Protein folding structure optimization based on GAPSO algorithm in the off-lattice model

Lin, Xiao; Zhang, Xiaolong

doi:10.1109/bibm.2014.6999246

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…A list of benchmark sequences with the 'A' and 'B' monomers are given in Table I where N is the sum of the monomers. A comparative study is performed in Table II to observe the performance of ELAMO with respect to some powerful optimization algorithms; Improved Particle Swarm Optimization (EPSO) [27], Internal Feedback strategy based on Artificial Bee Colony Algorithm (IF-ABC) [28], Combination of Genetic Algorithm and Particle Swarm Optimization (GAPSO) [21] and standard AMO which ELAMO is originated from. It is also important to note that the results of the compared algorithms included to the comparison table as they appeared in their original studies.…”

Section: Resultsmentioning

confidence: 99%

“…Multi-meme algorithms are also adapted for solving PSP by Krasnogor et al [20]. Lin and Zhang introduced a novel-hybrid global optimization method by forming Genetic Algorithm and Particle Swarm Optimization to solve PSP in which aiming to produce lower energy conformation levels [21]. Boiani and Parpinelli proposed a hybrid algorithm called cuHjDE-3D which is formed by self-adaptive Differential Evolution that uses jDE and Hooke-Jeeves Direct Search (HJDS) [22].…”

Section: Related Workmentioning

confidence: 99%

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Elitist Animal Migration Optimization for Protein Structure Prediction based on 3D Off-Lattice Model

Ülker¹

2023

IJACSA

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Predicting the structure of protein has been the center of attraction for the researchers. The aim is to make a reliable prediction of the protein structure by obtaining the minimum energy values among amino acids interactions. According to the generated shape of amino acids, the functionality of the proteins can be determined. However, it is known as one of the most challenging tasks in the field of bioinformatics considering its high computation complexity. Metaheuristic algorithms are mainly preferred by researchers from various fields, since their performances are quite satisfactory in solving such complex problems. Animal Migration Optimization (AMO) algorithm is a metaheuristic approach which mimics the behavior of animals during the migration process. However, in this research to reach a high solution quality, an elitist version of Animal Migration Optimization (ELAMO) algorithm is considered and in particular it is applied to Protein Structure Prediction (PSP) problem. The performance of ELAMO is tested on some well-studied artificial and real protein sequences, and then compared with powerful optimization algorithms which are specially designed for solving PSP problem. The results show that ELAMO is quite capable in solving this problem. Hence, it can be used as an efficient optimizer for solving complex problems that require better solution quality in the field of bioinformatics.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Elitist Animal Migration Optimization for Protein Structure Prediction based on 3D Off-Lattice Model

Ülker¹

2023

IJACSA

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“…In the proposed method each algorithm is improved itself using some improvement strategy. Another hybrid technique, named GAPSO and suggested by Lin and Zhang (2014), combines GA and PSO to predict the native conformation of proteins. In Jana et al.…”

Section: Protein Structure Predictionmentioning

confidence: 99%

On the role of metaheuristic optimization in bioinformatics

Calvet

Benito

Juan

et al. 2022

Int Trans Operational Res

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Metaheuristic algorithms are employed to solve complex and large-scale optimization problems in many different fields, from transportation and smart cities to finance. This paper discusses how metaheuristic algorithms are being applied to solve different optimization problems in the area of bioinformatics. While the text provides references to many optimization problems in the area, it focuses on those that have attracted more interest from the optimization community. Among the problems analyzed, the paper discusses in more detail the molecular docking problem, the protein structure prediction, phylogenetic inference, and different string problems. In addition, references to other relevant optimization problems are also given, including those related to medical imaging or gene selection for classification. From the previous analysis, the paper generates insights on research opportunities for the Operations Research and Computer Science communities in the field of bioinformatics.

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Protein folding optimization based on 3D off-lattice model via an improved artificial bee colony algorithm

et al. 2015

J Mol Model

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Protein folding is a fundamental topic in molecular biology. Conventional experimental techniques for protein structure identification or protein folding recognition require strict laboratory requirements and heavy operating burdens, which have largely limited their applications. Alternatively, computer-aided techniques have been developed to optimize protein structures or to predict the protein folding process. In this paper, we utilize a 3D off-lattice model to describe the original protein folding scheme as a simplified energy-optimal numerical problem, where all types of amino acid residues are binarized into hydrophobic and hydrophilic ones. We apply a balance-evolution artificial bee colony (BE-ABC) algorithm as the minimization solver, which is featured by the adaptive adjustment of search intensity to cater for the varying needs during the entire optimization process. In this work, we establish a benchmark case set with 13 real protein sequences from the Protein Data Bank database and evaluate the convergence performance of BE-ABC algorithm through strict comparisons with several state-of-the-art ABC variants in short-term numerical experiments. Besides that, our obtained best-so-far protein structures are compared to the ones in comprehensive previous literature. This study also provides preliminary insights into how artificial intelligence techniques can be applied to reveal the dynamics of protein folding. Graphical Abstract Protein folding optimization using 3D off-lattice model and advanced optimization techniques.

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Protein folding structure optimization based on GAPSO algorithm in the off-lattice model

Cited by 3 publications

References 16 publications

Elitist Animal Migration Optimization for Protein Structure Prediction based on 3D Off-Lattice Model

Elitist Animal Migration Optimization for Protein Structure Prediction based on 3D Off-Lattice Model

On the role of metaheuristic optimization in bioinformatics

Protein folding optimization based on 3D off-lattice model via an improved artificial bee colony algorithm

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