ArticleProtein science : a publication of the Protein Society2002
Classification of G-protein coupled receptors by alignment-independent extraction of principal chemical properties of primary amino acid sequences.
Article in Protein science : a publication of the Protein Society, 2002. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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23 citing papers in PubMed.
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- An Optimized Screen Reduces the Number of GA Transporters and Provides Insights Into Nitrate Transporter 1/Peptide Transporter Family Substrate Determinants.Frontiers in plant science · 2019Article
- Using machine learning tools for protein database biocuration assistance.Scientific reports · 2018Article
- Representation Learning for Class C G Protein-Coupled Receptors Classification.Molecules (Basel, Switzerland) · 2018Article
- Ens-PPI: A Novel Ensemble Classifier for Predicting the Interactions of Proteins Using Autocovariance Transformation from PSSM.BioMed research international · 2016Article
- Label noise in subtype discrimination of class C G protein-coupled receptors: A systematic approach to the analysis of classification errors.BMC bioinformatics · 2015Article
- The influence of alignment-free sequence representations on the semi-supervised classification of class C G protein-coupled receptors: semi-supervised classification of class C GPCRs.Medical & biological engineering & computing · 2015Article
- Identification of novel arthropod vector G protein-coupled receptors.Parasites & vectors · 2013Article
- A frequency-based linguistic approach to protein decoding and design: Simple concepts, diverse applications, and the SCS Package.Computational and structural biotechnology journal · 2013Review
- AllerTOP--a server for in silico prediction of allergens.BMC bioinformatics · 2013Article
- Homology modeling of the human 5-HT1A, 5-HT 2A, D1, and D2 receptors: model refinement with molecular dynamics simulations and docking evaluation.Journal of molecular modeling · 2012Article
- Ligand modulation of the Epstein-Barr virus-induced seven-transmembrane receptor EBI2: identification of a potent and efficacious inverse agonist.The Journal of biological chemistry · 2011Article
- Classification of G-protein coupled receptors based on support vector machine with maximum relevance minimum redundancy and genetic algorithm.BMC bioinformatics · 2010Article
- Identification of protein functions using a machine-learning approach based on sequence-derived properties.Proteome science · 2009Article
- VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines.BMC bioinformatics · 2007Article
- Mining the Arabidopsis thaliana genome for highly-divergent seven transmembrane receptors.Genome biology · 2006Article
- Prediction and classification of human G-protein coupled receptors based on support vector machines.Genomics, proteomics & bioinformatics · 2005Article
- GRIFFIN: a system for predicting GPCR-G-protein coupling selectivity using a support vector machine and a hidden Markov model.Nucleic acids research · 2005Article
- GPCRsclass: a web tool for the classification of amine type of G-protein-coupled receptors.Nucleic acids research · 2005Article
- Availability of short amino acid sequences in proteins.Protein science : a publication of the Protein Society · 2005Article
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6 authors.
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Abstract
We have developed an alignment-independent method for classification of G-protein coupled receptors (GPCRs) according to the principal chemical properties of their amino acid sequences. The method relies on a multivariate approach where the primary amino acid sequences are translated into vectors based on the principal physicochemical properties of the amino acids and transformation of the data into a uniform matrix by applying a modified autocross-covariance transform. The application of principal component analysis to a data set of 929 class A GPCRs showed a clear separation of the major classes of GPCRs. The application of partial least squares projection to latent structures created a highly valid model (cross-validated correlation coefficient, Q(2) = 0.895) that gave unambiguous classification of the GPCRs in the training set according to their ligand binding class. The model was further validated by external prediction of 535 novel GPCRs not included in the training set. Of the latter, only 14 sequences, confined in rapidly expanding GPCR classes, were mispredicted. Moreover, 90 orphan GPCRs out of 165 were tentatively identified to GPCR ligand binding class. The alignment-independent method could be used to assess the importance of the principal chemical properties of every single amino acid in the protein sequences for their contributions in explaining GPCR family membership. It was then revealed that all amino acids in the unaligned sequences contributed to the classifications, albeit to varying extent; the most important amino acids being those that could also be determined to be conserved by using traditional alignment-based methods.
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