GPCR-GIA: a web-server for identifying G-protein coupled receptors and their families with grey incidence analysis
- PMID: 19776029
- DOI: 10.1093/protein/gzp057
GPCR-GIA: a web-server for identifying G-protein coupled receptors and their families with grey incidence analysis
Abstract
G-protein-coupled receptors (GPCRs) play fundamental roles in regulating various physiological processes as well as the activity of virtually all cells. Different GPCR families are responsible for different functions. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop an automated method to address the two problems: given the sequence of a query protein, can we identify whether it is a GPCR? If it is, what family class does it belong to? Here, a two-layer ensemble classifier called GPCR-GIA was proposed by introducing a novel scale called 'grey incident degree'. The overall success rate by GPCR-GIA in identifying GPCR and non-GPCR was about 95%, and that in identifying the GPCRs among their nine family classes was about 80%. These rates were obtained by the jackknife cross-validation tests on the stringent benchmark data sets where none of the proteins has > or = 50% pairwise sequence identity to any other in a same class. Moreover, a user-friendly web-server was established at http://218.65.61.89:8080/bioinfo/GPCR-GIA. For user's convenience, a step-by-step guide on how to use the GPCR-GIA web server is provided. Generally speaking, one can get the desired two-level results in around 10 s for a query protein sequence of 300-400 amino acids; the longer the sequence is, the more time that is needed.
Similar articles
-
GPCR-CA: A cellular automaton image approach for predicting G-protein-coupled receptor functional classes.J Comput Chem. 2009 Jul 15;30(9):1414-23. doi: 10.1002/jcc.21163. J Comput Chem. 2009. PMID: 19037861
-
GPCR-2L: predicting G protein-coupled receptors and their types by hybridizing two different modes of pseudo amino acid compositions.Mol Biosyst. 2011 Mar;7(3):911-9. doi: 10.1039/c0mb00170h. Epub 2010 Dec 23. Mol Biosyst. 2011. PMID: 21180772
-
QuatIdent: a web server for identifying protein quaternary structural attribute by fusing functional domain and sequential evolution information.J Proteome Res. 2009 Mar;8(3):1577-84. doi: 10.1021/pr800957q. J Proteome Res. 2009. PMID: 19226167
-
Proteomic applications of automated GPCR classification.Proteomics. 2007 Aug;7(16):2800-14. doi: 10.1002/pmic.200700093. Proteomics. 2007. PMID: 17639603 Review.
-
Bioinformatics tools for predicting GPCR gene functions.Adv Exp Med Biol. 2014;796:205-24. doi: 10.1007/978-94-007-7423-0_10. Adv Exp Med Biol. 2014. PMID: 24158807 Review.
Cited by
-
A knockout mutation of a constitutive GPCR in Tetrahymena decreases both G-protein activity and chemoattraction.PLoS One. 2011;6(11):e28022. doi: 10.1371/journal.pone.0028022. Epub 2011 Nov 29. PLoS One. 2011. PMID: 22140501 Free PMC article.
-
iNR-Drug: predicting the interaction of drugs with nuclear receptors in cellular networking.Int J Mol Sci. 2014 Mar 19;15(3):4915-37. doi: 10.3390/ijms15034915. Int J Mol Sci. 2014. PMID: 24651462 Free PMC article.
-
Application of density similarities to predict membrane protein types based on pseudo-amino acid composition.J Theor Biol. 2011 May 7;276(1):132-7. doi: 10.1016/j.jtbi.2011.01.048. Epub 2011 Feb 4. J Theor Biol. 2011. PMID: 21296088 Free PMC article.
-
iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou's PseAAC to formulate DNA samples.Mol Genet Genomics. 2016 Feb;291(1):285-96. doi: 10.1007/s00438-015-1108-5. Epub 2015 Aug 30. Mol Genet Genomics. 2016. PMID: 26319782
-
Classification of G-protein coupled receptors based on support vector machine with maximum relevance minimum redundancy and genetic algorithm.BMC Bioinformatics. 2010 Jun 16;11:325. doi: 10.1186/1471-2105-11-325. BMC Bioinformatics. 2010. PMID: 20550715 Free PMC article.
Publication types
MeSH terms
Substances
LinkOut - more resources
Full Text Sources