Protein Structure Prediction with EPSO in Toy Model

Zhu, Hongwei; Pu, Chengdong; Lin, Xiao; Gu, Jinguang; Zhang, Shanjun; Su, Mengsi

doi:10.1109/icinis.2009.172

Cited by 12 publications

(9 citation statements)

References 8 publications

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“…We add the item which is much smaller than the former items in Eq. (7), and the search ability of the algorithm has little effect. In the middle period of the search, when the similarity of particles grows higher and higher and the velocities become smaller and smaller, the item can ensure the velocities of search are not 0 and can continue to perform a 3) Combination of multiple crossover and mutation Crossover and mutation of the GA are ways to improve the efficiency of the evolution of the population.…”

Section: ) a Variable Factorsmentioning

confidence: 98%

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Improved hybrid optimization algorithm for 3D protein structure prediction

et al. 2014

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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.

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Section: ) a Variable Factorsmentioning

confidence: 98%

“…In protein structure prediction, the toy off-lattice model is one of the most widely used and relatively accurate models [7]. Amino acids are divided into hydrophobic amino acids (A) and hydrophilic amino acids (B).…”

Section: Introductionmentioning

confidence: 99%

Improved hybrid optimization algorithm for 3D protein structure prediction

et al. 2014

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“…The proposed APM-BA algorithm is compared with simple bees algorithm [20] and the algorithms which are already used for protein structure prediction such as CPSO [10], EPSO [11], IF-ABC [18] in 2D off-lattice model. All experiments are implemented in MATLAB R2010a and executed on an Intel Core (TM) 17-2670 QMCPU running at 2.20 GHz with 8 GB of RAM with Windows XP.…”

Section: Parameter Settings and Initializationmentioning

confidence: 99%

Improved Bees Algorithm for Protein Structure Prediction Using AB Off-Lattice Model

Jana

Sil

Das

2015

Advances in Intelligent Systems and Computing

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Protein Structure Prediction (PSP) using sequence of amino acids is a multimodal optimization problem and belongs to NP hard class. Researchers and scientists put their efforts to design efficient computational intelligent algorithm for solving this kind of problem. Bees Algorithm (BA) is a swarm intelligence based algorithm inspired by the foraging behaviour of honey bees colony, already exhibits its potential ability for solving optimization problems. However, it may produce premature convergence when solving PSP like problems. To prevent this situation, Adaptive Polynomial Mutation based Bees Algorithm (APM-BA) has been proposed in this paper for predicting protein structure in 2D AB off-lattice model. In this strategy, each of best scout bees are mutated with adaptive polynomial mutation technique when their performances are no more improve during execution phase. The experiments are conducted on artificial and real protein sequences and numerical results show that the proposed algorithm has strong ability for solving PSP problem having minimum energy.

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“…These methods are based on simplified models of protein structure, such as particle swarm optimization (PSO) (Liu et al, 2005;Zhu et al, 2009), genetic-annealing algorithm (GAA) (Zhang et al, 2007), quasi-physical algorithm (Liu and Huang, 2007), and energy landscape paving minimizer (ELP) (Bachmann et al, 2005). All these approaches have been successfully studied in the off-lattice AB model, but the efficiency still needs to be improved.…”

Section: Introductionmentioning

confidence: 99%

Effective 3D protein structure prediction with local adjustment genetic-annealing algorithm

Zhang

Lin

2010

Interdiscip Sci Comput Life Sci

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The protein folding problem consists of predicting protein tertiary structure from a given amino acid sequence by minimizing the energy function. The protein folding structure prediction is computationally challenging and has been shown to be NP-hard problem when the 3D off-lattice AB model is employed. In this paper, the local adjustment genetic-annealing (LAGA) algorithm was used to search the ground state of 3D offlattice AB model for protein folding structure. The algorithm included an improved crossover strategy and an improved mutation strategy, where a local adjustment strategy was also used to enhance the searching ability. The experiments were carried out with the Fibonacci sequences. The experimental results demonstrate that the LAGA algorithm appears to have better performance and accuracy compared to the previous methods.

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Protein Structure Prediction with EPSO in Toy Model

Cited by 12 publications

References 8 publications

Improved hybrid optimization algorithm for 3D protein structure prediction

Improved hybrid optimization algorithm for 3D protein structure prediction

Improved Bees Algorithm for Protein Structure Prediction Using AB Off-Lattice Model

Effective 3D protein structure prediction with local adjustment genetic-annealing algorithm

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