Representation learning for class C G protein-coupled receptors classification

Raúl Cruz-Barbosa, Erik German Ramos-Pérez, Jesús Giraldo

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)

Abstract

© 2018 by the authors. 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.
Original languageEnglish
Article number690
JournalMolecules
Volume23
Issue number3
DOIs
Publication statusPublished - 1 Jan 2018

Keywords

  • Deep learning
  • G protein-coupled receptors
  • Pattern classification
  • Representation learning

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