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dc.contributorVellido Alcacena, Alfredo
dc.contributorKönig, Caroline
dc.contributor.authorRubio Cuervo, Damián
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2021-02-18T09:56:11Z
dc.date.available2021-02-18T09:56:11Z
dc.date.issued2020-10
dc.identifier.urihttp://hdl.handle.net/2117/340035
dc.description.abstractG protein-coupled receptors, also known as GPCRs, are a kind of protein receptors that transfer signals to the inner part of cells when a certain kind of molecule is detected outside. In this thesis this kind of receptors have been analyzed from a molecular dynamics perspective. The interest in GPCRs is their structural and functional complexity, which confers great richness to their ligand space because the molecular space for drug design will be more extensive. The molecular dynamics approach allows us to explore the conformational space of GPCRs by obtaining large scale time series of protein conformations by computational simulation. Machine learning techniques have been used to differentiate between different ligand-induced conformational changes in GPCR cannabinoid receptors, focusing on feature selection in the form of protein motif search and on the analysis of different strategies for the transformation of raw molecular dynamics data into alternative representations that could be more suitable for, ease and improve the performance of machine learning based methods.
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya
dc.subjectÀrees temàtiques de la UPC::Informàtica
dc.subject.lcshMachine learning
dc.subject.lcshCell receptors
dc.subject.otherReceptores acoplados a proteina G
dc.subject.otherRAPG
dc.subject.otherdinámica molecular
dc.subject.otheranálisis exploratorio de datos
dc.subject.otherG-protein coupled receptors
dc.subject.otherGPCR
dc.subject.othermolecular dynamics
dc.subject.otherexploratory data analysis
dc.titleExploratory analysis of the molecular dynamics of cannabinoid receptor proteins
dc.typeMaster thesis
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacReceptors cel·lulars
dc.identifier.slug152571
dc.rights.accessOpen Access
dc.date.updated2020-11-02T05:00:51Z
dc.audience.educationlevelMàster
dc.audience.mediatorFacultat d'Informàtica de Barcelona
dc.audience.degreeMÀSTER UNIVERSITARI EN INNOVACIÓ I RECERCA EN INFORMÀTICA (Pla 2012)


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