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dc.contributor.authorDíaz, Lucía-
dc.contributor.authorBujons Vilàs, Jordi-
dc.contributor.authorDelgado, Antonio-
dc.contributor.authorGutiérrez-de-Terán, Hugo-
dc.contributor.authorÅqvist, Johan-
dc.identifier.citationJournal of Chemical Information and Modelinges_ES
dc.description.abstractGlucocerebrosidase (GCase, acid β-Glucosidase) hydrolyzes the sphingolipid glucosylceramide into glucose and ceramide. Mutations in this enzyme lead to a lipid metabolism disorder known as Gaucher disease. The design of competitive inhibitors of GCase is a promising field of research for the design of pharmacological chaperones as new therapeutic agents. Using a series of recently reported molecules with experimental binding affinities for GCase in the nanomolar to micromolar range, we here report an extensive theoretical analysis of their binding mode. On the basis of molecular docking, molecular dynamics, and binding free energy calculations using the linear interaction energy method (LIE), we provide details on the molecular interactions supporting ligand binding in the different families of compounds. The applicability of other computational approaches, such as the COMBINE methodology, is also investigated. The results show the robustness of the standard parametrization of the LIE method, which reproduces the experimental affinities with a mean unsigned error of 0.7 kcal/mol. Several structure−activity relationships are established using the computational models here provided, including the identification of hot spot residues in the binding site. The models derived are envisaged as important tools in ligand-design programs for GCase inhibitors.es_ES
dc.description.sponsorshipFinancial support from the “Ministerio de Ciencia e Innovación”, Spain (Project CTQ2008-01426/BQU) and “Generalitat de Catalunya” (Grant 2009SGR-1072) is acknowledged. J.Å. acknowledges support from the Swedish Research Council (VR). L.D. is grateful to CSIC for predoctoral research training support within the JAE-Predoc program. H.G.T. is a researcher of the Isidro Parga Pondal program (Xunta de Galicia, Spain). Mr. Lars Boukharta is gratefully acknowledged for technical assistance and helpful discussions. Finally, the authors acknowledge the “Centre de Supercomputació de Catalunya” (CESCA) for allowing the use of its software and hardware resources.es_ES
dc.publisherAmerican Chemical Societyes_ES
dc.titleComputational Prediction of Structure−Activity Relationships for the Binding of Aminocyclitols to β-Glucocerebrosidasees_ES
dc.description.peerreviewedPeer reviewedes_ES
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