Protein structure prediction using particle swarm optimization and a distributed parallel approach

Kondov, Ivan; Berlich, Rüdiger

doi:10.1145/1998570.1998579

Cited by 6 publications

(3 citation statements)

References 36 publications

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“…Different types of metaheuristic have been used in solving the simplified PSP problem. These include Monte Carlo simulation [22], simulated annealing [23], genetic algorithms (GA) [24,25], tabu search with GA [26], tabu search with hill climbing [27], ant colony optimisation [28], particle swarm optimisation [29,30], immune algorithms [31], tabu-based stochastic local search [8,32], and constraint programming [33,34]. Cebrián et al [32] used tabu-based local search, and Shatabda et al [8] used memory-based local search with tabu heuristic and achieved the state-of-the-art results.…”

Section: Hp Energy Basedmentioning

confidence: 99%

Mixing Energy Models in Genetic Algorithms for On-Lattice Protein Structure Prediction

Rashid

Newton²,

Hoque³

et al. 2013

BioMed Research International

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Protein structure prediction (PSP) is computationally a very challenging problem. The challenge largely comes from the fact that the energy function that needs to be minimised in order to obtain the native structure of a given protein is not clearly known. A high resolution 20 × 20 energy model could better capture the behaviour of the actual energy function than a low resolution energy model such as hydrophobic polar. However, the fine grained details of the high resolution interaction energy matrix are often not very informative for guiding the search. In contrast, a low resolution energy model could effectively bias the search towards certain promising directions. In this paper, we develop a genetic algorithm that mainly uses a high resolution energy model for protein structure evaluation but uses a low resolution HP energy model in focussing the search towards exploring structures that have hydrophobic cores. We experimentally show that this mixing of energy models leads to significant lower energy structures compared to the state-of-the-art results.

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Section: Hp Energy Basedmentioning

confidence: 99%

Mixing Energy Models in Genetic Algorithms for On-Lattice Protein Structure Prediction

Rashid

Newton²,

Hoque³

et al. 2013

BioMed Research International

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“…Kondow and Berlich [61] runs particle swarm optimization (PSO) on cloud for the simulation of proteins three-dimensional structure. They simulate all-atom force field using ArFlock library, aimed at finding the folded state of two proteins of different sizes starting from completely extended conformations.…”

Section: Cloud and Distributed Computingmentioning

confidence: 99%

Soft Computing Techniques for the Protein Folding Problem on High Performance Computing Architectures

Llanes¹,

Muñoz²,

Bueno-Crespo³

et al. 2016

CDT

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The protein-folding problem has been extensively studied during the last fifty years. The understanding of the dynamics of global shape of a protein and the influence on its biological function can help us to discover new and more effective drugs to deal with diseases of pharmacological relevance. Different computational approaches have been developed by different researchers in order to foresee the threedimensional arrangement of atoms of proteins from their sequences. However, the computational complexity of this problem makes mandatory the search for new models, novel algorithmic strategies and hardware platforms that provide solutions in a reasonable time frame. We present in this revision work the past and last tendencies regarding protein folding simulations from both perspectives; hardware and software. Of particular interest to us are both the use of inexact solutions to this computationally hard problem as well as which hardware platforms have been used for running this kind of Soft Computing techniques.

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“…Tabu Search with Hill Climbing [32], Ant Colony Optimization [33], Particle Swarm Optimization [34,35],…”

Section: Introductionmentioning

confidence: 99%

Guided macro-mutation in a graded energy based genetic algorithm for protein structure prediction

Rashid

Iqbal

Khatib

et al. 2016

Computational Biology and Chemistry

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Protein structure prediction is considered as one of the most challenging and computationally intractable combinatorial problem. Thus, the efficient modeling of convoluted search space, the clever use of energy functions, and more importantly, the use of effective sampling algorithms become crucial to address this problem. For protein structure modeling, an off-lattice model provides limited scopes to exercise and evaluate the algorithmic developments due to its astronomically large set of data-points. In contrast, an on-lattice model widens the scopes and permits studying the relatively larger proteins because of its finite set of data-points. In this work, we took the full advantage of an on-lattice model by using a face-centered-cube lattice that has the highest packing density with the maximum degree of freedom. We proposed a graded energy-strategically mixes the Miyazawa-Jernigan (MJ) energy with the hydrophobic-polar (HP) energy-based genetic algorithm (GA) for conformational search. In our application, we introduced a 2 × 2 HP energy guided macro-mutation operator within the GA to explore the best possible local changes exhaustively. Conversely, the 20 × 20 MJ energy model-the ultimate objective function of our GA that needs to be minimized-considers the impacts amongst the 20 different amino acids and allow searching the globally acceptable conformations. On a set of benchmark proteins, our proposed approach outperformed state-of-the-art approaches in terms of the free energy levels and the root-mean-square deviations.

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Protein structure prediction using particle swarm optimization and a distributed parallel approach

Cited by 6 publications

References 36 publications

Mixing Energy Models in Genetic Algorithms for On-Lattice Protein Structure Prediction

Mixing Energy Models in Genetic Algorithms for On-Lattice Protein Structure Prediction

Soft Computing Techniques for the Protein Folding Problem on High Performance Computing Architectures

Guided macro-mutation in a graded energy based genetic algorithm for protein structure prediction

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