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Histone deacetylase inhibitors: structure-based modeling and isoform-selectivity prediction.
Rome Center for Molecular Design Dipartimento di Chimica e Tecnologie del Farmaco, Facolta ̀ di Farmacia e Medicina.
Rome Center for Molecular Design Dipartimento di Chimica e Tecnologie del Farmaco, Facolta ̀ di Farmacia e Medicina.ORCID iD: 0000-0002-4831-3423
Istituto Pasteur - Fondazione Cenci Bolognetti Dipartimento di Chimica e Tecnologie del Farmaco, Facolta ̀ di Farmacia e Medicina.
Rome Center for Molecular Design Dipartimento di Chimica e Tecnologie del Farmaco, Facolta ̀ di Farmacia e Medicina; Visiting Professor from the Department of Biochemistry and Molecular Biophysics, Wash ington University School of Medicine, St. Louis, Missouri 63110, United States.
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2012 (English)In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 52, no 8, 2215-35 p.Article in journal (Refereed) Published
Abstract [en]

An enhanced version of comparative binding energy (COMBINE) analysis, named COMBINEr, based on both ligand-based and structure-based alignments has been used to build several 3-D QSAR models for the eleven human zinc-based histone deacetylases (HDACs). When faced with an abundance of data from diverse structure-activity sources, choosing the best paradigm for an integrative analysis is difficult. A common example from studies on enzyme-inhibitors is the abundance of crystal structures characterized by diverse ligands complexed with different enzyme isoforms. A novel comprehensive tool for data mining on such inhomogeneous set of structure-activity data was developed based on the original approach of Ortiz, Gago, and Wade, and applied to predict HDAC inhibitors' isoform selectivity. The COMBINEr approach (apart from the AMBER programs) has been developed to use only software freely available to academics.

Place, publisher, year, edition, pages
American Chemical Society (ACS), 2012. Vol. 52, no 8, 2215-35 p.
Keyword [en]
Histone Deacetylases, HDAC, 3D QSAR, Structure Based Drug Design, COMBINEr, Molecular Docking
National Category
Medicinal Chemistry Chemical Sciences Computer and Information Science
Identifiers
URN: urn:nbn:se:uu:diva-321307DOI: 10.1021/ci300160yPubMedID: 22762501OAI: oai:DiVA.org:uu-321307DiVA: diva2:1094958
Available from: 2017-05-11 Created: 2017-05-11 Last updated: 2017-05-12

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