Extended particle swarm optimisation method for folding protein on triangular lattice

Guo, Yuzhen; Wu, Zikai; Wang, Ying; Wang, Yong

doi:10.1049/iet-syb.2015.0059

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…Furthermore, we have selected the most used instances in the literature to conduct the forthcoming experiments. The used benchmark sequences are shown in Table 1, where the symbol Table 2 shows a comparison of the best results taken from the above-stated algorithms used to solve the PSP problem in the 2D triangular lattice model, namely HGA [22], TS [23], ERS-GA [24], HHGA [24], IMOG [26], EPSO [25], and our suggested approach GALSTS. For each instance given in Table 2, we observe that the best solutions obtained by GALSTS are better or equal than all the cited approaches bellow.…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, two approaches have been proposed in [24], including the Elite-based Reproduction Strategy-Genetic Algorithm (ERS-GA), and a Hybridization of Hill-climbing and Genetic Algorithm (HHGA) that combine the ERS-GA with a hill-climbing algorithm. Recently, an Extended Particle Swarm Optimization method (EPSO) was applied to PSP in [25]. A new approach called IMOG was proposed in [26], which combines Ions Motion Optimization algorithm (IMO) with a Greedy algorithm, the authors showed that IMOG algorithm has good search ability and stability using benchmark data sets.…”

Section: Introductionmentioning

confidence: 99%

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A new hybrid genetic algorithm for protein structure prediction on the 2D triangular lattice

Boumedine¹,

Bouroubi²

2021

Turk J Elec Eng & Comp Sci

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The flawless functioning of the protein is essentially related to its three-dimensional structure. Therefore, predicting protein structure from its amino acid sequence is a fundamental problem that draws researchers' attention in many areas. The Protein Structure Prediction problem (PSP) can be formulated as a combinatorial optimization problem based on simplified lattice models such as the hydrophobic-polar model. In this paper, we propose a new hybrid algorithm that combines three different known heuristic algorithms: the Genetic Algorithm, the Tabu Search Strategy, and the Local Search Algorithm to solve the PSP problem. Regarding the evaluation of the proposed approach, we present an experimental study, where we consider the quality of the product solution as the main assessment criterion. Furthermore, we compared the proposed algorithm with state-of-the-art algorithms using a selection of well-studied benchmark instances.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new hybrid genetic algorithm for protein structure prediction on the 2D triangular lattice

Boumedine¹,

Bouroubi²

2021

Turk J Elec Eng & Comp Sci

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“…For this reason many researchers have been attracted to study this problem by proposing many heuristic algorithms. In recent years, for 2D HP protein folding problem, many methods have been proposed, e.g., PSO (Particle Swarm Optimization) [ 2 ], ACO (Ant Colony Algorithm) [ 3 ], ABO (Artificial Bee Colony) [ 4 ] and SOM (Self-Organizing Mapping) [ 5 ] etc.…”

Section: Introductionmentioning

confidence: 99%

Hybrid method to solve HP model on 3D lattice and to probe protein stability upon amino acid mutations

Guo

Tao

et al. 2017

BMC Syst Biol

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BackgroundPredicting protein structure from amino acid sequence is a prominent problem in computational biology. The long range interactions (or non-local interactions) are known as the main source of complexity for protein folding and dynamics and play the dominant role in the compact architecture. Some simple but exact model, such as HP model, captures the pain point for this difficult problem and has important implications to understand the mapping between protein sequence and structure.ResultsIn this paper, we formulate the biological problem into optimization model to study the hydrophobic-hydrophilic model on 3D square lattice. This is a combinatorial optimization problem and known as NP-hard. Particle swarm optimization is utilized as the heuristic framework to solve the hard problem. To avoid premature in computation, we incorporated the Tabu search strategy. In addition, a pulling strategy was designed to accelerate the convergence of algorithm based on the characteristic of native protein structure. Together a novel hybrid method combining particle swarm optimization, Tabu strategy, and pulling strategy can fold the amino acid sequences on 3D square lattice efficiently. Promising results are reported in several examples by comparing with existing methods. This allows us to use this tool to study the protein stability upon amino acid mutation on 3D lattice. In particular, we evaluate the effect of single amino acid mutation and double amino acids mutation via 3D HP lattice model and some useful insights are derived.ConclusionWe propose a novel hybrid method to combine several heuristic strategies to study HP model on 3D lattice. The results indicate that our hybrid method can predict protein structure more accurately and efficiently. Furthermore, it serves as a useful tools to probe the protein stability on 3D lattice and provides some biological insights.

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A PSO/HS based algorithm for optimization tasks

Ülker

2017

2017 Computing Conference

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Extended particle swarm optimisation method for folding protein on triangular lattice

Cited by 3 publications

References 12 publications

A new hybrid genetic algorithm for protein structure prediction on the 2D triangular lattice

A new hybrid genetic algorithm for protein structure prediction on the 2D triangular lattice

Hybrid method to solve HP model on 3D lattice and to probe protein stability upon amino acid mutations

A PSO/HS based algorithm for optimization tasks

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