Drug target ontology to classify and integrate drug discovery data

Yu Lin, Saurabh Mehta, Hande Küçük-McGinty, John Paul Turner, Dusica Vidovic, Michele Forlin, Amar Koleti, Dac Trung Nguyen, Lars Juhl Jensen, Rajarshi Guha, Stephen L. Mathias, Oleg Ursu, Vasileios Stathias, Jianbin Duan, Nooshin Nabizadeh, Caty Chung, Christopher Mader, Ubbo E Visser, Jeremy J. Yang, Cristian G. BologaTudor I. Oprea, Stephan C Schuerer

Research output: Contribution to journalArticle

6 Citations (Scopus)

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.

Original languageEnglish (US)
Article number50
JournalJournal of Biomedical Semantics
Volume8
Issue number1
DOIs
StatePublished - Nov 9 2017

Fingerprint

Drug Discovery
Ontology
Pharmaceutical Preparations
Proteins
Genes
Genome
Biological Ontologies
Molecular Sequence Annotation
Association reactions
Tissue
Molecules
Cytoplasmic and Nuclear Receptors
Data integration
Knowledge acquisition
Ion Channels
Semantics
Protein Binding
Binding sites
Biological Assay

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Health Informatics
  • Computer Networks and Communications

Cite this

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 E; Yang, Jeremy J.; Bologa, Cristian G.; Oprea, Tudor I.; Schuerer, Stephan C.

In: Journal of Biomedical Semantics, Vol. 8, No. 1, 50, 09.11.2017.

Research output: Contribution to journalArticle

Lin, Y, Mehta, S, Küçük-McGinty, H, Turner, JP, Vidovic, D, Forlin, M, Koleti, A, Nguyen, DT, Jensen, LJ, Guha, R, Mathias, SL, Ursu, O, Stathias, V, Duan, J, Nabizadeh, N, Chung, C, Mader, C, Visser, UE, Yang, JJ, Bologa, CG, Oprea, TI & Schuerer, SC 2017, 'Drug target ontology to classify and integrate drug discovery data', Journal of Biomedical Semantics, vol. 8, no. 1, 50. https://doi.org/10.1186/s13326-017-0161-x
Lin Y, Mehta S, Küçük-McGinty H, Turner JP, Vidovic D, Forlin M et al. Drug target ontology to classify and integrate drug discovery data. Journal of Biomedical Semantics. 2017 Nov 9;8(1). 50. https://doi.org/10.1186/s13326-017-0161-x
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 E ; Yang, Jeremy J. ; Bologa, Cristian G. ; Oprea, Tudor I. ; Schuerer, Stephan C. / Drug target ontology to classify and integrate drug discovery data. In: Journal of Biomedical Semantics. 2017 ; Vol. 8, No. 1.
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T1 - Drug target ontology to classify and integrate drug discovery data

AU - Lin, Yu

AU - Mehta, Saurabh

AU - Küçük-McGinty, Hande

AU - Turner, John Paul

AU - Vidovic, Dusica

AU - Forlin, Michele

AU - Koleti, Amar

AU - Nguyen, Dac Trung

AU - Jensen, Lars Juhl

AU - Guha, Rajarshi

AU - Mathias, Stephen L.

AU - Ursu, Oleg

AU - Stathias, Vasileios

AU - Duan, Jianbin

AU - Nabizadeh, Nooshin

AU - Chung, Caty

AU - Mader, Christopher

AU - Visser, Ubbo E

AU - Yang, Jeremy J.

AU - Bologa, Cristian G.

AU - Oprea, Tudor I.

AU - Schuerer, Stephan C

PY - 2017/11/9

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N2 - 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.

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