GLASS: A comprehensive database for experimentally validated GPCR-ligand associations

Bioinformatics

Volumn 31, Issue 18, 2015, Pages 3035-3042

  Chan, Wallace K B ^a   Zhang, Hongjiu ^b   Yang, Jianyi ^b   Brender, Jeffrey R ^b   Hur, Junguk ^c   Ozgur, Arzucan ^d   Zhang, Yang ^a,b  

^a Department of Biological Chemistry ^*  (United States)

^b UNIVERSITY OF MICHIGAN   (United States)

^c UNIVERSITY OF NORTH DAKOTA SCHOOL OF MEDICINE AND HEALTH SCIENCES   (United States)

^d BOGAZICI UNIVERSITY   (Turkey)

Author keywords

[No Author keywords available]

Indexed keywords

G PROTEIN COUPLED RECEPTOR; LIGAND; PROTEIN BINDING;

ALGORITHM; CHEMISTRY; DATA MINING; HUMAN; INTERNET; METABOLISM; MOLECULAR MODEL; PROTEIN DATABASE;

ALGORITHMS; DATA MINING; DATABASES, PROTEIN; HUMANS; INTERNET; LIGANDS; MODELS, MOLECULAR; PROTEIN BINDING; RECEPTORS, G-PROTEIN-COUPLED;

EID: 84941789777     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btv302     Document Type: Article

References (31)

1. Beukers, M. W. et al. (1999) TinyGRAP database: a bioinformatics tool to mine G-protein-coupled receptor mutant data. Trends Pharmacol. Sci., 20, 475-477.
2. Bienfait, B. and Ertl, P. (2013) JSME: a free molecule editor in JavaScript. J. Cheminf., 5, 24.
3. Cherezov, V. et al. (2007) High-resolution crystal structure of an engineered human beta2-adrenergic G protein-coupled receptor. Science, 318, 1258-1265.
4. Cock, P. J. et al. (2009) Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics, 25, 1422-1423.
5. Dorsam, R. T. and Gutkind, J. S. (2007) G-protein-coupled receptors and cancer. Nat. Rev. Cancer, 7, 79-94.
6. Erkan, G. et al. (2007) Semi-supervised classification for extracting protein interaction sentences using dependency parsing. EMNLP-CoNLL, 228-237.
7. Friedman, J. H. (2001) Greedy function approximation: a gradient boosting machine. Ann. Statist., 29, 1189-1232.
8. Gatica, E. A. and Cavasotto, C. N. (2012) Ligand and decoy sets for docking to G protein-coupled receptors. J. Chem. Inf. Model., 52, 1-6.
9. Gaulton, A. et al. (2012) ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res., 40, D1100-1107.
10. Gray, K. A. et al. (2014) Genenames. org: the HGNC resources in 2015. Nucleic Acids Res., gku1071.
11. Horn, F. et al. (1998) GPCRDB: an information system for G protein-coupled receptors. Nucleic Acids Res., 26, 275-279.
12. Hur, J. et al. (2009) SciMiner: web-based literature mining tool for target identification and functional enrichment analysis. Bioinformatics, 25, 838-840.
13. Khelashvili, G. et al. (2010) GPCR-OKB: the G protein coupled receptor oligomer knowledge base. Bioinformatics, 26, 1804-1805.
14. Klabunde, T. and Hessler, G. (2002) Drug design strategies for targeting G protein coupled receptors. Chembiochem, 3, 928-944.
15. Knox, C. et al. (2011) DrugBank 3. 0: a comprehensive resource for 'omics' research on drugs. Nucleic Acids Res., 39, D1035-1041.
16. Lipinski, C. A. et al. (2001) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Delivery Rev., 46, 3-26.
17. Liu, T. et al. (2007) BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res., 35, D198-D201.
18. Maglott, D. et al. (2011) Entrez Gene: gene-centered information at NCBI. Nucleic Acids Res., 39, D52-D57.
19. Magrane, M. and Consortium, U. (2011) UniProt Knowledgebase: a hub of integrated protein data. Database, 2011, bar009.
20. Manning, C. D. et al. (2014) The Stanford CoreNLP natural language processing toolkit. In: Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55-60.
21. O'Boyle, N. M. et al. (2011) Open Babel: an open chemical toolbox. J. Cheminf., 3, 33.
22. Okuno, Y. et al. (2008) GLIDA: GPCR-ligand database for chemical genomics drug discovery-database and tools update. Nucleic Acids Res., 36, D907-D912.
23. Overington, J. P. et al. (2006) How many drug targets are there? Nat. Rev. Drug Discov., 5, 993-996.
24. Ozgur, A. and Radev, D. R. (2009) Supervised classification for extracting biomedical events. In: Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task. Association for Computational Linguistics, pp. 111-114.
25. Qin, C. et al. (2014) Therapeutic target database update 2014: a resource for targeted therapeutics. Nucleic Acids Res., 42, D1118-1123.
26. Rocktaschel, T. et al. (2012) ChemSpot: a hybrid system for chemical named entity recognition. Bioinformatics, 28, 1633-1640.
27. Sharman, J. L. et al. (2011) IUPHAR-DB: new receptors and tools for easy searching and visualization of pharmacological data. Nucleic Acids Res., 39, D534-D538.
28. van Laarhoven, T. et al. (2011) Gaussian interaction profile kernels for predicting drug-target interaction. Bioinformatics, 27, 3036-3043.
29. Weill, N. and Rognan, D. (2009) Development and validation of a novel protein-ligand fingerprint to mine chemogenomic space: application to G protein-coupled receptors and their ligands. J. Chem. Inf. Model., 49, 1049-1062.
30. Zhang, J. and Zhang, Y. (2010) GPCRRD: G protein-coupled receptor spatial restraint database for 3D structure modeling and function annotation. Bioinformatics, 26, 3004-3005.
31. Zhou, H. and Skolnick, J. (2012) FINDSITE(X): a structure-based, small molecule virtual screening approach with application to all identified human GPCRs. Mol. Pharm., 9, 1775-1784.