Reference : Local vs. Global Search Strategies in Evolutionary GRID-based Conformational Sampling...
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Local vs. Global Search Strategies in Evolutionary GRID-based Conformational Sampling & Docking
Horvath, Dragos [Univ Strasbourg, CNRS, UMR 7177, Lab Infochim, Strasbourg, France.]
Brillet, Lorraine [> >]
Roy, Sylvaine [> >]
Conilleau, Sebastien [> >]
Tantar, Alexandru-Adrian mailto [INRIA Futurs -- LIFL/CNRS UMR 8022]
Boisson, Jean-Charles [> >]
Melab, Nouredine [> >]
Talbi, El-Ghazali [> >]
IEEE Congress on Evolutionary Computation
New York
IEEE Congress on Evolutionary Computation
MAY 18-21, 2009
[en] 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.

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