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.
CitationCruz, R.; Vellido, A.; Giraldo, Jesús. Advances in semi-supervised alignment-free classification of G protein-coupled receptors. A: International Work-Conference on Bioinformatics and Biomedical Engineering. "Proceedings of the International Work-Conference on Bioinformatics and Biomedical Engineering 2013". Granada: 2013, p. 759-766.
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