Two Local Search Methods for Protein Folding Simulation in the HP and the MJ Lattice Models

Kapsokalivas, Leonidas; Gan, Xiangchao; Albrecht, Andreas; Steinhöfel, Kathleen

doi:10.1007/978-3-540-70600-7_13

Cited by 5 publications

(8 citation statements)

References 36 publications

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“…Except for HP1, HP4 and HP6, our method improves on the number of energy function evaluations reported in [52], and on eight out of the ten instances, Oval min is smaller than

{Eval}_{avg}^{pub}

. If the worst (HP1) and best (HP7) performances are left out, we achieve an average speed-up of 2.1.…”

Section: Resultsmentioning

confidence: 85%

“…In the literature, comparable data with respect to function evaluations are provided in [ 52 ] with respect to the H-P model (ten benchmarks from Table 1 ), and in [ 41 ] for the M-J model (six benchmarks from Table 2 ); in both cases, for the three-dimensional cubic lattice. The function evaluations are counted for the respective energy function, and therefore, we denote by Eval the total number of energy function evaluations.…”

Section: Resultsmentioning

confidence: 99%

“…The function evaluations are counted for the respective energy function, and therefore, we denote by Eval the total number of energy function evaluations. We note that in [ 52 ], the value of Eval is equal to the product of the population size (= 20) and the number of ‘generations’. Since the number of ‘generations’ reported in [ 52 ] is the average value over 10 independent runs, we use Eval avg for comparison purposes.…”

Section: Resultsmentioning

confidence: 99%

“…We note that in [ 52 ], the value of Eval is equal to the product of the population size (= 20) and the number of ‘generations’. Since the number of ‘generations’ reported in [ 52 ] is the average value over 10 independent runs, we use Eval avg for comparison purposes. In [ 41 ] (see Tables 9-14 there), 10 independent runs are reported individually with their respective Eval value and minimum energy value.…”

Section: Resultsmentioning

confidence: 99%

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A Firefly-Inspired Method for Protein Structure Prediction in Lattice Models

et al. 2014

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We introduce a Firefly-inspired algorithmic approach for protein structure prediction over two different lattice models in three-dimensional space. In particular, we consider three-dimensional cubic and three-dimensional face-centred-cubic (FCC) lattices. The underlying energy models are the Hydrophobic-Polar (H-P) model, the Miyazawa–Jernigan (M-J) model and a related matrix model. The implementation of our approach is tested on ten H-P benchmark problems of a length of 48 and ten M-J benchmark problems of a length ranging from 48 until 61. The key complexity parameter we investigate is the total number of objective function evaluations required to achieve the optimum energy values for the H-P model or competitive results in comparison to published values for the M-J model. For H-P instances and cubic lattices, where data for comparison are available, we obtain an average speed-up over eight instances of 2.1, leaving out two extreme values (otherwise, 8.8). For six M-J instances, data for comparison are available for cubic lattices and runs with a population size of 100, where, a priori, the minimum free energy is a termination criterion. The average speed-up over four instances is 1.2 (leaving out two extreme values, otherwise 1.1), which is achieved for a population size of only eight instances. The present study is a test case with initial results for ad hoc parameter settings, with the aim of justifying future research on larger instances within lattice model settings, eventually leading to the ultimate goal of implementations for off-lattice models.

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“…Except for HP1, HP4 and HP6, our method improves on the number of energy function evaluations reported in [52], and on eight out of the ten instances, Oval min is smaller than

{Eval}_{avg}^{pub}

. If the worst (HP1) and best (HP7) performances are left out, we achieve an average speed-up of 2.1.…”

Section: Resultsmentioning

confidence: 85%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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A Firefly-Inspired Method for Protein Structure Prediction in Lattice Models

et al. 2014

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“…But these energy functions are expensive in terms of both complexity and computational resources, making it infeasible even for the smallest protein sequence [ 12 ]. Besides, there are methods available based on interactions between specific pairs of amino acids with lower complexities such as Miyazawa-Jernigan (MJ) model [ 13 – 15 ] and Berrera energy model (BM model). Both these models use 20 x 20 energy matrix, and MJ uses effective contacts between all amino acid pairs, whereas BM uses van der Waals radii of alpha carbon and side-chain heavy atoms.…”

Section: Introductionmentioning

confidence: 99%

A Novel Framework for Ab Initio Coarse Protein Structure Prediction

Dubey

Balaji

Kini

et al. 2018

Advances in Bioinformatics

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Hydrophobic-Polar model is a simplified representation of Protein Structure Prediction (PSP) problem. However, even with the HP model, the PSP problem remains NP-complete. This work proposes a systematic and problem specific design for operators of the evolutionary program which hybrids with local search hill climbing, to efficiently explore the search space of PSP and thereby obtain an optimum conformation. The proposed algorithm achieves this by incorporating the following novel features: (i) new initialization method which generates only valid individuals with (rather than random) better fitness values; (ii) use of probability-based selection operators that limit the local convergence; (iii) use of secondary structure based mutation operator that makes the structure more closely to the laboratory determined structure; and (iv) incorporating all the above-mentioned features developed a complete two-tier framework. The developed framework builds the protein conformation on the square and triangular lattice. The test has been performed using benchmark sequences, and a comparative evaluation is done with various state-of-the-art algorithms. Moreover, in addition to hypothetical test sequences, we have tested protein sequences deposited in protein database repository. It has been observed that the proposed framework has shown superior performance regarding accuracy (fitness value) and speed (number of generations needed to attain the final conformation). The concepts used to enhance the performance are generic and can be used with any other population-based search algorithm such as genetic algorithm, ant colony optimization, and immune algorithm.

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Computational Coarse Protein Modeling of HIV-1 Sequences Using Evolutionary Search Algorithm

Dubey

Balaji

2019

Global Virology III: Virology in the 21st Century

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Two Local Search Methods for Protein Folding Simulation in the HP and the MJ Lattice Models

Cited by 5 publications

References 36 publications

A Firefly-Inspired Method for Protein Structure Prediction in Lattice Models

A Firefly-Inspired Method for Protein Structure Prediction in Lattice Models

A Novel Framework for Ab Initio Coarse Protein Structure Prediction

Computational Coarse Protein Modeling of HIV-1 Sequences Using Evolutionary Search Algorithm

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