Wataru Nemoto

Prediction of interfaces for oligomerizations of G-protein coupled receptors.

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, D2 dopamine receptor and β2 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 D2 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 2005.

GRIP: A server for predicting interfaces for GPCR oligomerization

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.

Evolutionary Analysis of Functional Divergence among Chemokine Receptors, Decoy Receptors, and Viral Receptors

Chemokine receptors (CKRs) function in the inflammatory response and in vertebrate homeostasis. Decoy and viral receptors are two types of CKR homologs with modified functions from those of the typical CKRs. The decoy receptors are able to bind ligands without signaling. On the other hand, the viral receptors show constitutive signaling without ligands. We examined the sites related to the functional difference. At first, the decoy and viral receptors were each classified into five groups, based on the molecular phylogenetic analysis. A multiple amino acid sequence alignment between each group and the CKRs was then constructed. The difference in the amino acid composition between the group and the CKRs was evaluated as the Kullback–Leibler (KL) information value at each alignment site. The KL information value is considered to reflect the difference in the functional constraints at the site. The sites with the top 5% of KL information values were selected and mapped on the structure of a CKR. The comparisons with decoy receptor groups revealed that the detected sites were biased on the intracellular side. In contrast, the sites detected from the comparisons with viral receptor groups were found on both the extracellular and intracellular sides. More sites were found in the ligand binding pocket in the analyses of the viral receptor groups, as compared to the decoy receptor groups. Some of the detected sites were located in the GPCR motifs. For example, the DRY motif of the decoy receptors was often degraded, although the motif of the viral receptors was basically conserved. The observations for the viral receptor groups suggested that the constraints in the pocket region are loose and that the sites on the intracellular side are different from those for the decoy receptors, which may be related to the constitutive signaling activity of the viral receptors.

DOR – a Database of Olfactory Receptors – Integrated Repository for Sequence and Secondary Structural Information of Olfactory Receptors in Selected Eukaryotic Genomes

Olfaction is the response to odors and is mediated by a class of membrane-bound proteins called olfactory receptors (ORs). An understanding of these receptors serves as a good model for basic signal transduction mechanisms and also provides important clues for the strategies adopted by organisms for their ultimate survival using chemosensory perception in search of food or defense against predators. Prior research on cross-genome phylogenetic analyses from our group motivated the addressal of conserved evolutionary trends, clustering, and ortholog prediction of ORs. The database of olfactory receptors (DOR) is a repository that provides sequence and structural information on ORs of selected organisms (such as Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus, and Homo sapiens). Users can download OR sequences, study predicted membrane topology, and obtain cross-genome sequence alignments and phylogeny, including three-dimensional (3D) structural models of 100 selected ORs and their predicted dimer interfaces. The database can be accessed from http://caps.ncbs.res.in/DOR. Such a database should be helpful in designing experiments on point mutations to probe into the possible dimerization modes of ORs and to even understand the evolutionary changes between different receptors.

GRIPDB - G protein coupled Receptor Interaction Partners DataBase

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.

RECENT ADVANCES IN FUNCTIONAL REGION PREDICTION BY USING STRUCTURAL AND EVOLUTIONARY INFORMATION – REMAINING PROBLEMS AND FUTURE EXTENSIONS

Structural genomics projects have solved many new structures with unknown functions. One strategy to investigate the function of a structure is to computationally find the functionally important residues or regions on it. Therefore, the development of functional region prediction methods has become an important research subject. An effective approach is to use a method employing structural and evolutionary information, such as the evolutionary trace (ET) method. ET ranks the residues of a protein structure by calculating the scores for relative evolutionary importance, and locates functionally important sites by identifying spatial clusters of highly ranked residues. After ET was developed, numerous ET-like methods were subsequently reported, and many of them are in practical use, although they require certain conditions. In this mini review, we first introduce the remaining problems and the recent improvements in the methods using structural and evolutionary information. We then summarize the recent developments of the methods. Finally, we conclude by describing possible extensions of the evolution- and structure-based methods.

Functional region prediction with a set of appropriate homologous sequences--an index for sequence selection by integrating structure and sequence information with spatial statistics.

BackgroundThe 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.ResultsWe 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 index score, 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.ConclusionsAppropriate 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 to protein analyses. Such integration of structure and sequence information would be useful for other bioinformatics problems.

GGIP: Structure and sequence-based GPCR–GPCR interaction pair predictor

G Protein‐Coupled Receptors (GPCRs) are important pharmaceutical targets. More than 30% of currently marketed pharmaceutical medicines target GPCRs. Numerous studies have reported that GPCRs function not only as monomers but also as homo‐ or hetero‐dimers or higher‐order molecular complexes. Many GPCRs exert a wide variety of molecular functions by forming specific combinations of GPCR subtypes. In addition, some GPCRs are reportedly associated with diseases. GPCR oligomerization is now recognized as an important event in various biological phenomena, and many researchers are investigating this subject. We have developed a support vector machine (SVM)‐based method to predict interacting pairs for GPCR oligomerization, by integrating the structure and sequence information of GPCRs. The performance of our method was evaluated by the Receiver Operating Characteristic (ROC) curve. The corresponding area under the curve was 0.938. As far as we know, this is the only prediction method for interacting pairs among GPCRs. Our method could accelerate the analyses of these interactions, and contribute to the elucidation of the global structures of the GPCR networks in membranes. Proteins 2016; 84:1224–1233.

Collaborative environmental DNA sampling from petal surfaces of flowering cherry Cerasus × yedoensis ‘Somei-yoshino’ across the Japanese archipelago

Recent studies have shown that environmental DNA is found almost everywhere. Flower petal surfaces are an attractive tissue to use for investigation of the dispersal of environmental DNA in nature as they are isolated from the external environment until the bud opens and only then can the petal surface accumulate environmental DNA. Here, we performed a crowdsourced experiment, the “Ohanami Project”, to obtain environmental DNA samples from petal surfaces of Cerasus × yedoensis ‘Somei-yoshino’ across the Japanese archipelago during spring 2015. C. × yedoensis is the most popular garden cherry species in Japan and clones of this cultivar bloom simultaneously every spring. Data collection spanned almost every prefecture and totaled 577 DNA samples from 149 collaborators. Preliminary amplicon-sequencing analysis showed the rapid attachment of environmental DNA onto the petal surfaces. Notably, we found DNA of other common plant species in samples obtained from a wide distribution; this DNA likely originated from the pollen of the Japanese cedar. Our analysis supports our belief that petal surfaces after blossoming are a promising target to reveal the dynamics of environmental DNA in nature. The success of our experiment also shows that crowdsourced environmental DNA analyses have considerable value in ecological studies.