Limits...
GLIDA: GPCR--ligand database for chemical genomics drug discovery--database and tools update.

Okuno Y, Tamon A, Yabuuchi H, Niijima S, Minowa Y, Tonomura K, Kunimoto R, Feng C - Nucleic Acids Res. (2007)

Bottom Line: It provides interaction data between GPCRs and their ligands, along with chemical information on the ligands, as well as biological information regarding GPCRs.By analyzing the correlation patterns between GPCRs and ligands, we can gain more detailed knowledge about their conserved molecular recognition patterns and improve drug design efforts by focusing on inferred candidates for GPCR-specific drugs.This article provides a summary of the GLIDA database and user facilities, and describes recent improvements to database design, data contents, ligand classification programs, similarity search options and graphical interfaces.

View Article: PubMed Central - PubMed

Affiliation: Department of PharmacoInformatics, Center for Integrative Education of Pharmacy Frontier, Graduate School of Pharmaceutical Sciences, Kyoto University, Japan. okuno@pharm.kyoto-u.ac.jp

ABSTRACT
G-protein coupled receptors (GPCRs) represent one of the most important families of drug targets in pharmaceutical development. GLIDA is a public GPCR-related Chemical Genomics database that is primarily focused on the integration of information between GPCRs and their ligands. It provides interaction data between GPCRs and their ligands, along with chemical information on the ligands, as well as biological information regarding GPCRs. These data are connected with each other in a relational database, allowing users in the field of Chemical Genomics research to easily retrieve such information from either biological or chemical starting points. GLIDA includes a variety of similarity search functions for the GPCRs and for their ligands. Thus, GLIDA can provide correlation maps linking the searched homologous GPCRs (or ligands) with their ligands (or GPCRs). By analyzing the correlation patterns between GPCRs and ligands, we can gain more detailed knowledge about their conserved molecular recognition patterns and improve drug design efforts by focusing on inferred candidates for GPCR-specific drugs. This article provides a summary of the GLIDA database and user facilities, and describes recent improvements to database design, data contents, ligand classification programs, similarity search options and graphical interfaces. GLIDA is publicly available at http://pharminfo.pharm.kyoto-u.ac.jp/services/glida/. We hope that it will prove very useful for Chemical Genomics research and GPCR-related drug discovery.

Show MeSH

Related in: MedlinePlus

A screenshot of the ligand search process on the ligand classification page. Users can search the ligands from two starting points: keyword search and cluster selection. If they have a chemical structure of their query compound, the ligand search is performed using the cluster selection tool as follows. Selecting a set of atom types (step 1) that the query compound contains, the pull-down menu of cluster selection displays the list of the only clusters that include selected atom types as the principal components (step 2). By selecting a cluster from the list, users can check the principal component's atoms on the upper right section of the page. Finally, upon clicking the search button, GLIDA displays the list of all ligands classified in the selected cluster (step 3). The ‘Atom Type TABLE’ button links the user to the page showing the cluster size and representative atom types for each cluster.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2238933&req=5

Figure 2: A screenshot of the ligand search process on the ligand classification page. Users can search the ligands from two starting points: keyword search and cluster selection. If they have a chemical structure of their query compound, the ligand search is performed using the cluster selection tool as follows. Selecting a set of atom types (step 1) that the query compound contains, the pull-down menu of cluster selection displays the list of the only clusters that include selected atom types as the principal components (step 2). By selecting a cluster from the list, users can check the principal component's atoms on the upper right section of the page. Finally, upon clicking the search button, GLIDA displays the list of all ligands classified in the selected cluster (step 3). The ‘Atom Type TABLE’ button links the user to the page showing the cluster size and representative atom types for each cluster.

Mentions: The GPCR classification table on the search page was adapted from the phylogenetic tree within the GPCRDB information system (http://www.gpcr.org/7tm/phylo/phylo.html). The GPCR classification table displays the entries of the corresponding GPCRs at the tree branches, and these are hyperlinked with the corresponding result pages (Figure 1a). GLIDA also provides an original ligand classification (Figures 1b and 2). With the great increase in ligand entries, we have to improve our method of classifying all the ligands in GLIDA. Hierarchical clustering and its tree representation, which were used in the old version of GLIDA, are unsuitable for the data mining of huge chemical databases. We therefore have adopted principal component analysis (PCA) for clustering of 23 214 ligand structures in this new version, as follows. We generated frequency profiles of the atoms and the bonds converted into the KEGG atom types from MDL MOL files of ligand entries (19). The KEGG-type profile for each ligand is shown in ‘Struct. file’ item of general information table of GLIDA. PCA was applied to the data matrix consisting of 700 KEGG-type features’ columns and 23 214 ligand entries’ rows. The resulting principal components (PCs) constitute a new set of linearly independent, orthogonal axes that capture the directions of maximum variance in the data. The samples (chemical compounds) were then projected onto these PC axes. Herein, we used the top 314 PCs as seeds of clusters that account for >80% (cumulative proportion) of the total variance. Finally, each compound was assigned to the PC cluster having the maximum score among the 314 PCs. In order to annotate the features of each cluster (PC), we selected for each PC the atom types and their bonds corresponding to the top 10 loadings having the largest magnitude. The ligand classification page displays a table of all the atom types selected by PCA (Figure 2). By clicking on some of the atoms in this table, users can search clusters that include the selected atom types. Consequently, the ligands relevant to users’ interests are included in the retrieved cluster.Figure 2.


GLIDA: GPCR--ligand database for chemical genomics drug discovery--database and tools update.

Okuno Y, Tamon A, Yabuuchi H, Niijima S, Minowa Y, Tonomura K, Kunimoto R, Feng C - Nucleic Acids Res. (2007)

A screenshot of the ligand search process on the ligand classification page. Users can search the ligands from two starting points: keyword search and cluster selection. If they have a chemical structure of their query compound, the ligand search is performed using the cluster selection tool as follows. Selecting a set of atom types (step 1) that the query compound contains, the pull-down menu of cluster selection displays the list of the only clusters that include selected atom types as the principal components (step 2). By selecting a cluster from the list, users can check the principal component's atoms on the upper right section of the page. Finally, upon clicking the search button, GLIDA displays the list of all ligands classified in the selected cluster (step 3). The ‘Atom Type TABLE’ button links the user to the page showing the cluster size and representative atom types for each cluster.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2238933&req=5

Figure 2: A screenshot of the ligand search process on the ligand classification page. Users can search the ligands from two starting points: keyword search and cluster selection. If they have a chemical structure of their query compound, the ligand search is performed using the cluster selection tool as follows. Selecting a set of atom types (step 1) that the query compound contains, the pull-down menu of cluster selection displays the list of the only clusters that include selected atom types as the principal components (step 2). By selecting a cluster from the list, users can check the principal component's atoms on the upper right section of the page. Finally, upon clicking the search button, GLIDA displays the list of all ligands classified in the selected cluster (step 3). The ‘Atom Type TABLE’ button links the user to the page showing the cluster size and representative atom types for each cluster.
Mentions: The GPCR classification table on the search page was adapted from the phylogenetic tree within the GPCRDB information system (http://www.gpcr.org/7tm/phylo/phylo.html). The GPCR classification table displays the entries of the corresponding GPCRs at the tree branches, and these are hyperlinked with the corresponding result pages (Figure 1a). GLIDA also provides an original ligand classification (Figures 1b and 2). With the great increase in ligand entries, we have to improve our method of classifying all the ligands in GLIDA. Hierarchical clustering and its tree representation, which were used in the old version of GLIDA, are unsuitable for the data mining of huge chemical databases. We therefore have adopted principal component analysis (PCA) for clustering of 23 214 ligand structures in this new version, as follows. We generated frequency profiles of the atoms and the bonds converted into the KEGG atom types from MDL MOL files of ligand entries (19). The KEGG-type profile for each ligand is shown in ‘Struct. file’ item of general information table of GLIDA. PCA was applied to the data matrix consisting of 700 KEGG-type features’ columns and 23 214 ligand entries’ rows. The resulting principal components (PCs) constitute a new set of linearly independent, orthogonal axes that capture the directions of maximum variance in the data. The samples (chemical compounds) were then projected onto these PC axes. Herein, we used the top 314 PCs as seeds of clusters that account for >80% (cumulative proportion) of the total variance. Finally, each compound was assigned to the PC cluster having the maximum score among the 314 PCs. In order to annotate the features of each cluster (PC), we selected for each PC the atom types and their bonds corresponding to the top 10 loadings having the largest magnitude. The ligand classification page displays a table of all the atom types selected by PCA (Figure 2). By clicking on some of the atoms in this table, users can search clusters that include the selected atom types. Consequently, the ligands relevant to users’ interests are included in the retrieved cluster.Figure 2.

Bottom Line: It provides interaction data between GPCRs and their ligands, along with chemical information on the ligands, as well as biological information regarding GPCRs.By analyzing the correlation patterns between GPCRs and ligands, we can gain more detailed knowledge about their conserved molecular recognition patterns and improve drug design efforts by focusing on inferred candidates for GPCR-specific drugs.This article provides a summary of the GLIDA database and user facilities, and describes recent improvements to database design, data contents, ligand classification programs, similarity search options and graphical interfaces.

View Article: PubMed Central - PubMed

Affiliation: Department of PharmacoInformatics, Center for Integrative Education of Pharmacy Frontier, Graduate School of Pharmaceutical Sciences, Kyoto University, Japan. okuno@pharm.kyoto-u.ac.jp

ABSTRACT
G-protein coupled receptors (GPCRs) represent one of the most important families of drug targets in pharmaceutical development. GLIDA is a public GPCR-related Chemical Genomics database that is primarily focused on the integration of information between GPCRs and their ligands. It provides interaction data between GPCRs and their ligands, along with chemical information on the ligands, as well as biological information regarding GPCRs. These data are connected with each other in a relational database, allowing users in the field of Chemical Genomics research to easily retrieve such information from either biological or chemical starting points. GLIDA includes a variety of similarity search functions for the GPCRs and for their ligands. Thus, GLIDA can provide correlation maps linking the searched homologous GPCRs (or ligands) with their ligands (or GPCRs). By analyzing the correlation patterns between GPCRs and ligands, we can gain more detailed knowledge about their conserved molecular recognition patterns and improve drug design efforts by focusing on inferred candidates for GPCR-specific drugs. This article provides a summary of the GLIDA database and user facilities, and describes recent improvements to database design, data contents, ligand classification programs, similarity search options and graphical interfaces. GLIDA is publicly available at http://pharminfo.pharm.kyoto-u.ac.jp/services/glida/. We hope that it will prove very useful for Chemical Genomics research and GPCR-related drug discovery.

Show MeSH
Related in: MedlinePlus