Representation Learning for Class C G Protein-Coupled Receptors Classification.

ABSTRACT: 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 physicoch

SUBMITTER: Cruz-Barbosa R 

PROVIDER: S-EPMC6017523 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature