Suppr超能文献

G 蛋白偶联受体本体论:G 蛋白偶联受体药理学知识框架的开发与应用。

GPCR ontology: development and application of a G protein-coupled receptor pharmacology knowledge framework.

机构信息

Center for Computational Science, University of Miami, Miami, FL 33136, USA and Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA.

出版信息

Bioinformatics. 2013 Dec 15;29(24):3211-9. doi: 10.1093/bioinformatics/btt565. Epub 2013 Sep 29.

Abstract

MOTIVATION

Novel tools need to be developed to help scientists analyze large amounts of available screening data with the goal to identify entry points for the development of novel chemical probes and drugs. As the largest class of drug targets, G protein-coupled receptors (GPCRs) remain of particular interest and are pursued by numerous academic and industrial research projects.

RESULTS

We report the first GPCR ontology to facilitate integration and aggregation of GPCR-targeting drugs and demonstrate its application to classify and analyze a large subset of the PubChem database. The GPCR ontology, based on previously reported BioAssay Ontology, depicts available pharmacological, biochemical and physiological profiles of GPCRs and their ligands. The novelty of the GPCR ontology lies in the use of diverse experimental datasets linked by a model to formally define these concepts. Using a reasoning system, GPCR ontology offers potential for knowledge-based classification of individuals (such as small molecules) as a function of the data.

AVAILABILITY

The GPCR ontology is available at http://www.bioassayontology.org/bao_gpcr and the National Center for Biomedical Ontologies Web site.

摘要

动机

需要开发新的工具来帮助科学家分析大量可用的筛选数据,以确定开发新型化学探针和药物的切入点。作为最大的一类药物靶点,G 蛋白偶联受体(GPCR)仍然是特别关注的对象,并被众多学术和工业研究项目所追求。

结果

我们报告了第一个 GPCR 本体论,以促进 GPCR 靶向药物的整合和聚集,并展示了其在对大型 PubChem 数据库子集进行分类和分析中的应用。该 GPCR 本体论基于先前报道的生物测定学本体论,描述了 GPCR 及其配体的现有药理学、生物化学和生理学特征。GPCR 本体论的新颖之处在于使用多种实验数据集通过模型链接来正式定义这些概念。通过推理系统,GPCR 本体论为基于数据的个体(如小分子)的知识分类提供了可能性。

可用性

GPCR 本体论可在 http://www.bioassayontology.org/bao_gpcr 和国家生物医学本体论中心网站上获得。

相似文献

1
GPCR ontology: development and application of a G protein-coupled receptor pharmacology knowledge framework.
Bioinformatics. 2013 Dec 15;29(24):3211-9. doi: 10.1093/bioinformatics/btt565. Epub 2013 Sep 29.
3
Requirements and ontology for a G protein-coupled receptor oligomerization knowledge base.
BMC Bioinformatics. 2007 May 30;8:177. doi: 10.1186/1471-2105-8-177.
4
GLIDA: GPCR-ligand database for chemical genomic drug discovery.
Nucleic Acids Res. 2006 Jan 1;34(Database issue):D673-7. doi: 10.1093/nar/gkj028.
5
GPCR-OKB: the G Protein Coupled Receptor Oligomer Knowledge Base.
Bioinformatics. 2010 Jul 15;26(14):1804-5. doi: 10.1093/bioinformatics/btq264. Epub 2010 May 25.
6
GLIDA: GPCR--ligand database for chemical genomics drug discovery--database and tools update.
Nucleic Acids Res. 2008 Jan;36(Database issue):D907-12. doi: 10.1093/nar/gkm948. Epub 2007 Nov 5.
8
Allosteric Modulation of Class A GPCRs: Targets, Agents, and Emerging Concepts.
J Med Chem. 2019 Jan 10;62(1):88-127. doi: 10.1021/acs.jmedchem.8b00875. Epub 2018 Aug 28.
9
Using silico methods predicting ligands for orphan GPCRs.
Curr Protein Pept Sci. 2006 Oct;7(5):459-64. doi: 10.2174/138920306778559359.
10
Computational approaches for ligand discovery and design in class-A G protein- coupled receptors.
Curr Pharm Des. 2013;19(12):2216-36. doi: 10.2174/1381612811319120009.

引用本文的文献

1
How to Develop a Drug Target Ontology: KNowledge Acquisition and Representation Methodology (KNARM).
Methods Mol Biol. 2019;1939:49-69. doi: 10.1007/978-1-4939-9089-4_4.
2
Drug target ontology to classify and integrate drug discovery data.
J Biomed Semantics. 2017 Nov 9;8(1):50. doi: 10.1186/s13326-017-0161-x.
3
Utilizing a structural meta-ontology for family-based quality assurance of the BioPortal ontologies.
J Biomed Inform. 2016 Jun;61:63-76. doi: 10.1016/j.jbi.2016.03.007. Epub 2016 Mar 14.
4
Targeting the IGF-1R: The Tale of the Tortoise and the Hare.
Front Endocrinol (Lausanne). 2015 Apr 27;6:64. doi: 10.3389/fendo.2015.00064. eCollection 2015.
5
Evolving BioAssay Ontology (BAO): modularization, integration and applications.
J Biomed Semantics. 2014 Jun 3;5(Suppl 1 Proceedings of the Bio-Ontologies Spec Interest G):S5. doi: 10.1186/2041-1480-5-S1-S5. eCollection 2014.

本文引用的文献

1
The Semanticscience Integrated Ontology (SIO) for biomedical research and knowledge discovery.
J Biomed Semantics. 2014 Mar 6;5(1):14. doi: 10.1186/2041-1480-5-14.
3
Structure of the δ-opioid receptor bound to naltrindole.
Nature. 2012 May 16;485(7398):400-4. doi: 10.1038/nature11111.
4
Structure of the nociceptin/orphanin FQ receptor in complex with a peptide mimetic.
Nature. 2012 May 16;485(7398):395-9. doi: 10.1038/nature11085.
5
Structure of the human κ-opioid receptor in complex with JDTic.
Nature. 2012 Mar 21;485(7398):327-32. doi: 10.1038/nature10939.
6
Crystal structure of the µ-opioid receptor bound to a morphinan antagonist.
Nature. 2012 Mar 21;485(7398):321-6. doi: 10.1038/nature10954.
7
Systems chemical biology and the Semantic Web: what they mean for the future of drug discovery research.
Drug Discov Today. 2012 May;17(9-10):469-74. doi: 10.1016/j.drudis.2011.12.019. Epub 2011 Dec 29.
8
Refining efficacy: exploiting functional selectivity for drug discovery.
Adv Pharmacol. 2011;62:79-107. doi: 10.1016/B978-0-12-385952-5.00009-9.
9
BioAssay Ontology (BAO): a semantic description of bioassays and high-throughput screening results.
BMC Bioinformatics. 2011 Jun 24;12:257. doi: 10.1186/1471-2105-12-257.
10
BioAssay ontology annotations facilitate cross-analysis of diverse high-throughput screening data sets.
J Biomol Screen. 2011 Apr;16(4):415-26. doi: 10.1177/1087057111400191.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验