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Drug target ontology to classify and integrate drug discovery data.
Lin, Yu; Mehta, Saurabh; Küçük-McGinty, Hande; Turner, John Paul; Vidovic, Dusica; Forlin, Michele; Koleti, Amar; Nguyen, Dac-Trung; Jensen, Lars Juhl; Guha, Rajarshi; Mathias, Stephen L; Ursu, Oleg; Stathias, Vasileios; Duan, Jianbin; Nabizadeh, Nooshin; Chung, Caty; Mader, Christopher; Visser, Ubbo; Yang, Jeremy J; Bologa, Cristian G; Oprea, Tudor I; Schürer, Stephan C.
Afiliação
  • Lin Y; Center for Computational Science, University of Miami, Coral Gables, FL, USA.
  • Mehta S; Center for Computational Science, University of Miami, Coral Gables, FL, USA.
  • Küçük-McGinty H; Department of Applied Chemistry, Delhi Technological University, Delhi, India.
  • Turner JP; Center for Computational Science, University of Miami, Coral Gables, FL, USA.
  • Vidovic D; Department of Computer Science, University of Miami, Coral Gables, FL, USA.
  • Forlin M; Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL, USA.
  • Koleti A; Center for Computational Science, University of Miami, Coral Gables, FL, USA.
  • Nguyen DT; Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL, USA.
  • Jensen LJ; Center for Computational Science, University of Miami, Coral Gables, FL, USA.
  • Guha R; Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL, USA.
  • Mathias SL; Center for Computational Science, University of Miami, Coral Gables, FL, USA.
  • Ursu O; National Center for Advancing Translational Science, Rockville, MD, USA.
  • Stathias V; Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
  • Duan J; National Center for Advancing Translational Science, Rockville, MD, USA.
  • Nabizadeh N; Department of Internal Medicine, Translational Informatics Division, University of New Mexico School of Medicine, Albuquerque, NM, USA.
  • Chung C; Department of Internal Medicine, Translational Informatics Division, University of New Mexico School of Medicine, Albuquerque, NM, USA.
  • Mader C; Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL, USA.
  • Visser U; Center for Computational Science, University of Miami, Coral Gables, FL, USA.
  • Yang JJ; Department of Computer Science, University of Miami, Coral Gables, FL, USA.
  • Bologa CG; Center for Computational Science, University of Miami, Coral Gables, FL, USA.
  • Oprea TI; Center for Computational Science, University of Miami, Coral Gables, FL, USA.
  • Schürer SC; Center for Computational Science, University of Miami, Coral Gables, FL, USA.
J Biomed Semantics ; 8(1): 50, 2017 Nov 09.
Article em En | MEDLINE | ID: mdl-29122012
ABSTRACT

BACKGROUND:

One of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome.

RESULTS:

As part of that effort, we have developed a framework to integrate, navigate, and analyze drug discovery data based on formalized and standardized classifications and annotations of druggable protein targets, the Drug Target Ontology (DTO). DTO was constructed by extensive curation and consolidation of various resources. DTO classifies the four major drug target protein families, GPCRs, kinases, ion channels and nuclear receptors, based on phylogenecity, function, target development level, disease association, tissue expression, chemical ligand and substrate characteristics, and target-family specific characteristics. The formal ontology was built using a new software tool to auto-generate most axioms from a database while supporting manual knowledge acquisition. A modular, hierarchical implementation facilitate ontology development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships.

CONCLUSIONS:

DTO was built based on the need for a formal semantic model for druggable targets including various related information such as protein, gene, protein domain, protein structure, binding site, small molecule drug, mechanism of action, protein tissue localization, disease association, and many other types of information. DTO will further facilitate the otherwise challenging integration and formal linking to biological assays, phenotypes, disease models, drug poly-pharmacology, binding kinetics and many other processes, functions and qualities that are at the core of drug discovery. The first version of DTO is publically available via the website http//drugtargetontology.org/ , Github ( http//github.com/DrugTargetOntology/DTO ), and the NCBO Bioportal ( http//bioportal.bioontology.org/ontologies/DTO ). The long-term goal of DTO is to provide such an integrative framework and to populate the ontology with this information as a community resource.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Sistemas de Liberação de Medicamentos / Biologia Computacional / Descoberta de Drogas / Ontologias Biológicas Tipo de estudo: Guideline / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2017 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Sistemas de Liberação de Medicamentos / Biologia Computacional / Descoberta de Drogas / Ontologias Biológicas Tipo de estudo: Guideline / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2017 Tipo de documento: Article