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See detailAn Analysis of Dynamic Mutation Operators for Conformational Sampling
Tantar, Alexandru-Adrian UL; Melab, Nouredine; Talbi, El-Ghazali

in Lewis, Andrew; Mostaghim, Sanaz; Randall, Marcus (Eds.) Biologically-Inspired Optimisation Methods (2009)

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See detailLocal vs. Global Search Strategies in Evolutionary GRID-based Conformational Sampling & Docking
Horvath, Dragos; Brillet, Lorraine; Roy, Sylvaine et al

in 2009 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-5 (2009)

Conformational sampling, the computational prediction of the experimental geometries of small proteins (folding) or of protein-ligand complexes (docking), is often cited as one of the most challenging ... [more ▼]

Conformational sampling, the computational prediction of the experimental geometries of small proteins (folding) or of protein-ligand complexes (docking), is often cited as one of the most challenging multimodal optimization problems. Due to the extreme ruggedness of the energy landscape as a function of geometry, sampling heuristics must rely on an appropriate trade-off between global and local searching efforts. A previously reported "planetary strategy", a generalization of the classical island model used to deploy a hybrid genetic algorithm on computer grids, has shown a good ability to quickly discover low-energy geometries of small proteins and sugars, and sometimes even pinpoint their native structures - although not reproducibly. The procedure focused on broad exploration and used a tabu strategy to avoid revisiting the neighborhood of known solutions, at the risk of "burying" important minima in overhastily set tabu areas. The strategy reported here, termed "divide-and-conquer planetary model" couples this global search procedure to a local search tool. Grid nodes are now shared between global and local exploration tasks. The phase space is cut into "cells" corresponding to a specified sampling width for each of the N degrees of freedom. Global search locates cells containing low-energy geometries. Local searches pinpoint even deeper minima within a cell. Sampling width controls the important trade-off between the number of cells and the local search effort needed to reproducibly sample each cell. The probability to submit a cell to local search depends on the energy of the most stable geometry found within. Local searches are allotted limited resources and are not expected to converge. However, as long as they manage to discover some deeper local minima, the explored cell remains eligible for further local search, now relying on the improved energy level to enhance chances to be picked again. This competition prevents the system to waste too much effort in fruitless local searches. Eventually, after a limited number of local searches, a cell will be "closed" and used - first as "seed", later as tabu zone - to bias future global searches. Technical details and some folding and docking results will be discussed. [less ▲]

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See detailGRID-BASED CONFORMATIONAL SAMPLING
Parent, Benjamin; Tantar, Alexandru-Adrian UL; Melab, Nouredine et al

in STUDIA UNIVERSITATIS BABES-BOLYAI CHEMIA (2008), 53(2), 43-48

Computational simulations of conformational sampling in general, and of macromolecular folding in particular represent one of the most important and yet one of the most challenging applications of ... [more ▼]

Computational simulations of conformational sampling in general, and of macromolecular folding in particular represent one of the most important and yet one of the most challenging applications of computer science in biology and medicinal chemistry. This paper presents a massively parallel GRID-based conformational sampling strategy, exploring the extremely rugged energy response surface in function of molecular geometry, in search of low energy zones through phase spaces of hundreds of degrees of freedom. We have generalized the classical island model deployment of Genetic Algorithms (GA) to a "planetary" model where each node of the GRID is assimilated to a "planet" harboring quasi-independent multi-island simulations using a hybrid GA. Although different "planets" do not communicate to each other-thus minimizing inter-CPU exchanges on the GRID-each new simulation will benefit from the preliminary knowledge of already visited geometries, located on the dispatcher machine, and which are disseminated to any new "planet". This "panspermic" strategy allows new simulations to be conducted such as to either be attracted towards an apparently promising phase space zone (biasing strategies, intensification procedures) or to avoid already in-depth sampled (tabu) areas. Successful all-atom folding of mini-proteins typically used in benchmarks (the Trp cage 1L2Y and the Trp zipper 1LE1) has been observed, although the reproducibility of these highly stochastic simulations in huge problem spaces is still in need of improvement. [less ▲]

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See detailDocking and biomolecular simulations on computer grids: Status and trends
Tantar, Alexandru-Adrian UL; Conilleau, Sebastien; Parent, Benjamin et al

in Current Computer-Aided Drug Design (2008), 4(3), 235-249

This article outlines the recent developments in the field of large-scale parallel computing applied to molecular simulations, also including some original, preliminary contributions of the authors. It is ... [more ▼]

This article outlines the recent developments in the field of large-scale parallel computing applied to molecular simulations, also including some original, preliminary contributions of the authors. It is not meant to be an exhaustive review paper, but rather an introductive material aimed at narrowing the "cultural gap" between the developers and users of molecular simulations (chemists, medicinal chemists and biologists-typical workstation users) and the informatics experts in massively parallel computing. The article starts with a brief overview of the existing molecular simulation techniques, in emphasizing the weaknesses of present approaches and the need for more computer-intensive methods. Docking procedures are the most discussed, given the high importance of this application in computer-aided drug design. An introduction to computer grids is logically pursued with the presentation of some of the most promising large-scale parallel molecular simulations already performed. Eventually, the author's own research program, Docking@Grid, is briefly discussed. [less ▲]

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See detailA grid-based genetic algorithm combined with an adaptive simulated annealing for protein structure prediction
Tantar, Alexandru-Adrian UL; Melab, Nouredine; Talbi, El-Ghazali

in SOFT COMPUTING (2008), 12(12), 1185-1198

A 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 ... [more ▼]

A 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 alpha-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. [less ▲]

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See detailThe Influence of Mutation on Protein-Ligand Docking Optimization: A Locality Analysis
Tavares, Jorge; Tantar, Alexandru-Adrian UL; Melab, Nouredine et al

in LECTURE NOTES IN COMPUTER SCIENCE (2008), 5199

Evolutionary approaches to protein-ligand docking typically use a real-value encoding and mutation operators based on Gaussian and Cauchy distributions. The choice of mutation is important for an ... [more ▼]

Evolutionary approaches to protein-ligand docking typically use a real-value encoding and mutation operators based on Gaussian and Cauchy distributions. The choice of mutation is important for an efficient algorithm for this problem. We investigate the effect of mutation operators by locality analysis. High locality means that small variations in the genotype imply small variations in the phenotype. Results show that Gaussian-based operators have stronger locality than Cauchy-based ones, especially if an annealing scheme is used to control the variance. [less ▲]

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See detailGrid-based evolutionary strategies applied to the conformational sampling problem
Parent, Benjamin; Tantar, Alexandru-Adrian UL; Melab, Nouredine et al

in 2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS (2007)

Computational simulations of conformational sampling in general, and of macromolecular folding in particular represent one of the most important and yet one of the most challenging applications of ... [more ▼]

Computational simulations of conformational sampling in general, and of macromolecular folding in particular represent one of the most important and yet one of the most challenging applications of computer science in biology and medicinal chemistry. The advent of GRID computing may trigger some major progress in this field. This paper presents our first attempts to design GRID-based conformational sampling strategies, exploring the extremely rugged energy response surface in function of molecular geometry, in search of low energy zones through phase spaces of hundreds of degrees of freedom. We have generalized the classical island model deployment of Genetic Algorithms (GA) to a "planetary" model where each node of the grid is assimilated to a "planet" harboring quasi-independent multi-island simulations based on a hybrid GA-driven sampling approach. Although different "planets" do not communicate to each other - thus minimizing inter-CPU exchanges on the GRID - each new simulation will benefit from the preliminary knowledge extracted from the centralized pool of already visited geometries, located on the dispatcher machine, and which is disseminated to any new "planet". This "panspermic" strategy allows new simulations to be conducted such as to either be attracted towards an apparently promising phase space zone (biasing strategies, intensification procedures) or to avoid already in-depth sampled (tabu) areas. Successful folding of mini-proteins typically used in benchmarks for all-atoms protein simulations has been observed, although the reproducibility of these highly stochastic simulations in huge problem spaces is still in need of improvement. Work on two structured peptides (the "tryptophane cage" 1L2Y and the "tryptophane zipper" 1LE1) used as benchmarks for all-atom protein folding simulations has shown that the planetary model is able to reproducibly sample conformers from the neighborhood of the native geometries. However, within these neighborhoods (within ensembles of conformers similar to models published on hand of experimental geometry determinations), the energy landscapes are still extremely rugged. Therefore, simulations in general produce "correct" geometries (similar enough to experimental model for any practical purposes) which sometimes unfortunately correspond to relatively high energy levels and therefore are less stable than the most stable among misfolded conformers. The method thus reproducibly visits the native phase space zone, but fails to reproducibly hit the bottom of its rugged energy well. Intensifications of local sampling may in principle solve this problematic behavior, but is limited by computational ressources. The quest for the optimal time point at which a phase space zone should stop being intensively searched and declared tabu, a very difficult problem, is still awaiting for a practically useful solution. [less ▲]

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See detailA parallel hybrid genetic algorithm for protein structure prediction on the computational grid
Tantar, Alexandru-Adrian UL; Melab, N.; Talbi, E.-G. et al

in Future Generation Computer Systems (2007), 23(3), 398-409

Solving the structure prediction problem for complex proteins is difficult and computationally expensive. In this paper, we propose a bicriterion parallel hybrid genetic algorithm (GA) in order to ... [more ▼]

Solving the structure prediction problem for complex proteins is difficult and computationally expensive. In this paper, we propose a bicriterion parallel hybrid genetic algorithm (GA) in order to efficiently deal with the problem using the computational grid. The use of a near-optimal metaheuristic, such as a GA, allows a significant reduction in the number of explored potential structures. However, the complexity of the 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. A conjugated gradient-based Hill Climbing local search is combined with the GA in order to intensify the search in the neighborhood of its provided configurations. In this paper we consider two molecular complexes: the tryptophan-cage protein (Brookhaven Protein Data Bank ID 1L2Y) and alpha-cyclodextrin. The experimentation results obtained on a computational grid show the effectiveness of the approach. (c) 2006 Elsevier B.V. All rights reserved. [less ▲]

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