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The extracellular N-terminal domain suffices to discriminate class C G Protein-Coupled Receptor subtypes from n-grams of their sequences
dc.contributor.author | König, Caroline |
dc.contributor.author | Alquézar Mancho, René |
dc.contributor.author | Vellido Alcacena, Alfredo |
dc.contributor.author | Giraldo Arjonilla, Jesús |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
dc.date.accessioned | 2016-02-11T12:53:37Z |
dc.date.available | 2016-02-11T12:53:37Z |
dc.date.issued | 2015 |
dc.identifier.citation | König, C., Alquézar, R., Vellido, A., Giraldo, J. The extracellular N-terminal domain suffices to discriminate class C G Protein-Coupled Receptor subtypes from n-grams of their sequences. A: International Joint Conference on Artificial Neural Networks. "2015 International Joint Conference on Neural Networks (IJCNN 2015): Killarney, Ireland, 12-17 July 2015". Killarney: Institute of Electrical and Electronics Engineers (IEEE), 2015, p. 1-7. |
dc.identifier.isbn | 978-1-4799-1959-8 |
dc.identifier.uri | http://hdl.handle.net/2117/82851 |
dc.description.abstract | The investigation of protein functionality often relies on the knowledge of crystal 3-D structure. This structure is not always known or easily unravelled, which is the case of eukaryotic cell membrane proteins such as G Protein-Coupled Receptors (GPCRs) and specially of those of class C, which are the target of the current study. In the absence of information about tertiary or quaternary structures, functionality can be investigated from the primary structure, that is, from the amino acid sequence. In previous research, we found that the different subtypes of class C GPCRs could be discriminated with a high level of accuracy from the n-gram transformation of their complete primary sequences, using a method that combined two-stage feature selection with kernel classifiers. This study aims at discovering whether subunits of the complete sequence retain such discrimination capabilities. We report experiments that show that the extracellular N-terminal domain of the receptor suffices to retain the classification accuracy of the complete sequence and that it does so using a reduced selection of n-grams whose length of up to five amino acids opens up an avenue for class C GPCR signature motif discovery. |
dc.format.extent | 7 p. |
dc.language.iso | eng |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica |
dc.subject.lcsh | Protein research |
dc.subject.other | Bioinformatics |
dc.subject.other | Biomembranes |
dc.subject.other | Cellular biophysics |
dc.subject.other | Feature selection |
dc.subject.other | Molecular biophysics |
dc.subject.other | Molecular configurations |
dc.subject.other | Pattern classification |
dc.subject.other | Proteins |
dc.title | The extracellular N-terminal domain suffices to discriminate class C G Protein-Coupled Receptor subtypes from n-grams of their sequences |
dc.type | Conference report |
dc.subject.lemac | Proteïnes -- Investigació |
dc.contributor.group | Universitat Politècnica de Catalunya. VIS - Visió Artificial i Sistemes Intel·ligents |
dc.contributor.group | Universitat Politècnica de Catalunya. SOCO - Soft Computing |
dc.identifier.doi | 10.1109/IJCNN.2015.7280613 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7280613 |
dc.rights.access | Open Access |
local.identifier.drac | 17494199 |
dc.description.version | Postprint (author's final draft) |
local.citation.author | König, C.; Alquézar, R.; Vellido, A.; Giraldo, J. |
local.citation.contributor | International Joint Conference on Artificial Neural Networks |
local.citation.pubplace | Killarney |
local.citation.publicationName | 2015 International Joint Conference on Neural Networks (IJCNN 2015): Killarney, Ireland, 12-17 July 2015 |
local.citation.startingPage | 1 |
local.citation.endingPage | 7 |