Representation Learning for Class C G Protein-Coupled Receptors Classification
Cruz-Barbosa, Raúl (Technological University of the Mixteca Region. Computer Science Institute)
Ramos-Pérez, Erik-German (Technological University of the Mixteca Region. Computer Science Institute)
Giraldo, Jesús (Universitat Autònoma de Barcelona. Institut de Neurociències)

Data:	2018
Resum:	G protein-coupled receptors (GPCRs) are integral cell membrane proteins of relevance for pharmacology. The complete tertiary structure including both extracellular and transmembrane domains has not been determined for any member of class C GPCRs. An alternative way to work on GPCR structural models is the investigation of their functionality through the analysis of their primary structure. For this, sequence representation is a key factor for the GPCRs' classification context, where usually, feature engineering is carried out. In this paper, we propose the use of representation learning to acquire the features that best represent the class C GPCR sequences and at the same time to obtain a model for classification automatically. Deep learning methods in conjunction with amino acid physicochemical property indices are then used for this purpose. Experimental results assessed by the classification accuracy, Matthews' correlation coefficient and the balanced error rate show that using a hydrophobicity index and a restricted Boltzmann machine (RBM) can achieve performance results (accuracy of 92. 9%) similar to those reported in the literature. As a second proposal, we combine two or more physicochemical property indices instead of only one as the input for a deep architecture in order to add information from the sequences. Experimental results show that using three hydrophobicity-related index combinations helps to improve the classification performance (accuracy of 94. 1%) of an RBM better than those reported in the literature for class C GPCRs without using feature selection methods.
Ajuts:	Ministerio de Economía y Competitividad SAF2014-5839
Nota:	Altres ajuts: This work was partially supported by the Mexican National Council for Science and Technology (Consejo Nacional de Ciencia y Tecnología (CONACyT)), under the Catedra Program Number 1170.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Representation learning ; G protein-coupled receptors ; Deep learning ; Pattern classification
Publicat a:	Molecules, Vol. 23 Núm.3 (march 2018) , ISSN 1420-3049

DOI: 10.3390/molecules23030690
PMID: 29562690

19 p, 352.1 KB

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