GPCR-GIA: a web-server for identifying G-protein coupled receptors and their families with grey incidence analysis.

G-protein-coupled receptors (GPCRs) play fundamental roles in regulating various physiological processes as well as the activity of virtually all cells. Different GPCR families are responsible for different functions. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop an automated method to address the two problems: given the sequence of a query protein, can we identify whether it is a GPCR? If it is, what family class does it belong to? Here, a two-layer ensemble classifier called GPCR-GIA was proposed by introducing a novel scale called 'grey incident degree'. The overall success rate by GPCR-GIA in identifying GPCR and non-GPCR was about 95%, and that in identifying the GPCRs among their nine family classes was about 80%. These rates were obtained by the jackknife cross-validation tests on the stringent benchmark data sets where none of the proteins has > or = 50% pairwise sequence identity to any other in a same class. Moreover, a user-friendly web-server was established at http://218.65.61.89:8080/bioinfo/GPCR-GIA. For user's convenience, a step-by-step guide on how to use the GPCR-GIA web server is provided. Generally speaking, one can get the desired two-level results in around 10 s for a query protein sequence of 300-400 amino acids; the longer the sequence is, the more time that is needed.

[1]  Urs Gerber,et al.  G-protein-independent signaling by G-protein-coupled receptors , 2000, Trends in Neurosciences.

[2]  Qianzhong Li,et al.  Using pseudo amino acid composition to predict protein structural class: Approached by incorporating 400 dipeptide components , 2007, J. Comput. Chem..

[3]  Yanzhi Guo,et al.  Using the augmented Chou's pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach. , 2009, Journal of theoretical biology.

[4]  K. Chou Structural bioinformatics and its impact to biomedical science. , 2004, Current medicinal chemistry.

[5]  Hansoo Lee,et al.  Amiloride potentiates TRAIL-induced tumor cell apoptosis by intracellular acidification-dependent Akt inactivation. , 2005, Biochemical and biophysical research communications.

[6]  K Nishikawa,et al.  The folding type of a protein is relevant to the amino acid composition. , 1986, Journal of biochemistry.

[7]  K. Chou,et al.  EzyPred: a top-down approach for predicting enzyme functional classes and subclasses. , 2007, Biochemical and biophysical research communications.

[8]  K. Chou Prediction of protein cellular attributes using pseudo‐amino acid composition , 2001, Proteins.

[9]  K. Chou,et al.  Recent progress in protein subcellular location prediction. , 2007, Analytical biochemistry.

[10]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[11]  References , 1971 .

[12]  Kuo-Chen Chou,et al.  Predicting enzyme family class in a hybridization space , 2004, Protein science : a publication of the Protein Society.

[13]  Kuo-Chen Chou,et al.  Using optimized evidence-theoretic K-nearest neighbor classifier and pseudo-amino acid composition to predict membrane protein types. , 2005, Biochemical and biophysical research communications.

[14]  Sukanta Mondal,et al.  Pseudo amino acid composition and multi-class support vector machines approach for conotoxin superfamily classification. , 2006, Journal of theoretical biology.

[15]  Linda B. Buck,et al.  A family of candidate taste receptors in human and mouse , 2000, Nature.

[16]  H D Dakin,et al.  On Amino-acids. , 1918, The Biochemical journal.

[17]  K. Chou,et al.  Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms , 2008, Nature Protocols.

[18]  Guo-Ping Zhou,et al.  An Intriguing Controversy over Protein Structural Class Prediction , 1998, Journal of protein chemistry.

[19]  Bhaskar D. Kulkarni,et al.  Using pseudo amino acid composition to predict protein subnuclear localization: Approached with PSSM , 2007, Pattern Recognit. Lett..

[20]  Zheng-Zhi Wang,et al.  Classification of G-protein coupled receptors at four levels. , 2006, Protein engineering, design & selection : PEDS.

[21]  K. Chou,et al.  Predicting protein structural classes with pseudo amino acid composition: an approach using geometric moments of cellular automaton image. , 2008, Journal of theoretical biology.

[22]  김삼묘,et al.  “Bioinformatics” 특집을 내면서 , 2000 .

[23]  Lourdes Santana,et al.  Medicinal chemistry and bioinformatics--current trends in drugs discovery with networks topological indices. , 2007, Current topics in medicinal chemistry.

[24]  N. L. Johnson,et al.  Multivariate Analysis , 1958, Nature.

[25]  Haruhiko Yamamoto,et al.  Length analyses of Drosophila odorant receptors. , 2003, Journal of theoretical biology.

[26]  G P Zhou,et al.  Some insights into protein structural class prediction , 2001, Proteins.

[27]  Guo-Ping Zhou,et al.  Subcellular location prediction of apoptosis proteins , 2002, Proteins.

[28]  Jianding Qiu,et al.  Prediction of G-protein-coupled receptor classes based on the concept of Chou's pseudo amino acid composition: an approach from discrete wavelet transform. , 2009, Analytical biochemistry.

[29]  Hao Lin,et al.  Prediction of cell wall lytic enzymes using Chou's amphiphilic pseudo amino acid composition. , 2009, Protein and peptide letters.

[30]  Xiaoyong Zou,et al.  Using pseudo-amino acid composition and support vector machine to predict protein structural class. , 2006, Journal of theoretical biology.

[31]  Hugh M Robertson,et al.  G Protein-Coupled Receptors in Anopheles gambiae , 2002, Science.

[32]  Guangya Zhang,et al.  Predicting the cofactors of oxidoreductases based on amino acid composition distribution and Chou's amphiphilic pseudo-amino acid composition. , 2008, Journal of theoretical biology.

[33]  Yanda Li,et al.  Prediction of protein submitochondria locations by hybridizing pseudo-amino acid composition with various physicochemical features of segmented sequence , 2006, BMC Bioinformatics.

[34]  Xiaoying Jiang,et al.  Using the concept of Chou's pseudo amino acid composition to predict apoptosis proteins subcellular location: an approach by approximate entropy. , 2008, Protein and peptide letters.

[35]  James J. Chou,et al.  The Structure of the ζζ Transmembrane Dimer Reveals Features Essential for Its Assembly with the T Cell Receptor , 2006, Cell.

[36]  F. Prado-Prado,et al.  Predicting antimicrobial drugs and targets with the MARCH-INSIDE approach. , 2008, Current topics in medicinal chemistry.

[37]  K. R. Woods,et al.  Prediction of protein antigenic determinants from amino acid sequences. , 1981, Proceedings of the National Academy of Sciences of the United States of America.

[38]  Kuo-Chen Chou,et al.  Predicting enzyme family classes by hybridizing gene product composition and pseudo-amino acid composition. , 2005, Journal of theoretical biology.

[39]  J. Nieto,et al.  Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou's pseudo amino acid composition. , 2009, Journal of theoretical biology.

[40]  Kuo-Chen Chou,et al.  Using stacked generalization to predict membrane protein types based on pseudo-amino acid composition. , 2006, Journal of theoretical biology.

[41]  Peixiang Cai,et al.  Predicting protein structural class with pseudo-amino acid composition and support vector machine fusion network. , 2006, Analytical biochemistry.

[42]  C. Kuo-chen,et al.  FoldRate: A Web-Server for Predicting Protein Folding Rates from Primary Sequence , 2009 .

[43]  Kuo-Chen Chou,et al.  Fuzzy KNN for predicting membrane protein types from pseudo-amino acid composition. , 2006, Journal of theoretical biology.

[44]  Kuo-Chen Chou,et al.  GPCR‐CA: A cellular automaton image approach for predicting G‐protein–coupled receptor functional classes , 2009, J. Comput. Chem..

[45]  Z. Huang,et al.  Using complexity measure factor to predict protein subcellular location , 2005, Amino Acids.

[46]  K. Chou,et al.  Predicting protein fold pattern with functional domain and sequential evolution information. , 2009, Journal of theoretical biology.

[47]  M. Wang,et al.  Low-frequency Fourier spectrum for predicting membrane protein types. , 2005, Biochemical and biophysical research communications.

[48]  R. Rosenfeld Nature , 2009, Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery.

[49]  Ying-Li Chen,et al.  Prediction of apoptosis protein subcellular location using improved hybrid approach and pseudo-amino acid composition. , 2007, Journal of theoretical biology.

[50]  Fengmin Li,et al.  Predicting protein subcellular location using Chou's pseudo amino acid composition and improved hybrid approach. , 2008, Protein and peptide letters.

[51]  James M. Keller,et al.  A fuzzy K-nearest neighbor algorithm , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[52]  K. Chou,et al.  PseAAC: a flexible web server for generating various kinds of protein pseudo amino acid composition. , 2008, Analytical biochemistry.

[53]  R. Lefkowitz,et al.  Regulation of G protein-coupled receptor signaling by scaffold proteins. , 2002, Circulation research.

[54]  Kuo-Chen Chou,et al.  Prediction protein structural classes with pseudo-amino acid composition: approximate entropy and hydrophobicity pattern. , 2008, Journal of theoretical biology.

[55]  Xiaoyong Zou,et al.  Prediction of protein secondary structure content by using the concept of Chou's pseudo amino acid composition and support vector machine. , 2009, Protein and peptide letters.

[56]  K. Chou,et al.  Predicting the quaternary structure attribute of a protein by hybridizing functional domain composition and pseudo amino acid composition , 2009 .

[57]  Kuo-Chen Chou,et al.  Prediction of G-protein-coupled receptor classes. , 2005, Journal of proteome research.

[58]  K. Chou,et al.  Bioinformatical analysis of G-protein-coupled receptors. , 2002, Journal of proteome research.

[59]  Z. Wen,et al.  Delaunay triangulation with partial least squares projection to latent structures: a model for G-protein coupled receptors classification and fast structure recognition , 2007, Amino Acids.

[60]  D. Pfeffermann,et al.  Small area estimation , 2011 .

[61]  Kuo-Chen Chou,et al.  Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes , 2005, Bioinform..

[62]  Shawn M. Douglas,et al.  DNA-nanotube-induced alignment of membrane proteins for NMR structure determination , 2007, Proceedings of the National Academy of Sciences.

[63]  Deng Ju-Long,et al.  Control problems of grey systems , 1982 .

[64]  Zhanchao Li,et al.  Using Chou's amphiphilic pseudo-amino acid composition and support vector machine for prediction of enzyme subfamily classes. , 2007, Journal of theoretical biology.

[65]  Kuo-Chen Chou,et al.  Signal-CF: a subsite-coupled and window-fusing approach for predicting signal peptides. , 2007, Biochemical and biophysical research communications.

[66]  Hao Lin,et al.  Prediction of Subcellular Localization of Apoptosis Protein Using Chou’s Pseudo Amino Acid Composition , 2009, Acta biotheoretica.

[67]  K. Chou,et al.  Predicting protein quaternary structure by pseudo amino acid composition , 2003, Proteins.

[68]  Guangya Zhang,et al.  Predicting lipase types by improved Chou's pseudo-amino acid composition. , 2008, Protein and peptide letters.

[69]  Kuo-Chen Chou,et al.  Coupling interaction between thromboxane A2 receptor and alpha-13 subunit of guanine nucleotide-binding protein. , 2005, Journal of proteome research.

[70]  Kuo-Chen Chou,et al.  Using pseudo amino acid composition to predict protein structural classes: Approached with complexity measure factor , 2006, J. Comput. Chem..

[71]  Kuo-Chen Chou,et al.  Predicting protein subnuclear location with optimized evidence-theoretic K-nearest classifier and pseudo amino acid composition. , 2005, Biochemical and biophysical research communications.

[72]  J. Chou,et al.  The structure of phospholamban pentamer reveals a channel-like architecture in membranes. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[73]  Gajendra P. S. Raghava,et al.  GPCRsclass: a web tool for the classification of amine type of G-protein-coupled receptors , 2005, Nucleic Acids Res..

[74]  Yu-Dong Cai,et al.  Predicting protease types by hybridizing gene ontology and pseudo amino acid composition , 2006, Proteins.

[75]  Kuo-Chen Chou,et al.  MemType-2L: a web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM. , 2007, Biochemical and biophysical research communications.

[76]  K. Chou,et al.  Euk-mPLoc: a fusion classifier for large-scale eukaryotic protein subcellular location prediction by incorporating multiple sites. , 2007, Journal of proteome research.

[77]  Lin He,et al.  Application of Pseudo Amino Acid Composition for Predicting Protein Subcellular Location: Stochastic Signal Processing Approach , 2003, Journal of protein chemistry.

[78]  Kuo-Chen Chou,et al.  Ensemble classifier for protein fold pattern recognition , 2006, Bioinform..

[79]  Hao Lin,et al.  Predicting subcellular localization of mycobacterial proteins by using Chou's pseudo amino acid composition. , 2008, Protein and peptide letters.

[80]  K. Chou,et al.  Predicting protein-protein interactions from sequences in a hybridization space. , 2006, Journal of proteome research.

[81]  Thierry Denoeux,et al.  A k-nearest neighbor classification rule based on Dempster-Shafer theory , 1995, IEEE Trans. Syst. Man Cybern..

[82]  Yongsheng Ding,et al.  Using Chou's pseudo amino acid composition to predict subcellular localization of apoptosis proteins: An approach with immune genetic algorithm-based ensemble classifier , 2008, Pattern Recognit. Lett..

[83]  J. Chou,et al.  Structure and mechanism of the M2 proton channel of influenza A virus , 2008, Nature.

[84]  Kuo-Chen Chou,et al.  Using grey dynamic modeling and pseudo amino acid composition to predict protein structural classes , 2008, J. Comput. Chem..

[85]  Jiangning Song,et al.  Prediction of protein folding rates from primary sequence by fusing multiple sequential features , 2009 .

[86]  B. Garcia,et al.  Proteomics , 2011, Journal of biomedicine & biotechnology.

[87]  K. Chou,et al.  Signal-3L: A 3-layer approach for predicting signal peptides. , 2007, Biochemical and biophysical research communications.

[88]  Kuo-Chen Chou,et al.  Predicting eukaryotic protein subcellular location by fusing optimized evidence-theoretic K-Nearest Neighbor classifiers. , 2006, Journal of proteome research.

[89]  Ying-Li Chen,et al.  Prediction of the subcellular location of apoptosis proteins. , 2007, Journal of theoretical biology.

[90]  Lourdes Santana,et al.  Proteomics, networks and connectivity indices , 2008, Proteomics.

[91]  C. Tanford Contribution of Hydrophobic Interactions to the Stability of the Globular Conformation of Proteins , 1962 .

[92]  K. Chou,et al.  Protein subcellular location prediction. , 1999, Protein engineering.

[93]  Hao Lin The modified Mahalanobis Discriminant for predicting outer membrane proteins by using Chou's pseudo amino acid composition. , 2008, Journal of theoretical biology.

[94]  G. Milligan,et al.  Protein-protein interactions at G-protein-coupled receptors. , 2001, Trends in pharmacological sciences.

[95]  Hao Lin,et al.  Predicting conotoxin superfamily and family by using pseudo amino acid composition and modified Mahalanobis discriminant. , 2007, Biochemical and biophysical research communications.

[96]  K. Chou,et al.  Prediction of protein structural classes. , 1995, Critical reviews in biochemistry and molecular biology.