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Advances in semi-supervised alignment-free classification of G protein-coupled receptors
Cruz Barbosa, Raúl; Vellido Alcacena, Alfredo; Giraldo, Jesús
Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics; Universitat Politècnica de Catalunya. SOCO - Soft Computing
G Protein-coupled receptors (GPCRs) are integral cell membrane proteins of great relevance for pharmacology due to their role in transducing extracellular signals. The 3-D s tructure is unknown for most of them, and the investigation of their structure-function relationships usually relies on the construction of 3-D receptor models from amino acid sequence alignment onto those receptors of known structure. Sequence alignment risks the loss of relevant information. Different approaches have attempted the analysis of alignment-free sequences on the basis of amino acid physicochemical properties. In this paper, we use the Auto-Cross Covariance method and compare it to an amino acid composition representation. Novel semi-supervised manifold learning methods are then used to classify the several members of class C GPCRs on the basis of the transformed data. This approach is relevant because protein sequences are not always labeled and methods that provide robust classification for a limited amount of labels are required.
Peer Reviewed
Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica
G proteins receptors -- Research -- Methodology
biology computing
cellular biophysics
covariance analysis
molecular biophysics
Proteïnes G -- Receptors

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