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dc.contributor.authorZarbafian, Shahroozen_US
dc.contributor.authorMoghadasi, Mohammaden_US
dc.contributor.authorRoshandelpoor, Atharen_US
dc.contributor.authorNan, Fengen_US
dc.contributor.authorLi, Keyongen_US
dc.contributor.authorVakili, Piroozen_US
dc.contributor.authorVajda, Sandoren_US
dc.contributor.authorKozakov, Dmytroen_US
dc.contributor.authorPaschalidis, Ioannis Ch.en_US
dc.date2018-03-21
dc.date.accessioned2020-01-10T15:00:22Z
dc.date.available2020-01-10T15:00:22Z
dc.date.issued2018-04-12
dc.identifier.citationShahrooz Zarbafian, Mohammad Moghadasi, Athar Roshandelpoor, Feng Nan, Keyong Li, Pirooz Vakili, Sandor Vajda, Dima Kozakov, Ioannis Ch Paschalidis. 2018. "Protein docking refinement by convex underestimation in the low-dimensional subspace of encounter complexes." Scientific Reports, Volume 8, Issue 1, https://doi.org/10.1038/s41598-018-23982-3
dc.identifier.urihttps://hdl.handle.net/2144/39074
dc.description.abstractWe propose a novel stochastic global optimization algorithm with applications to the refinement stage of protein docking prediction methods. Our approach can process conformations sampled from multiple clusters, each roughly corresponding to a different binding energy funnel. These clusters are obtained using a density-based clustering method. In each cluster, we identify a smooth “permissive” subspace which avoids high-energy barriers and then underestimate the binding energy function using general convex polynomials in this subspace. We use the underestimator to bias sampling towards its global minimum. Sampling and subspace underestimation are repeated several times and the conformations sampled at the last iteration form a refined ensemble. We report computational results on a comprehensive benchmark of 224 protein complexes, establishing that our refined ensemble significantly improves the quality of the conformations of the original set given to the algorithm. We also devise a method to enhance the ensemble from which near-native models are selected.en_US
dc.language.isoen_US
dc.relation.ispartofScientific Reports
dc.rightsAttribution 4.0 Internationalen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectScience & technologyen_US
dc.subjectMultidisciplinary sciencesen_US
dc.subjectParamagnetic relaxation enhancementen_US
dc.subjectEnergy landscapeen_US
dc.subjectMolecular dockingen_US
dc.subjectFolding funnelsen_US
dc.subjectBindingen_US
dc.subjectAssociationen_US
dc.subjectConformationsen_US
dc.subjectOptimizationsen_US
dc.subjectMinimizationen_US
dc.subjectRecognitionen_US
dc.subjectAlgorithmsen_US
dc.subjectAnimalsen_US
dc.subjectAntibodiesen_US
dc.subjectBenchmarkingen_US
dc.subjectBinding sitesen_US
dc.subjectEnzymesen_US
dc.subjectHumansen_US
dc.subjectKineticsen_US
dc.subjectLigandsen_US
dc.subjectMolecular docking simulationen_US
dc.subjectMultifactor dimensionality reductionen_US
dc.subjectProtein bindingen_US
dc.subjectProtein conformationen_US
dc.subjectProtein interaction domains and motifsen_US
dc.subjectReceptors, cell surfaceen_US
dc.subjectThermodynamicsen_US
dc.titleProtein docking refinement by convex underestimation in the low-dimensional subspace of encounter complexesen_US
dc.typeArticleen_US
dc.description.versionPublished versionen_US
dc.identifier.doi10.1038/s41598-018-23982-3
pubs.elements-sourcemanual-entryen_US
pubs.notesin press optkey: optvolume: optnumber: optpages: optmonth:en_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Engineeringen_US
pubs.organisational-groupBoston University, College of Engineering, Department of Biomedical Engineeringen_US
pubs.organisational-groupBoston University, College of Engineering, Department of Electrical & Computer Engineeringen_US
pubs.organisational-groupBoston University, College of Engineering, Department of Mechanical Engineeringen_US
pubs.publication-statusPublisheden_US
dc.identifier.orcid0000-0002-3343-2913 (Paschalidis, Ioannis Ch)
dc.identifier.mycv359077


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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International