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ABSTRACT: BACKGROUND: The detection of conserved residue clusters on a protein structure is one of the effective strategies for the prediction of functional protein regions. Various methods, such as Evolutionary Trace, have been developed based on this strategy. In such approaches, the conserved residues are identified through comparisons of homologous amino acid sequences. Therefore, the selection of homologous sequences is a critical step. It is empirically known that a certain degree of sequence divergence in the set of homologous sequences is required for the identification of conserved residues. However, the development of a method to select homologous sequences appropriate for the identification of conserved residues has not been sufficiently addressed. An objective and general method to select appropriate homologous sequences is desired for the efficient prediction of functional regions. RESULTS: We have developed a novel index to select the sequences appropriate for the identification of conserved residues, and implemented the index within our method to predict the functional regions of a protein. The implementation of the index improved the performance of the functional region prediction. The index represents the degree of conserved residue clustering on the tertiary structure of the protein. For this purpose, the structure and sequence information were integrated within the index by the application of spatial statistics. Spatial statistics is a field of statistics in which not only the attributes but also the geometrical coordinates of the data are considered simultaneously. Higher degrees of clustering generate larger index scores. We adopted the set of homologous sequences with the highest indexscore, under the assumption that the best prediction accuracy is obtained when the degree of clustering is the maximum. The set of sequences selected by the index led to higher functional region prediction performance than the sets of sequences selected by other sequence-based methods. CONCLUSIONS: Appropriate homologous sequences are selected automatically and objectively by the index. Such sequence selection improved the performance of functional region prediction. As far as we know, this is the first approach in which spatial statistics have been applied t o protein analyses. Such integration of structure and sequence information would be useful for other bioinformatics problems.
BMC Structural Biology 05/2012; 12(1):11. · 2.48 Impact Factor
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ABSTRACT: The G protein Coupled Receptor (GPCR) superfamily is one of the most important pharmaceutical targets. Studies of GPCRs have long been performed under the assumption that GPCRs function as monomers. However, recent studies have revealed that many GPCRs function as homo- and/or hetero-dimers or higher-order oligomeric molecular complexes. As a result, information about GPCR oligomerization is rapidly accumulating, although the molecular mechanisms of oligomerization are not fully understood. A comprehensive collection of information about oligomerization would accelerate investigations of the molecular mechanisms of GPCRs' oligomerization and involvement in signaling. Hence, we have developed a database, G protein coupled Receptor Interaction Partners DataBase (GRIPDB), which provides information about GPCR oligomerization. The entries in the database are divided into two sections: (I) Experiment Information section and (II) Prediction Information section. The Experiment Information section contains (I-i) experimentally indentified GPCR oligomers and their annotations, and (I-ii) experimentally suggested interfaces for the oligomerization. Since the number of experimentally suggested interfaces is limited, the entries in the Prediction Information section have been introduced to provide information about the oligomerization interfaces predicted by our computational method. The experimentally suggested or computationally predicted interfaces are displayed by 3D graphics, using GPCRs with available coordinates. The information in the GRIPDB, especially that about the interfaces, is useful to investigate the molecular mechanisms of signal transduction via GPCR oligomerization. The GRIPDB is available on the web at the following URL: http://grip.cbrc.jp/GDB/index.html .
Journal of Receptor and Signal Transduction Research 03/2011; 31(3):199-205. · 1.59 Impact Factor
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ABSTRACT: G-Protein Coupled Receptors (GPCRs) are one of the most important pharmaceutical targets. Recent studies have revealed that many GPCRs form homo- and/or hetero-oligomers. The molecular mechanisms of oligomerization are not fully understood yet, due to the lack of structural data for GPCR complexes. Therefore, accurate interface prediction would accelerate investigations of the molecular mechanisms of oligomerization and signaling via GPCRs. However, interface prediction for GPCR oligomerization is difficult, because the various GPCR subtypes often use different structural regions as their interfaces, even when the subtypes belong to the same subfamily. Previously, we developed a method to predict the interfaces for GPCR oligomerization, which overcomes the difficulty described above. We have now launched a web service, named G-protein coupled Receptors Interaction Partners (GRIP) ( http://grip.cbrc.jp/GRIP/index.html ), to predict the interfaces for GPCR oligomerization. As far as we know, it is the only service to predict the interfaces for GPCR oligomerization.
Journal of Receptor and Signal Transduction Research 11/2009; 29(6):312-7. · 1.59 Impact Factor
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ABSTRACT: G-Protein Coupled Receptors (GPCRs) are one of the most important targets for pharmaceutical drug design. Over the past 30 years, mounting evidence has suggested the existence of homo and hetero dimers or higher-order complexes (oligomers) that are involved in signal transduction and some diseases. The number of reports describing GPCR oligomerization has increased, and in 2003, the organization of mouse rhodopsin into two-dimensional arrays of dimers was determined by an atomic force microscopic analysis. The analysis of the mouse rhodopsin complex has enabled us to discuss the oligomerization based on structural data. Although many unsolved problems still remains, the idea that GPCRs directly interact to form oligomers has been gradually accepted. One of the recent findings in the GPCR investigations is the clarification of the mechanisms of GPCR oligomerization at a molecular level. Most of these studies have suggested the importance of transmembrane alpha-helices for GPCR oligomerization. In this review, we will first summarize the importance of GPCR oligomerization and the functions of GPCRs. Then, we will explain the involvement of transmembrane alpha-helices in the oligomerization and a drug design strategy that targets these regions for GPCR oligomerization. Considering the current drug design methods, which are based on the modification of the protein-protein interactions of soluble regions of proteins, a "peptide mimic approach" that targets the transmembrane alpha-helices constituting the interfaces would be promising in drug discovery for GPCR oligomerization. For that purpose, we must know the positions of the interfaces. However, problems specific to membrane proteins have made it difficult to identify the positions of the interfaces experimentally. Therefore, information about the interfaces predicted by bioinformatics approaches is valuable. At the end of this review, several bioinformatics approaches toward interface prediction for oligomerization are introduced. The benefits and the pitfalls of these approaches are also discussed.
Current Protein and Peptide Science 01/2007; 7(6):561-75. · 2.89 Impact Factor
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Tanpakushitsu kakusan koso. Protein, nucleic acid, enzyme 09/2005; 50(10 Suppl):1382-7.
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Folia Pharmacologica Japonica 04/2005; 125(3):159-64.
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ABSTRACT: Several lines of biochemical and pharmacological evidence have suggested that some G-protein-coupled receptors (GPCRs) form homo oligomers, hetero oligomers or both. The GPCRs oligomerizations are considered to be related to signal transduction and some diseases. Therefore, an accurate prediction of the residues that interact upon oligomerization interface would further our understanding of signal transduction and the diseases in which GPCRs are involved. One of the complications for such a prediction is that the interfaces differ with the subtypes, even within the same GPCR family. Focusing on the distribution of residues conserved on the molecular surface in a particular subtype, we developed a new method to predict the interface for the GPCR oligomers, and applied it to several subtypes of known GPCRs to check the sensitivity. Subsequently, we found that predicted interfaces of rhodopsin, D(2) dopamine receptor and beta(2) adrenergic receptor agreed with the experimentally suggested interfaces, despite difference in the interface region among the three subtypes. Moreover, a highly conserved residue detected from the D(2) dopamine receptor corresponded to a residue involved in a missense change found in the large family of myoclonus dystonia. Our observation suggests the possibility that the disease is caused by the disorder of the oligomerization, although the molecular mechanism of the disease has not been revealed yet. The benefits and the pitfalls of the new method will be discussed, based on the results of the applications.
Proteins Structure Function and Bioinformatics 03/2005; 58(3):644-60. · 3.39 Impact Factor
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ABSTRACT: Introduction About 1000 genes encoding GPCRs (G protein Coupled Receptors) exist in human genome. Because over 30% of clinically marketed drugs are active at this family, GPCRs are one of the most important target classes of proteins for drug. For the last fifteen years, the formation of GPCR homo and hetero oligomer has been suggested by biochemical and pharmacological evidence. More recently, by atomic force microscopy, it was revealed that bovine rhodopsins form a homo oligomer in native membrane [2]. Some subtypes form oligomers, while others do not. The oligomerization patterns di#er with the subtypes of GPCRs. Signal transduction by GPCRs are considered to be associated with the oligomerization. To clarify the mechanism of signal transduction, it is important to elucidate the oligomeric patterns of GPCRs. Accordingly, prediction of interface for the oligomerization would be the first step to understand the mechanism of the oligomerization. When we examined the spatial distributi
07/2004;
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Genome Informatics. 01/2003; 14:512-513.
