On the structural complexity of a protein

Calland, Pierre-Yves

doi:10.1093/proeng/gzg011

Cited by 13 publications

(8 citation statements)

References 7 publications

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“…For example, the [met]-enkephalin pentapeptide, composed of 75 atoms and having five amino-acids, Tyr-Gly-Gly-Phe-Met, and 22 variable backbone dihedral angles, a number of 10 11 local optima is estimated. Detailed aspects concerning complexity issues are discussed in Crescenzi et al (1998);Calland (2003). This leads to the mention of the Levinthal's paradox (Cyrus Levinthal 1969), which states that, despite the numerous pathways, in vivo molecular folding expands over a time scale magnitude of several milliseconds.…”

Section: Protein Structure Predictionmentioning

confidence: 99%

A grid-based genetic algorithm combined with an adaptive simulated annealing for protein structure prediction

2008

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International audienceA hierarchical hybrid model of parallel metaheuristics is proposed, combining an evolutionary algorithm and an adaptive simulated annealing. The algorithms are executed inside a grid environment with different parallelization strategies: the synchronous multi-start model, parallel evaluation of different solutions and an insular model with asynchronous migrations. Furthermore, a conjugated gradient local search method is employed at different stages of the exploration process. The algorithms were evaluated using the protein structure prediction problem, having as benchmarks the tryptophan-cage protein (Brookhaven Protein Data Bank ID: 1L2Y), the tryptophan-zipper protein (PDB ID: 1LE1) and the α-Cyclodextrin complex. Experimentations were performed on a nation-wide grid infrastructure, over six distinct administrative domains and gathering nearly 1,000 CPUs. The complexity of the protein structure prediction problem remains prohibitive as far as large proteins are concerned, making the use of parallel computing on the computational grid essential for its efficient resolution

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Section: Protein Structure Predictionmentioning

confidence: 99%

A grid-based genetic algorithm combined with an adaptive simulated annealing for protein structure prediction

2008

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“…For example, for the [met]-enkephalin pentapeptide, composed of 75 atoms and having five amino-acids, Tyr-Gly-Gly-PheMet, and 22 variable backbone dihedral angles, a number of 10 11 local optima is estimated. Detailed aspects concerning complexity matters were discussed in [25] [26], leading to the mention of the Levinthal's paradox [7] which states that, despite numerous pathways, in vivo molecular folding for example, has a time scale magnitude of several milliseconds. Notes on molecular structure prediction complexity may be found in [24].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Parallel Metaheuristics for Protein Structure Prediction on the Computational Grid

Tantar

Melab

Talbi

2007

2007 IEEE International Parallel and Distributed Processing Symposium

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I. INTRODUCTIONWith the evolution of high-performance and highthroughput distributed computing and with the proliferation of nuclear magnetic resonance (NMR) data, we are at the dawns of a new era in molecular research and pharmaceutical drug design. A focus is set by current research on molecular structure prediction, molecular folding and molecular docking. Computational modeling and prediction offer an alternative to laboratory experimentation, unfeasible for large size molecules. A viable approach for addressing the implied complexity matters is grid computing, nowadays admitted as a powerful way for achieving high performance on computational-intensive applications.The protein structure prediction problem, further referred to as PSP, is one of the particularly interesting challenges of

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“…The reason why side-chain information has not been used before during the backbone threading process is obviously due to the belief that the problem is computationally too intractable to be practically useful. Clearly, our generalized threading problem is NP-hard because both backbone threading and side-chain packing prediction are its special cases, respectively, and both have been shown to be NP-hard [27], [28]. Despite all this, we still attempt to develop a rigorous and sufficiently fast algorithm for generalized protein threading, by taking advantage of the special properties of the generalized threading problem.…”

Section: Introductionmentioning

confidence: 99%

An Algorithm for Simultaneous Backbone Threading and Side-Chain Packing

Liu

Guo

et al. 2007

Algorithmica

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To utilize fully all available information in protein structure prediction, including both backbone and side-chain structures, we present a novel algorithm for solving a generalized threading problem. In this problem we consider simultaneous backbone threading and side-chain packing during the process of a protein structure prediction. For a given query protein sequence and a template structure, our goal is to find a threading alignment between the query sequence and the template structure, along with a rotamer assignment for each side-chain of the query protein, which optimizes an energy function that combines a backbone threading energy and a side-chain packing energy. This highly computationally challenging problem is solved through first formulating this problem as a graph-based optimization problem. Various graph-theoretic techniques are employed to achieve the computational efficiency to make our algorithm practically useful, which takes advantage of a number of special properties of the graph representing this generalized threading problem. The overall framework of our algorithm is a dynamic programming algorithm implemented on an optimal tree decomposition of the graph representation of our problem. By using various additional heuristic techniques such as dead-end elimination, we have demonstrated that our algorithm can solve a generalized threading problem within a practically acceptable amount of time and space, the first of its kind.

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On the structural complexity of a protein

Cited by 13 publications

References 7 publications

A grid-based genetic algorithm combined with an adaptive simulated annealing for protein structure prediction

A grid-based genetic algorithm combined with an adaptive simulated annealing for protein structure prediction

A Comparative Study of Parallel Metaheuristics for Protein Structure Prediction on the Computational Grid

An Algorithm for Simultaneous Backbone Threading and Side-Chain Packing

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