Abstract:
:G-protein-coupled receptors (GPCRs) initiate signaling pathways via trimetric guanine nucleotide-binding proteins. GPCRs are classified based on their ligand-binding properties and molecular phylogenetic analyses. Nonetheless, these later analyses are in most case dependent on multiple sequence alignments, themselves dependent on human intervention and expertise. Alignment-free classifications of GPCR sequences, in addition to being unbiased, present many applications uncovering hidden physicochemical parameters shared among specific groups of receptors, to being used in automated workflows for large-scale molecular modeling applications. Current alignment-free classification methods, however, do not reach a full accuracy. This chapter discusses how GPCRs amino acid sequences can be classified using pseudo amino acid composition and multiscale energy representation of different physiochemical properties of amino acids. A hybrid feature extraction strategy is shown to be suitable to represent GPCRs and to be able to exploit GPCR amino acid sequence discrimination capability in spatial as well as transform domain. Classification strategies such as support vector machine and probabilistic neural network are then discussed in regards to GPCRs classification. The work of GPCR-Hybrid web predictor is also discussed.
journal_name
Methods Enzymoljournal_title
Methods in enzymologyauthors
Rehman ZU,Mirza MT,Khan A,Xhaard Hdoi
10.1016/B978-0-12-407865-9.00004-2subject
Has Abstractpub_date
2013-01-01 00:00:00pages
61-79eissn
0076-6879issn
1557-7988pii
B978-0-12-407865-9.00004-2journal_volume
522pub_type
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