Literature DB >> 12456868

A study on the correlation of G-protein-coupled receptor types with amino acid composition.

David W Elrod1, Kuo-Chen Chou.   

Abstract

G-protein-coupled receptors have become a target in utilizing bioinformatics and genomics technology to facilitate drug discovery for psychiatric diseases. In this study the covariant-discriminant algorithm [Chou and Elrod (1999) Protein Eng., 12, 107-118] has been used to analyze the correlation between the types of G-protein-coupled receptors and the amino acid composition. It has been found that different types of G-protein-coupled receptors are quite closely correlated with the amino acid composition, implying that the types of G-protein-coupled receptors are predictable to a considerably accurate extent if a good training data set can be established for that purpose. The method derived here can be also used to do preliminary classification of orphan G-protein-coupled receptors. This will significantly expedite the process of identifying proper G-protein-coupled receptors for drug discovery.

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Year:  2002        PMID: 12456868     DOI: 10.1093/protein/15.9.713

Source DB:  PubMed          Journal:  Protein Eng        ISSN: 0269-2139


  9 in total

1.  GPCRpred: an SVM-based method for prediction of families and subfamilies of G-protein coupled receptors.

Authors:  Manoj Bhasin; G P S Raghava
Journal:  Nucleic Acids Res       Date:  2004-07-01       Impact factor: 16.971

2.  Prediction of mitochondrial proteins using discrete wavelet transform.

Authors:  Lin Jiang; Menglong Li; Zhining Wen; Kelong Wang; Yuanbo Diao
Journal:  Protein J       Date:  2006-06       Impact factor: 2.371

3.  Classification of G-protein coupled receptors based on support vector machine with maximum relevance minimum redundancy and genetic algorithm.

Authors:  Zhanchao Li; Xuan Zhou; Zong Dai; Xiaoyong Zou
Journal:  BMC Bioinformatics       Date:  2010-06-16       Impact factor: 3.169

4.  An improved classification of G-protein-coupled receptors using sequence-derived features.

Authors:  Zhen-Ling Peng; Jian-Yi Yang; Xin Chen
Journal:  BMC Bioinformatics       Date:  2010-08-09       Impact factor: 3.169

5.  GPCRsclass: a web tool for the classification of amine type of G-protein-coupled receptors.

Authors:  Manoj Bhasin; G P S Raghava
Journal:  Nucleic Acids Res       Date:  2005-07-01       Impact factor: 16.971

6.  Identifying G-protein coupled receptors using weighted Levenshtein distance and the nearest neighbor method.

Authors:  Jian Hua Xu
Journal:  Genomics Proteomics Bioinformatics       Date:  2005-11       Impact factor: 7.691

7.  Predicting the coupling specificity of GPCRs to G-proteins by support vector machines.

Authors:  Cui Ping Guan; Zhen Ran Jiang; Yan Hong Zhou
Journal:  Genomics Proteomics Bioinformatics       Date:  2005-11       Impact factor: 7.691

8.  Graph Theory-Based Sequence Descriptors as Remote Homology Predictors.

Authors:  Guillermin Agüero-Chapin; Deborah Galpert; Reinaldo Molina-Ruiz; Evys Ancede-Gallardo; Gisselle Pérez-Machado; Gustavo A de la Riva; Agostinho Antunes
Journal:  Biomolecules       Date:  2019-12-23

9.  HMM-ModE: implementation, benchmarking and validation with HMMER3.

Authors:  Swati Sinha; Andrew Michael Lynn
Journal:  BMC Res Notes       Date:  2014-07-30
  9 in total

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