Improved hybrid optimization algorithm for 3D protein structure prediction
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- 作者：Changjun Zhou (1)
Caixia Hou (1)
Xiaopeng Wei (1)
Qiang Zhang (1)
1. Key Laboratory of Advanced Design and Intelligent Computing ; Dalian University ; Dalian ; China - 关键词：Crossover ; Genetic algorithm ; Mutation ; Protein structure prediction ; Particle swarm optimization ; Toy off ; lattice model ; Tabu search
- 刊名：Journal of Molecular Modeling
- 出版年：2014
- 出版时间：July 2014
- 年：2014
- 卷：20
- 期：7
- 全文大小：1,282 KB
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- 刊物主题：Chemistry
Computer Applications in Chemistry
Biomedicine
Molecular Medicine
Health Informatics and Administration
Life Sciences
Computer Application in Life Sciences - 出版者：Springer Berlin / Heidelberg
- ISSN：0948-5023
文摘
A new improved hybrid optimization algorithm - PGATS algorithm, which is based on toy off-lattice model, is presented for dealing with three-dimensional protein structure prediction problems. The algorithm combines the particle swarm optimization (PSO), genetic algorithm (GA), and tabu search (TS) algorithms. Otherwise, we also take some different improved strategies. The factor of stochastic disturbance is joined in the particle swarm optimization to improve the search ability; the operations of crossover and mutation that are in the genetic algorithm are changed to a kind of random liner method; at last tabu search algorithm is improved by appending a mutation operator. Through the combination of a variety of strategies and algorithms, the protein structure prediction (PSP) in a 3D off-lattice model is achieved. The PSP problem is an NP-hard problem, but the problem can be attributed to a global optimization problem of multi-extremum and multi-parameters. This is the theoretical principle of the hybrid optimization algorithm that is proposed in this paper. The algorithm combines local search and global search, which overcomes the shortcoming of a single algorithm, giving full play to the advantage of each algorithm. In the current universal standard sequences, Fibonacci sequences and real protein sequences are certified. Experiments show that the proposed new method outperforms single algorithms on the accuracy of calculating the protein sequence energy value, which is proved to be an effective way to predict the structure of proteins.