linear adjustment mechanism, particle swarmoptimization of stochastic disturbance factor, and tabu search algorithm of mutation operator. Single algorithm has been improved and used a variety of adjustment mechanism. Thealgorithm exhibits a certain advantage for calculating longer sequences.The third kind of method is an improved genetic particle swarm optimization(GA-PSO)that is another algorithm which is based on the AB model. The algorithm combinessingle-point crossover and mutation, as well as crossover and mutation of linear and particleswarm algorithm that of containing disturbance factor. By comparing, the results are betterresults than a single algorithm and less time than the second algorithm.This paper presents three hybrid search algorithms about the problem of protein structureprediction and uses Fibonacci sequences and real protein sequences as the experimental datato validate, achieving better results. And it reduces the minimum energy function which iscorresponding to the model Effectively. more stable protein structure are obtained.