Drug target ontology to classify and integrate drug discovery data
| Autor: | Dusica Vidovic, Cristian Bologa, Christopher Mader, Jeremy J. Yang, Stephan C. Schürer, Tudor I. Oprea, Vasileios Stathias, Lars Juhl Jensen, Stephen L. Mathias, Yu Lin, Hande Küçük-McGinty, Nooshin Nabizadeh, Saurabh Mehta, Oleg Ursu, Rajarshi Guha, Michele Forlin, Dac-Trung Nguyen, Ubbo Visser, Jianbin Duan, Amar Koleti, John Paul Turner, Caty Chung |
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| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Computer science Drug target Druggability Ontology (information science) Genome Data modeling Drug Delivery Systems 0302 clinical medicine Protein structure Drug Discovery Bioassay 0303 health sciences Drug discovery Small molecule Receptor–ligand kinetics Computer Science Applications Semantics Formal ontology 030220 oncology & carcinogenesis lcsh:R858-859.7 medicine.symptom Protein target Information Systems Protein family Computer Networks and Communications Protein domain Health Informatics Computational biology lcsh:Computer applications to medicine. Medical informatics Semantic data model Open Biomedical Ontologies World Wide Web 03 medical and health sciences medicine Journal Article Humans Binding site Gene 030304 developmental biology Ligand Research Computational Biology Proteins Substrate (chemistry) Biological Ontologies Knowledge acquisition 030104 developmental biology Mechanism of action Nuclear receptor Software |
| Zdroj: | Lin, Y, Mehta, S, Küçük-McGinty, H, Turner, J P, Vidovic, D, Forlin, M, Koleti, A, Nguyen, D-T, Jensen, L J, Guha, R, Mathias, S L, Ursu, O, Stathias, V, Duan, J, Nabizadeh, N, Chung, C, Mader, C, Visser, U, Yang, J J, Bologa, C G, Oprea, T I & Schürer, S C 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 Journal of Biomedical Semantics Journal of Biomedical Semantics, Vol 8, Iss 1, Pp 1-16 (2017) |
| Popis: | BackgroundOne 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.ResultsAs part of that effort, we have been developing 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 also supporting manual knowledge acquisition. A modular, hierarchical implementation facilitates 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.ConclusionsDTO 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 websitehttp://drugtargetontology.org/, Github (https://github.com/DrugTargetOntology/DTO), and the NCBO Bioportal (https://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. |
| Databáze: | OpenAIRE |
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