1
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Xie HL, Fu L, Nie XD. Using ensemble SVM to identify human GPCRs N-linked glycosylation sites based on the general form of Chou's PseAAC. Protein Eng Des Sel 2013; 26:735-42. [PMID: 24048266 DOI: 10.1093/protein/gzt042] [Citation(s) in RCA: 84] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
As the most frequent drug target, G-protein coupled receptors (GPCRs) are a large family of seven transmembrane receptors that sense molecules outside the cell and activate inside signal transduction pathways. Glycosylation is one of the most complex post-translational modifications (PTMs) of proteins in eukaryotic cells. It plays important roles in a variety of cellular functions, including protein folding, protein trafficking and localization, cell-cell interactions and epitope recognition. Therefore, investigating the exact position of glycosylation site in GPCR sequence can provide useful clues for drug design and other biotechnology applications. Experimental identification of glycosylation sites is expensive and laborious. Hence, there is a significant interest in the development of computational methods for reliable prediction of glycosylation sites from amino acid sequences. In this article, we presented an effective method to recognize the sites of human GPCRs by combining amino acid hydrophobicity with ensemble support vector machine. The prediction accuracy, sensitivity, specificity, Matthews correlation coefficient and area under the curve values were 94.4, 89.7, 98.9%, 0.895 and 0.989, respectively. The establishment of such a fast and accurate prediction method will speed up the pace of identifying proper GPCRs functional sites to facilitate drug discovery.
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Affiliation(s)
- Hua-Lin Xie
- School of Chemistry and Chemical Engineering, Central South University, Changsha 410083, People's Republic of China
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2
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Huang JH, Cao DS, Yan J, Xu QS, Hu QN, Liang YZ. Using core hydrophobicity to identify phosphorylation sites of human G protein-coupled receptors. Biochimie 2012; 94:1697-704. [PMID: 22503742 DOI: 10.1016/j.biochi.2012.03.022] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2011] [Accepted: 03/28/2012] [Indexed: 01/23/2023]
Abstract
As the most frequent drug target, G protein-coupled receptors (GPCRs) are a large family of seven trans-membrane receptors that sense molecules outside the cell and activate inside signal transduction pathways. The activity and lifetime of activated receptors are regulated by receptor phosphorylation. Therefore, investigating the exact positions of phosphorylation sites in GPCRs sequence could provide useful clues for drug design and other biotechnology applications. Experimental identification of phosphorylation sites is expensive and laborious. Hence, there is significant interest in the development of computational methods for reliable prediction of phosphorylation sites from amino acid sequences. In this article, we presented a simple and effective method to recognize phosphorylation sites of human GPCRs by combining amino acid hydrophobicity and support vector machine. The prediction accuracy, sensitivity, specificity, Matthews correlation coefficient and area under the curve values for phosphoserine, phosphothreonine, and phosphotyrosine were 0.964, 0.790, 0.999, 0.866, 0.941; 0.954, 0.800, 0.985, 0.828, 0.958; and 0.976, 0.820, 0.993, 0.861, 0.959, respectively. The establishment of such a fast and accurate prediction method will speed up the pace of identifying proper GPCRs sites to facilitate drug discovery.
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Affiliation(s)
- Jian-Hua Huang
- Research center of Modernization of Traditional Chinese Medicines, Central South University, Changsha 410083, PR China
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3
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ur-Rehman Z, Khan A. G-protein-coupled receptor prediction using pseudo-amino-acid composition and multiscale energy representation of different physiochemical properties. Anal Biochem 2011; 412:173-82. [DOI: 10.1016/j.ab.2011.01.040] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2010] [Revised: 01/26/2011] [Accepted: 01/27/2011] [Indexed: 11/28/2022]
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4
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Xiao X, Qiu WR. Using adaptive K-nearest neighbor algorithm and cellular automata images to predicting G-protein-coupled receptor classes. Interdiscip Sci 2010; 2:180-4. [DOI: 10.1007/s12539-010-0080-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2009] [Revised: 11/03/2009] [Accepted: 11/07/2009] [Indexed: 11/28/2022]
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5
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Suwa M, Ono Y. Computational overview of GPCR gene universe to support reverse chemical genomics study. Methods Mol Biol 2010; 577:41-54. [PMID: 19718507 DOI: 10.1007/978-1-60761-232-2_4] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/20/2023]
Abstract
In order to support high-throughput screening for ligands of G-protein coupled receptors (GPCRs) by using bioinformatics technology, we introduce a database (SEVENS) with genome-scale annotation and software (GRIFFIN) that can simulate GPCR function. SEVENS ( http://sevens.cbrc.jp/ ) is an integrated database that includes GPCR genes that are identified with high accuracy (99.4% sensitivity and 96.6% specificity) from various types of genomes, by a pipeline that integrates such software as a gene finder, a sequence alignment tool, a motif and domain assignment tool, and a transmembrane helix (TMH) predictor. SEVENS provides the user a genome-scale overview of the "GPCR universe" with detailed information of chromosomal mapping, phylogenetic tree, protein sequence and structure, and experimental evidence, all of which are accessible via a user-friendly interface. GRIFFIN ( http://griffin.cbrc.jp/ ) can predict GPCR and G-protein coupling selectivity induced by ligand binding with high sensitivity and specificity (more than 87% on average), based on the support vector machine (SVM) and hidden Markov Model (HMM). SEVENS and GRIFFIN are expected to contribute to revealing the function of orphan and unknown GPCRs.
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Affiliation(s)
- Makiko Suwa
- Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan
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6
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Qiu JD, Huang JH, Liang RP, Lu XQ. Prediction of G-protein-coupled receptor classes based on the concept of Chou’s pseudo amino acid composition: An approach from discrete wavelet transform. Anal Biochem 2009; 390:68-73. [DOI: 10.1016/j.ab.2009.04.009] [Citation(s) in RCA: 93] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2009] [Revised: 03/27/2009] [Accepted: 04/06/2009] [Indexed: 10/20/2022]
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7
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Abstract
Identification and Classification of G-protein coupled receptors (GPCRs) using protein sequences is an important computational challenge, given that experimental screening of thousands of ligands is an expensive proposition. There are two distinct but complementary approaches to GPCR classification --machine learning and sequence motif analysis. Machine learning methodologies typically suffer from problems of class imbalance and lack of multi-class classification. Many sequence motif methods, meanwhile, are too dependent on the similarity of the primary sequence alignments. It is desirable to have a motif discovery and application methodology that is not strongly dependent on primary sequence similarity. It should also overcome limitations of machine learning. We propose and evaluate the effectiveness of a simple methodology that uses a reduced protein functional alphabet representation, where similar functional residues have similar symbols. Regular expression motifs can then be obtained by ClustalW based multiple sequence alignment, using an identity matrix. Since evolutionary matrices like BLOSUM, PAM are not used, this method can be useful for any set of sequences that do not necessarily share a common ancestry. Reduced alphabet motifs can accurately classify known GPCR proteins and the results are comparable to PRINTS and PROSITE. For well known GPCR proteins from SWISSPROT, there were no false negatives and only a few false positives. This methodology covers most currently known classes of GPCRs, even if there are very few representative sequences. It also predicts more than one class for certain sequences, thus overcoming the limitation of machine learning methods. We also annotated, 695 orphan receptors, and 121 were identified as belonging to Family A. A simple JavaScript based web interface has been developed to predict GPCR families and subfamilies (www.insilico-consulting.com/gpcrmotif.html).
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Affiliation(s)
- Rajeev Gangal
- Insilico Consulting, 402, Citi Centre, 39/2, Erandwane, Karve Road, Pune, Maharashtra, India
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8
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Wen Z, Li M, Li Y, Guo Y, Wang K. Delaunay triangulation with partial least squares projection to latent structures: a model for G-protein coupled receptors classification and fast structure recognition. Amino Acids 2006; 32:277-83. [PMID: 16729188 DOI: 10.1007/s00726-006-0341-y] [Citation(s) in RCA: 54] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2006] [Accepted: 03/23/2006] [Indexed: 11/26/2022]
Abstract
As an important transmembrane protein family in eukaryon, G-protein coupled receptors (GPCRs) play a significant role in cellular signal transduction and are important targets for drug design. However, it is very difficult to resolve their tertiary structure by X-ray crystallography. In this study, we have developed a Delaunay model, which constructs a series of simplexes with latent variables to classify the families of GPCRs and projects unknown sequences to principle component space (PC-space) to predict their topology. Computational results show that, for the classification of GPCRs, the method achieves the accuracy of 91.0 and 87.6% for Class A, more than 80% for the other three classes in differentiating GPCRs from non-GPCRs and 70% for discriminating between four major classes of GPCR, respectively. When recognizing the structure of GPCRs, all the N-terminals of sequences can be determined correctly. The maximum accuracy of predicting transmembrane segments is achieved in the 7th transmembrane segment of Rhodopsin, which is 99.4%, and the average error is 2.1 amino acids, which is the lowest in all of the segments prediction. This method could provide structural information of a novel GPCR as a tool for experiments and other algorithms of structure prediction of GPCRs. Academic users should send their request for the MATLAB program for classifying GPCRs and predicting the topology of them at liml@scu.edu.cn .
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Affiliation(s)
- Z Wen
- College of Chemistry, Sichuan University, Chengdu, Sichuan, China
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9
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Zhang L, Salom D, He J, Okun A, Ballesteros J, Palczewski K, Li N. Expression of functional G protein-coupled receptors in photoreceptors of transgenic Xenopus laevis. Biochemistry 2006; 44:14509-18. [PMID: 16262251 DOI: 10.1021/bi051386z] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
G protein-coupled receptors (GPCRs) constitute the largest superfamily of transmembrane signaling proteins; however, the only known GPCR crystal structure is that of rhodopsin. This disparity reflects the difficulty in generating purified GPCR samples of sufficient quantity and quality. Rhodopsin, the light receptor of retinal rod neurons, is produced in large amounts of homogeneous quality in the vertebrate retina. We used transgenic Xenopus laevis to convert these retina rod cells into bioreactors to successfully produce 20 model GPCRs. The receptors accumulated in rod outer segments and were homogeneously glycosylated. Ligand and [(35)S]GTPgammaS binding assays of the 5HT(1A) and EDG(1) GPCRs confirmed that they were properly folded and functional. 5HT(1A)R was highly purified by taking advantage of the rhodopsin C-terminal immunoaffinity tag common to all GPCR constructs. We have also developed an automated system that can generate hundreds of transgenic tadpoles per day. This expression approach could be extended to other animal model systems and become a general method for the production of large numbers of GPCRs and other membrane proteins for pharmacological and structural studies.
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Affiliation(s)
- Li Zhang
- Novasite Pharmaceuticals, Inc., San Diego, California 92121, USA
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10
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Kratochwil NA, Malherbe P, Lindemann L, Ebeling M, Hoener MC, Mühlemann A, Porter RHP, Stahl M, Gerber PR. An automated system for the analysis of G protein-coupled receptor transmembrane binding pockets: alignment, receptor-based pharmacophores, and their application. J Chem Inf Model 2005; 45:1324-36. [PMID: 16180909 DOI: 10.1021/ci050221u] [Citation(s) in RCA: 51] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
G protein-coupled receptors (GPCRs) share a common architecture consisting of seven transmembrane (TM) domains. Various lines of evidence suggest that this fold provides a generic binding pocket within the TM region for hosting agonists, antagonists, and allosteric modulators. Here, a comprehensive and automated method allowing fast analysis and comparison of these putative binding pockets across the entire GPCR family is presented. The method relies on a robust alignment algorithm based on conservation indices, focusing on pharmacophore-like relationships between amino acids. Analysis of conservation patterns across the GPCR family and alignment to the rhodopsin X-ray structure allows the extraction of the amino acids lining the TM binding pocket in a so-called ligand binding pocket vector (LPV). In a second step, LPVs are translated to simple 3D receptor pharmacophore models, where each amino acid is represented by a single spherical pharmacophore feature and all atomic detail is omitted. Applications of the method include the assessment of selectivity issues, support of mutagenesis studies, and the derivation of rules for focused screening to identify chemical starting points in early drug discovery projects. Because of the coarseness of this 3D receptor pharmacophore model, however, meaningful scoring and ranking procedures of large sets of molecules are not justified. The LPV analysis of the trace amine-associated receptor family and its experimental validation is discussed as an example. The value of the 3D receptor model is demonstrated for a class C GPCR family, the metabotropic glutamate receptors.
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Affiliation(s)
- Nicole A Kratochwil
- Pharmaceuticals Division, F. Hoffmann-La Roche Ltd, CH-4070 Basel, Switzerland.
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11
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Frimurer TM, Ulven T, Elling CE, Gerlach LO, Kostenis E, Högberg T. A physicogenetic method to assign ligand-binding relationships between 7TM receptors. Bioorg Med Chem Lett 2005; 15:3707-12. [PMID: 15993056 DOI: 10.1016/j.bmcl.2005.05.102] [Citation(s) in RCA: 56] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2005] [Revised: 05/09/2005] [Accepted: 05/26/2005] [Indexed: 11/29/2022]
Abstract
A computational protocol has been devised to relate 7TM receptor proteins (GPCRs) with respect to physicochemical features of the core ligand-binding site as defined from the crystal structure of bovine rhodopsin. The identification of such receptors that already are associated with ligand information (e.g., small molecule ligands with mutagenesis or SAR data) is used to support structure-guided drug design of novel ligands. A case targeting the newly identified prostaglandin D2 receptor CRTH2 serves as a primary example to illustrate the procedure.
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MESH Headings
- Animals
- Benzimidazoles/chemistry
- Benzimidazoles/pharmacology
- Binding Sites/physiology
- Binding, Competitive/drug effects
- Biphenyl Compounds
- Cattle
- Computer Simulation
- Drug Design
- Hydrocarbons, Aromatic/pharmacology
- Indomethacin/analogs & derivatives
- Indomethacin/chemistry
- Indomethacin/pharmacology
- Ligands
- Models, Biological
- Molecular Structure
- Receptors, G-Protein-Coupled/antagonists & inhibitors
- Receptors, G-Protein-Coupled/classification
- Receptors, G-Protein-Coupled/metabolism
- Receptors, Immunologic/antagonists & inhibitors
- Receptors, Immunologic/metabolism
- Receptors, Prostaglandin/antagonists & inhibitors
- Receptors, Prostaglandin/metabolism
- Rhodopsin/chemistry
- Structure-Activity Relationship
- Tetrazoles/chemistry
- Tetrazoles/pharmacology
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12
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Guo YZ, Li ML, Wang KL, Wen ZN, Lu MC, Liu LX, Jiang L. Fast fourier transform-based support vector machine for prediction of G-protein coupled receptor subfamilies. Acta Biochim Biophys Sin (Shanghai) 2005; 37:759-66. [PMID: 16270155 DOI: 10.1111/j.1745-7270.2005.00110.x] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
Although the sequence information on G-protein coupled receptors (GPCRs) continues to grow, many GPCRs remain orphaned (i.e. ligand specificity unknown) or poorly characterized with little structural information available, so an automated and reliable method is badly needed to facilitate the identification of novel receptors. In this study, a method of fast Fourier transform-based support vector machine has been developed for predicting GPCR subfamilies according to protein's hydrophobicity. In classifying Class B, C, D and F subfamilies, the method achieved an overall Matthe's correlation coefficient and accuracy of 0.95 and 93.3%, respectively, when evaluated using the jackknife test. The method achieved an accuracy of 100% on the Class B independent dataset. The results show that this method can classify GPCR subfamilies as well as their functional classification with high accuracy. A web server implementing the prediction is available at http://chem.scu.edu.cn/blast/Pred-GPCR.
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Affiliation(s)
- Yan-Zhi Guo
- College of Chemistry, Sichuan University, Chengdu 610064, China
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13
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Schiöth HB, Haitina T, Ling MK, Ringholm A, Fredriksson R, Cerdá-Reverter JM, Klovins J. Evolutionary conservation of the structural, pharmacological, and genomic characteristics of the melanocortin receptor subtypes. Peptides 2005; 26:1886-900. [PMID: 15985310 DOI: 10.1016/j.peptides.2004.11.034] [Citation(s) in RCA: 104] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2004] [Accepted: 11/21/2004] [Indexed: 11/21/2022]
Abstract
We have cloned melanocortin receptors (MCRs) from several species of fish. The MC4R and MC5R subtypes arose early in vertebrate evolution and their primary structure is remarkably conserved. Expression and pharmacological characterization of the MCRs in fish has revealed that they bind and respond to melanocortin peptides with high potency. Detailed characterization of the binding properties of the different subtypes suggests that MCRs in early vertebrates had preference for adrenocorticotropic hormone (ACTH) peptides, while the high sensitivity for the shorter proopiomelanocortin (POMC) products, such as the alpha-, beta-, and gamma-melanocyte-stimulating hormone (MSH), has appeared later, perhaps as the MCR subtypes gained more specialized functions. The MCR repertoire shows in general high similarities in their primary structures, while they are however not similar in terms of functional roles. The MCRs serve therefore as an interesting model family to understand the molecular mechanisms of how functions of the genes can diverge during evolution. In this review, we provide an overview of our recent studies on the cloning, expression, pharmacology, 3D modeling, and genomic studies of the MCRs in non-mammalian species.
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Affiliation(s)
- Helgi B Schiöth
- Department of Neuroscience, Uppsala University, Biomedical Centre, Box 593, SE75124 Uppsala, Sweden.
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14
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Yabuki Y, Muramatsu T, Hirokawa T, Mukai H, Suwa M. GRIFFIN: a system for predicting GPCR-G-protein coupling selectivity using a support vector machine and a hidden Markov model. Nucleic Acids Res 2005; 33:W148-53. [PMID: 15980445 PMCID: PMC1160255 DOI: 10.1093/nar/gki495] [Citation(s) in RCA: 38] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
We describe a novel system, GRIFFIN (G-protein and Receptor Interaction Feature Finding INstrument), that predicts G-protein coupled receptor (GPCR) and G-protein coupling selectivity based on a support vector machine (SVM) and a hidden Markov model (HMM) with high sensitivity and specificity. Based on our assumption that whole structural segments of ligands, GPCRs and G-proteins are essential to determine GPCR and G-protein coupling, various quantitative features were selected for ligands, GPCRs and G-protein complex structures, and those parameters that are the most effective in selecting G-protein type were used as feature vectors in the SVM. The main part of GRIFFIN includes a hierarchical SVM classifier using the feature vectors, which is useful for Class A GPCRs, the major family. For the opsins and olfactory subfamilies of Class A and other minor families (Classes B, C, frizzled and smoothened), the binding G-protein is predicted with high accuracy using the HMM. Applying this system to known GPCR sequences, each binding G-protein is predicted with high sensitivity and specificity (>85% on average). GRIFFIN () is freely available and allows users to easily execute this reliable prediction of G-proteins.
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Affiliation(s)
- Yukimitsu Yabuki
- Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST)2-42 Aomi, Koto-ku, Tokyo 135-0064, Japan
- Information and Mathematical Science Laboratory (IMS) Inc.Meikei Building, 1-5-21 Otsuka, Bunkyo-ku, Tokyo 112-0012, Japan
| | - Takahiko Muramatsu
- Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST)2-42 Aomi, Koto-ku, Tokyo 135-0064, Japan
- Nara Institute of Science and Technology, Graduate School of Information Science8916-5 Takayama-cho, Ikoma-shi, Nara 630-0192, Japan
| | - Takatsugu Hirokawa
- Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST)2-42 Aomi, Koto-ku, Tokyo 135-0064, Japan
| | - Hidehito Mukai
- Mitsubishi Kagaku Institute of Life Sciences11 Minamiooya, Machida, Tokyo 194-8511, Japan
| | - Makiko Suwa
- Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST)2-42 Aomi, Koto-ku, Tokyo 135-0064, Japan
- Nara Institute of Science and Technology, Graduate School of Information Science8916-5 Takayama-cho, Ikoma-shi, Nara 630-0192, Japan
- To whom correspondence should be addressed. Tel: +81 3 3599 8051; Fax: +81 3 3599 8081;
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15
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Bhasin M, Raghava GPS. GPCRsclass: a web tool for the classification of amine type of G-protein-coupled receptors. Nucleic Acids Res 2005; 33:W143-7. [PMID: 15980444 PMCID: PMC1160112 DOI: 10.1093/nar/gki351] [Citation(s) in RCA: 45] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
The receptors of amine subfamily are specifically major drug targets for therapy of nervous disorders and psychiatric diseases. The recognition of novel amine type of receptors and their cognate ligands is of paramount interest for pharmaceutical companies. In the past, Chou and co-workers have shown that different types of amine receptors are correlated with their amino acid composition and are predictable on its basis with considerable accuracy [Elrod and Chou (2002) Protein Eng., 15, 713–715]. This motivated us to develop a better method for the recognition of novel amine receptors and for their further classification. The method was developed on the basis of amino acid composition and dipeptide composition of proteins using support vector machine. The method was trained and tested on 167 proteins of amine subfamily of G-protein-coupled receptors (GPCRs). The method discriminated amine subfamily of GPCRs from globular proteins with Matthew's correlation coefficient of 0.98 and 0.99 using amino acid composition and dipeptide composition, respectively. In classifying different types of amine receptors using amino acid composition and dipeptide composition, the method achieved an accuracy of 89.8 and 96.4%, respectively. The performance of the method was evaluated using 5-fold cross-validation. The dipeptide composition based method predicted 67.6% of protein sequences with an accuracy of 100% with a reliability index ≥5. A web server GPCRsclass has been developed for predicting amine-binding receptors from its amino acid sequence [ and (mirror site)].
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MESH Headings
- Artificial Intelligence
- Dipeptides/chemistry
- Internet
- Receptors, Adrenergic/chemistry
- Receptors, Adrenergic/classification
- Receptors, Biogenic Amine/chemistry
- Receptors, Biogenic Amine/classification
- Receptors, Cholinergic/chemistry
- Receptors, Cholinergic/classification
- Receptors, Dopamine/chemistry
- Receptors, Dopamine/classification
- Receptors, G-Protein-Coupled/chemistry
- Receptors, G-Protein-Coupled/classification
- Receptors, Serotonin/chemistry
- Receptors, Serotonin/classification
- Sequence Analysis, Protein
- Software
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Affiliation(s)
| | - G. P. S. Raghava
- To whom the correspondence should be addressed. Tel: +91 172 2690557/2695225; Fax: +91 172 2690632/2690585;
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16
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Bhasin M, Raghava GPS. GPCRpred: an SVM-based method for prediction of families and subfamilies of G-protein coupled receptors. Nucleic Acids Res 2004; 32:W383-9. [PMID: 15215416 PMCID: PMC441554 DOI: 10.1093/nar/gkh416] [Citation(s) in RCA: 122] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2004] [Revised: 04/02/2004] [Accepted: 04/02/2004] [Indexed: 11/13/2022] Open
Abstract
G-protein coupled receptors (GPCRs) belong to one of the largest superfamilies of membrane proteins and are important targets for drug design. In this study, a support vector machine (SVM)-based method, GPCRpred, has been developed for predicting families and subfamilies of GPCRs from the dipeptide composition of proteins. The dataset used in this study for training and testing was obtained from http://www.soe.ucsc.edu/research/compbio/gpcr/. The method classified GPCRs and non-GPCRs with an accuracy of 99.5% when evaluated using 5-fold cross-validation. The method is further able to predict five major classes or families of GPCRs with an overall Matthew's correlation coefficient (MCC) and accuracy of 0.81 and 97.5% respectively. In recognizing the subfamilies of the rhodopsin-like family, the method achieved an average MCC and accuracy of 0.97 and 97.3% respectively. The method achieved overall accuracy of 91.3% and 96.4% at family and subfamily level respectively when evaluated on an independent/blind dataset of 650 GPCRs. A server for recognition and classification of GPCRs based on multiclass SVMs has been set up at http://www.imtech.res.in/raghava/gpcrpred/. We have also suggested subfamilies for 42 sequences which were previously identified as unclassified ClassA GPCRs. The supplementary information is available at http://www.imtech.res.in/raghava/gpcrpred/info.html.
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Affiliation(s)
- Manoj Bhasin
- Institute of Microbial Technology Sector 39-A, Chandigarh, 160036, India
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17
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Papasaikas PK, Bagos PG, Litou ZI, Promponas VJ, Hamodrakas SJ. PRED-GPCR: GPCR recognition and family classification server. Nucleic Acids Res 2004; 32:W380-2. [PMID: 15215415 PMCID: PMC441569 DOI: 10.1093/nar/gkh431] [Citation(s) in RCA: 39] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2004] [Revised: 04/02/2004] [Accepted: 04/13/2004] [Indexed: 11/12/2022] Open
Abstract
The vast cell-surface receptor family of G-protein coupled receptors (GPCRs) is the focus of both academic and pharmaceutical research due to their key role in cell physiology along with their amenability to drug intervention. As the data flow rate from the various genome and proteome projects continues to grow, so does the need for fast, automated and reliable screening for new members of the various GPCR families. PRED-GPCR is a free Internet service for GPCR recognition and classification at the family level. A submitted sequence or set of sequences, is queried against the PRED-GPCR library, housing 265 signature profile HMMs corresponding to 67 well-characterized GPCR families. Users query the server through a web interface and results are presented in HTML output format. The server returns all single-motif matches along with the combined results for the corresponding families. The service is available online since October 2003 at http://bioinformatics.biol.uoa.gr/PRED-GPCR.
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Affiliation(s)
- P K Papasaikas
- Department of Cell Biology and Biophysics, Faculty of Biology, University of Athens, Panepistimiopolis, Athens 157 01, Greece
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Qian B, Soyer OS, Neubig RR, Goldstein RA. Depicting a protein's two faces: GPCR classification by phylogenetic tree-based HMMs. FEBS Lett 2003; 554:95-9. [PMID: 14596921 DOI: 10.1016/s0014-5793(03)01112-8] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Related proteins with similar biological functions generally share common features, allowing us to extract the common sequence features. These common features enable us to build statistical models that can be used to classify proteins, to predict new members, and to study the sequence-function relationship of this protein function group. Although evolution underlies the basis of multiple sequence analysis methods, most methods ignore phylogenetic relationships and the evolutionary process in building these statistical models. Previously we have shown that a phylogenetic tree-based profile hidden Markov model (T-HMM) is superior in generating a profile for a group of similar proteins. In this study we used the method to generate common features of G protein-coupled receptors (GPCRs). The profile generated by T-HMM gives high accuracy in GPCR function classification, both by ligand and by coupled G protein.
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Affiliation(s)
- Bin Qian
- Biophysics Research Division, University of Michigan, Ann Arbor, MI 48105, USA
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Madabushi S, Gross AK, Philippi A, Meng EC, Wensel TG, Lichtarge O. Evolutionary trace of G protein-coupled receptors reveals clusters of residues that determine global and class-specific functions. J Biol Chem 2003; 279:8126-32. [PMID: 14660595 DOI: 10.1074/jbc.m312671200] [Citation(s) in RCA: 151] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
G protein-coupled receptor (GPCR) activation mediated by ligand-induced structural reorganization of its helices is poorly understood. To determine the universal elements of this conformational switch, we used evolutionary tracing (ET) to identify residue positions commonly important in diverse GPCRs. When mapped onto the rhodopsin structure, these trace residues cluster into a network of contacts from the retinal binding site to the G protein-coupling loops. Their roles in a generic transduction mechanism were verified by 211 of 239 published mutations that caused functional defects. When grouped according to the nature of the defects, these residues sub-divided into three striking sub-clusters: a trigger region, where mutations mostly affect ligand binding, a coupling region near the cytoplasmic interface to the G protein, where mutations affect G protein activation, and a linking core in between where mutations cause constitutive activity and other defects. Differential ET analysis of the opsin family revealed an additional set of opsin-specific residues, several of which form part of the retinal binding pocket, and are known to cause functional defects upon mutation. To test the predictive power of ET, we introduced novel mutations in bovine rhodopsin at a globally important position, Leu-79, and at an opsin-specific position, Trp-175. Both were functionally critical, causing constitutive G protein activation of the mutants and rapid loss of regeneration after photobleaching. These results define in GPCRs a canonical signal transduction mechanism where ligand binding induces conformational changes propagated through adjacent trigger, linking core, and coupling regions.
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Affiliation(s)
- Srinivasan Madabushi
- Program in Structural and Computational Biology and Molecular Biophysics, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
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Papasaikas PK, Bagos PG, Litou ZI, Hamodrakas SJ. A novel method for GPCR recognition and family classification from sequence alone using signatures derived from profile hidden Markov models. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2003; 14:413-420. [PMID: 14758984 DOI: 10.1080/10629360310001623999] [Citation(s) in RCA: 18] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
G-protein coupled receptors (GPCRs) constitute a broad class of cell-surface receptors, including several functionally distinct families, that play a key role in cellular signalling and regulation of basic physiological processes. GPCRs are the focus of a significant amount of current pharmaceutical research since they interact with more than 50% of prescription drugs, whereas they still comprise the best potential targets for drug design. Taking into account the excess of data derived by genome sequencing projects, the use of computational tools for automated characterization of novel GPCRs is imperative. Typical computational strategies for identifying and classifying GPCRs involve sequence similarity searches (e.g. BLAST) coupled with pattern database analysis (e.g. PROSITE, BLOCKS). The diagnostic method presented here is based on a probabilistic approach that exploits highly discriminative profile Hidden Markov Models, excised from low entropy regions of multiple sequence alignments, to derive potent family signatures. For a given query, a P-value is obtained, combining individual hits derived from the same family. Hence a best-guess family membership is depicted, allowing GPCRs' classification at a family level, solely using primary structure information. A web-based version of the application is freely available at URL: http:/bioinformatics.biol.uoa.gr/PRED-GPCR.
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Affiliation(s)
- P K Papasaikas
- Department of Cell Biology and Biophysics, Faculty of Biology, University of Athens, Panepistimiopolis, Athens 157 01, Greece
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Abstract
An approach to discover sequence patterns characteristic of ligand classes is described and applied to aminergic G protein-coupled receptors (GPCRs). Putative ligand-binding residue positions were inferred from considering three lines of evidence: conservation in the subfamily absent or underrepresented in the superfamily, any available mutation data, and the physicochemical properties of the ligand. For aminergic GPCRs, the motif is composed of a conserved aspartic acid in the third transmembrane (TM) domain (rhodopsin position 117) and a conserved tryptophan in the seventh TM domain (rhodopsin position 293); the roles of each are readily justified by molecular modeling of ligand-receptor interactions. This minimally defined motif is an appropriate computational tool for identifying additional, potentially novel aminergic GPCRs from a set of experimentally uncharacterized "orphan" GPCRs, complementing existing sequence matching, clustering, and machine-learning techniques. Motif sensitivity stems from the stepwise addition of residues characteristic of an entire class of ligand (and not tailored for any particular biogenic amine). This sensitivity is balanced by careful consideration of residues (evidence drawn from mutation data, correlation of ligand properties to residue properties, and location with respect to the extracellular face), thereby maintaining specificity for the aminergic class. A number of orphan GPCRs assigned to the aminergic class by this motif were later discovered to be a novel subfamily of trace amine GPCRs, as well as the successful classification of the histamine H4 receptor.
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Affiliation(s)
- Enoch S Huang
- Pfizer Discovery Technology Center, 620 Memorial Drive, Cambridge, MA 02139, USA.
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Abstract
Groups of related genes abound in large eukaryotic genomes. In such 'subgenomes', homology modeling carried out for a few genes will probably have relevance to the entire group. Subgenomes also afford unique ways of determining protein structural information. In addition to analyses based on the quantification of residue variability in paralogs, two-way comparisons, both within and among species, help to disclose functional amino acids. Comparative studies of gene families throughout the mammalian genome will also help elucidate the functional significance of single nucleotide polymorphisms in coding regions.
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Affiliation(s)
- Orna Man
- The Crown Human Genome Center, The Weizmann Institute of Science, Rehovot, 76100, Israel
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