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Local versus global search strategies in evolutionary grid-based conformational and docking

hal.structure.identifierUniversité de Strasbourg [UNISTRA]
dc.contributor.authorHORVÁTH, Dragos
dc.contributor.authorBRILLET, Lorraine
dc.contributor.authorROY, Sylvaine
hal.structure.identifierUnité de Biotechnologie, Biocatalyse et Biorégulation [U3B]
dc.contributor.authorCONILLEAU, Sébastien
hal.structure.identifierAdvanced Learning Evolutionary Algorithms [ALEA]
dc.contributor.authorTANTAR, Alexandru-Adrian
hal.structure.identifierParallel Cooperative Multi-criteria Optimization [DOLPHIN]
dc.contributor.authorBOISSON, Jean-Charles
hal.structure.identifierParallel Cooperative Multi-criteria Optimization [DOLPHIN]
dc.contributor.authorMELAB, Nouredine
hal.structure.identifierParallel Cooperative Multi-criteria Optimization [DOLPHIN]
dc.contributor.authorTALBI, El-Ghazali
dc.date.issued2009-05-14
dc.date.conference2009-05-18
dc.description.abstractEnConformational 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 ldquoplanetary strategyrdquo, 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 ldquoburyingrdquo important minima in overhastily set tabu areas. The strategy reported here, termed ldquodivide-and-conquer planetary modelrdquo 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 ldquocellsrdquo 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 ldquoclosedrdquo and used - first as ldquoseedrdquo, later as tabu zone-to bias future global searches. Technical details and some folding and docking results will be discussed.
dc.language.isoen
dc.title.enLocal versus global search strategies in evolutionary grid-based conformational and docking
dc.typeCommunication dans un congrès
dc.identifier.doi10.1109/CEC.2009.4982955
dc.subject.halInformatique [cs]/Recherche opérationnelle [cs.RO]
bordeaux.page247-254
bordeaux.countryNO
bordeaux.conference.cityTrondheim
bordeaux.peerReviewedoui
hal.identifierhal-00837532
hal.version1
hal.invitednon
hal.proceedingsoui
hal.conference.end2009-05-21
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-00837532v1
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