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

Rashid, Mahmood A.; Newton, M. A. Hakim; Hoque, Tamjidul; Sattar, Abdul

doi:10.1155/2013/924137

Cited by 9 publications

(34 citation statements)

References 41 publications

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“…The result presented in Table 7 under Column GA + is the output of 20 different runs of GA + [11] in an identical setting over 60 minutes duration. The algorithm runs on a single thread using both HP and BM energy models in a mixing manner.…”

Section: Experimental Results and Analysesmentioning

confidence: 99%

A Parallel Framework for Multipoint Spiral Search in ab Initio Protein Structure Prediction

Rashid

Shatabda

Newton³

et al. 2014

Advances in Bioinformatics

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Protein structure prediction is computationally a very challenging problem. A large number of existing search algorithms attempt to solve the problem by exploring possible structures and finding the one with the minimum free energy. However, these algorithms perform poorly on large sized proteins due to an astronomically wide search space. In this paper, we present a multipoint spiral search framework that uses parallel processing techniques to expedite exploration by starting from different points. In our approach, a set of random initial solutions are generated and distributed to different threads. We allow each thread to run for a predefined period of time. The improved solutions are stored threadwise. When the threads finish, the solutions are merged together and the duplicates are removed. A selected distinct set of solutions are then split to different threads again. In our ab initio protein structure prediction method, we use the three-dimensional face-centred-cubic lattice for structure-backbone mapping. We use both the low resolution hydrophobic-polar energy model and the high-resolution 20 × 20 energy model for search guiding. The experimental results show that our new parallel framework significantly improves the results obtained by the state-of-the-art single-point search approaches for both energy models on three-dimensional face-centred-cubic lattice. We also experimentally show the effectiveness of mixing energy models within parallel threads.

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Section: Experimental Results and Analysesmentioning

confidence: 99%

A Parallel Framework for Multipoint Spiral Search in ab Initio Protein Structure Prediction

Rashid

Shatabda

Newton³

et al. 2014

Advances in Bioinformatics

Self Cite

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“…The use of HP-optimal structure samples was shown to boost protein structure prediction in more sophisticated models [21,22,48,50]. Here, no exact methods are available and thus local search schemes are applied.…”

Section: Discussionmentioning

confidence: 99%

“…This results from the energetically lower start conformations, as HP-optimal structures show, on average, about two orders of magnitude lower energies in the enhanced energy model compared with random structures [21,50]. The impact of such an HP-optimal initialization scheme seems to be strongly connected to the subsequently applied optimization procedure, as Rashid and colleagues [48] found their genetic algorithm to be more efficient with random initializations.…”

Section: Optimized Prediction In Full Potential Modelsmentioning

confidence: 99%

“…Among them are its extension and application to the HPNX model [46], which also takes polar interactions into account [47]. Furthermore, HP-optimal predictions can be used within a hierarchical prediction approach to search for energy-optimal structures within full 20 × 20 pairwise potential models [21,22,48]. The latter requires the identification of HP-optimal conformations that are structurally diverse.…”

Section: Introductionmentioning

confidence: 99%

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Exact methods for lattice protein models

Mann

Backofen²

2014

Bio-Algorithms and Med-Systems

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Lattice protein models are well-studied abstractions of globular proteins. By discretizing the structure space and simplifying the energy model over regular proteins, they enable detailed studies of protein structure formation and evolution. However, even in the simplest lattice protein models, the prediction of optimal structures is computationally difficult. Therefore, often, heuristic approaches are applied to find such conformations.Commonly, heuristic methods find only locally optimal solutions. Nevertheless, there exist methods that guarantee to predict globally optimal structures. Currently, only one such exact approach is publicly available, namely the constraint-based protein structure prediction method and variants. Here, we review exact approaches and derived methods. We discuss fundamental concepts like hydrophobic core construction and their use in optimal structure prediction, as well as possible applications like combinations of different energy models.

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“…However, the underlying energy matrix in [33] is taken from [32]. Therefore, we executed computational experiments for 3D FCC lattices on benchmarks from Table 3 for the energy matrix defined in [32].…”

Section: Methodsmentioning

confidence: 99%

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

Cited by 9 publications

References 41 publications

A Parallel Framework for Multipoint Spiral Search in ab Initio Protein Structure Prediction

A Parallel Framework for Multipoint Spiral Search in ab Initio Protein Structure Prediction

Exact methods for lattice protein models

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

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