Nucleic Acids Research Advance Access published online on November 5, 2007
Nucleic Acids Research, doi:10.1093/nar/gkm948
© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
GLIDA: GPCR—ligand database for chemical genomics drug discovery—database and tools update
Yasushi Okuno1,*,
Akiko Tamon2,
Hiroaki Yabuuchi1,
Satoshi Niijima1,
Yohsuke Minowa1,
Koichiro Tonomura1,
Ryo Kunimoto1 and
Chunlai Feng1
1Department of PharmacoInformatics, Center for Integrative Education of Pharmacy Frontier, Graduate School of Pharmaceutical Sciences, Kyoto University and 2Bio Science Group, IT Solution Div.1, Industry Solution Business Unit, Mitsui Knowledge Industry, Osaka city, Japan
*To whom correspondence should be addressed: Tel: +81 75 753 4559; Fax: +81 75 753 4544; Email: okuno{at}pharm.kyoto-u.ac.jp
Received September 6, 2007. Revised October 14, 2007. Accepted October 15, 2007.
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ABSTRACT
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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.
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INTRODUCTION
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The family of G-protein coupled receptors (GPCRs) represents
one of the most important classes of pharmaceutical targets
(
1). Among the more than 1000 GPCRs encoded in the human genome,
more than 400 are of potential therapeutic interest (
2). Currently
the drugs available on the market address only 30 GPCRs, which
represent a small fraction of the GPCR target family. A large
majority of human-derived GPCRs still remain promising drug
targets, and thus a key goal of GPCR research related to drug
design is to identify new ligands for such target GPCRs.
With the unprecedented accumulation of genomic information, databases and bioinformatics have become essential tools to guide GPCR research (3). The GPCRDB (2) and IUPHAR receptor database (IUPHAR-RD) (4) are representatives of widely used public databases covering GPCRs. These databases, which provide substantial data on the GPCR proteins and pharmacological information on receptor proteins containing GPCRs, are mainly focused on biological aspects of the GPCR gene products or proteins. In spite of the significance of ligand compounds as drug leads, the relationships between GPCRs and their ligands and/or chemical information on the ligands themselves are not yet fully covered.
On the other hand, there is increasing interest in publicly collecting and applying chemical as well as biological information in the post-genome era (5–8). This new trend is called ‘Chemical Genomics’, and it aims to identify all possible chemical ligands and drugs for all targets families (9,10). There is a vast amount of information on the interactions between small molecules and proteins/genes. However, compound–protein interactions have not yet been analyzed on a large scale, and there are no effective methods to extract meaningful information from the data in a comprehensive manner. Therefore, we need to integrate chemoinformatics and bioinformatics into a common computational platform for mining of Chemical Genomics data (11).
GLIDA (GPCR-Ligand DAtabase) is a public GPCR-related Chemical Genomics database designed to simultaneously mine biological information on GPCRs and chemical information on their ligands. It provides various analytical data regarding GPCR–ligand correlations by incorporating bioinformatics and chemoinformatics techniques, and thus it should prove very useful for GPCR-related drug discovery from the viewpoint of Chemical Genomics research. There have been several major improvements to GLIDA since it was last described in Ref. (12): (i) there are more increments in the entries of the ligands and the corresponding ligand–GPCR pairs; (ii) the ligands are originally classified using a new strategy; (iii) additional options are available within the similarity search program for the GPCRs and ligands and (iv) the graphical interface to display the correlation maps between GPCRs and ligands has been enhanced.
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DATA CONTENTS
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GLIDA contains three types of primary data: biological information
on GPCRs, chemical information on their ligands and information
on binding of the GPCR–ligand pairs. The GPCR entries
were acquired from human, mouse and rat entries deposited in
the GPCRDB because these three species include sufficient information
regarding ligands, and rats and mice are representative model
animals used in drug discovery research. The ligand-binding
information was manually collected and curated using various
public web sites and commercial databases such as the IUPHAR-RD,
PubMed (
5), PubChem (
5), DrugBank (
13), Ki Database (
14) and
MDL ISIS/Base 2.5.
Table 1 indicates the size and scope of the
GLIDA database. In particular, we have dramatically expanded
the entry number of ligands and the corresponding ligand–GPCR
pairs. The latest GLIDA version includes 24 077 ligand entries
and 39 140 GPCR–ligand pair entries, representing nearly
35-fold and 20-fold increases, respectively, since the last
publication of GLIDA in 2006. The total number of GPCR entries
remains unchanged, but entries with associated ligand information
have increased slightly, suggesting that it is difficult to
de-orphan the GPCRs whose ligands have not yet been identified
(
15).
GPCR and ligand data
The database lists general information on GPCR and ligand data,
respectively. The general information table listing GPCRs contains
gene names, family names, protein sequences (in fasta format)
and links to other biological databases, such as GPCRDB, UniProt
(
16), IUPHAR-RD, Entrez Gene (
17) and KEGG (
18). The ligand
result page provides a general information table containing
names, molecular structures, CAS registry numbers, formulas,
molecular weights, structure files and links to PubChem, KEGG,
ChEBI (
8) and DrugBank that are in publicly available chemical
databases.
Information on binding of GPCR–ligand pairs
The interaction information relating GPCRs to particular ligands, a key issue for GPCR-related drug discovery, is deposited in a relational database. GLIDA allows users to retrieve GPCR–ligand-binding information dynamically and continuously. When users retrieve a GPCR (or ligand) entry, its result page displays all entries showing the corresponding ligands (or GPCR) entries with their binding activity types, as well as references. The references are hyperlinked with the corresponding PubMed literature. The activity types include agonist, antagonist and full, partial or inverse agonist (Table 1). Here the detail annotations such as full, partial or inverse agonist are not finished yet. The ligands classified as agonists are possible full agonists or partial agonists. Inverse agonists can be also contained among the antagonists.
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WEB INTERFACE AND APPLICATION
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GLIDA is available at
http://pharminfo.pharm.kyoto-u.ac.jp/services/glida/.
The web interface of GLIDA includes a GPCR search page (
Figure 1a)
and a ligand search page (
Figure 1b). Each page consists of
a classification menu and a keyword search box. The users can
search a GPCR (or ligand) manually using the classification
tool, or automatically by using the keyword search function.
Every GPCR (or ligand) has its own results page (
Figure 1c or
d) containing a general information table regarding a GPCR (or
ligand), a table of its correlated ligands (or GPCRs) and a
menu button to carry out a similarity search and correlation
analysis.
View larger version (68K):
[in this window]
[in a new window]
[Download PowerPoint slide]
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Figure 1. A screenshot of GLIDA showing linked relations among search pages (a and b), result pages (c and d), an analytical report page (e), and a binding information page (f). The analytical report page consists of a correlation map and a list resulting from a similarity search. Red and blue colors of the spots on the correlation map indicate the ligand activities of antagonists including inverse agonist and agonists including full/partial agonist, respectively.
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Classification of GPCRs and ligands
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.
View larger version (81K):
[in this window]
[in a new window]
[Download PowerPoint slide]
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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.
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Similarity search and correlation map for GPCRs and ligands
The fact that similar proteins bind similar ligands is the underlying
principle of the Chemical Genomics approach to drug discovery
(
11). GLIDA has a variety of similarity search functions for
GPCRs and ligands, respectively, on its result pages (
Figure 1c
or d). Alignment scores for protein sequences generated by the
BLAST algorithm provide similarity measures for GPCRs. In addition
to sequence similarity, gene expression patterns in tissue origins
and developmental stages were used as similarity measures. The
expression data for each GPCR was generated from the EST sequences
in different libraries served from NCBI/Unigene (
http://www.ncbi.nlm.nih.gov/UniGene/ddd.cgi).
We can thereby retrieve the GPCRs that present tissue-/stage-specific
distribution similar to a query GPCR. For example, co-expression
information on specific GPCRs enables us to speculate about
GPCR-heterodimerization that might have an effect on their activity
(
1). Ligand similarity is defined by the dissimilarity (distance)
of frequency profile patterns generated from the constitutive
atoms and bonds of the chemical structure, using the KEGG atom
types (
19,
20). From the similarity search, the most similar
GPCRs (or ligands) within the users’ selected parameters
are retrieved and listed with their similarity scores on an
analytical report page (
Figure 1e). In the latest GLIDA version,
various parameters have been added as search options, such as
selections of species, ligand activities, displayed number of
GPCRs/ligands and map graphical mode. As another result of similarity
search calculations, GLIDA illustrates the correlation map (
Figure 1e)
showing homologous GPCRs (or ligands) and their ligands (or
GPCRs) that are retrieved. This map shows spots that match the
GPCRs and their ligands in a 2D matrix. The ordering along the
x-axis and the
y-axis are calculated respectively by two-way
clustering of the GPCRs and the ligands, based on their similarities.
In particular, the ordering along the
x- and
y-axes allows users
to evaluate the sequence similarities among GPCRs and the correlation
coefficients among ligands simultaneously. By analyzing the
correlation patterns between GPCRs and ligands that are illustrated
by these maps, we can gain detailed knowledge about their interactions.
We can then utilize this information to infer possible candidates
for development of GPCR-specific drugs. Furthermore, we have
enhanced a graphical interface to display the correlation map
between GPCRs and ligands. Graphics are an important tool to
aid visualization and interpretation of high-dimensional data.
The old version of GLIDA used only the PNG (Portable Network
Graphics) format to display a GPCR–ligand correlation
map. Due to the great increase in entries, the latest GLIDA
version introduces the SVG (Scalable Vector Graphics) format,
which is adaptable to an enormous correlation map size. The
SVG vector image can be scaled indefinitely without loss of
image quality, while the PNG bitmap image cannot. Users must
install the free plug-in software on their computer in advance
to use the SVG format (
http://www.adobe.com/svg/viewer/install/).
In the case of uninstalled devices, PNG representation should
be selected as a graphical mode.
Figure 1 shows an example of
the GPCR–ligand search and analysis process starting from
a GPCR query using GLIDA.
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DISCUSSION AND FUTURE DIRECTIONS
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GLIDA provides a unique database useful for GPCR-related Chemical
Genomics research and drug discovery. GLIDA is distinct from
other public Chemical Genomics databases because it contains
original, GPCR-specific chemical entries and offers a common
mining platform of bioinformatics and chemoinformatics. GLIDA
provides several advantages over other databases, in that a
search can be started either from a GPCR or from a ligand. Thus,
searches can be carried out in a dynamic and user-friendly way.
GLIDA's coverage of chemical and biological information simultaneously
also provides an advantage to users by saving them the time
and labor required to search multiple databases. The ligand
search page is another distinct characteristic of GLIDA, in
that it displays the structural distribution of ligands. It
thereby facilitates research on GPCR-related drugs by incorporating
structural aspects of the ligand compounds into the search.
The analytical report pages resulting from the calculated structural
similarities of GPCRs and ligands can give the user deep insights
into the GPCR–ligand relationships. The lists of neighboring
ligands (or GPCRs) and the correlation maps are useful visualization
tools for analyzing correlations among the structural features
and the GPCR–ligand-binding properties. Because this database
system can be applied to proteins other than the GPCR family,
it may also be considered as a promising database for other
types of Chemical Genomics research. One critical issue is how
to define similarity metrics for proteins and ligands, because
the underlying principle of GLIDA is that similar receptors
bind similar ligands. For example, ligand similarity can be
defined by manifold representations such as graph, fingerprint
and descriptors. Protein similarity can be also measured in
different ways such as overall sequence homology (phylogenetic
relationships), consensus motifs, common binding sites, 3D structures
and reported functional annotations. Therefore we will add new
menus incorporating these various similarity metrics for GPCRs
and ligands. GLIDA will be updated continuously. In particular,
we are now planning to add the drawing tool of chemical structures
and to expand the ligand-searching function for an arbitrary
chemical query.
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ACKNOWLEDGEMENTS
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This work was supported by grants from the Ministry of Education,
Culture, Sports, Science and Technology of Japan, from the JSPS,
KAKENHI, Grant-in-Aid for Publication of Scientific Research
Results and from the Ministry of Health, Labour and Welfare
of Japan. Financial support from the SUNTORY INSTITUTE FOR BIOORGANIC
RESEARCH, the TATEISI SCIENCE AND TECHNOLOGY FOUNDATION and
the Okawa Foundation for Information and Telecommunications
is gratefully acknowledged. Funding to pay the Open Access publication
charges for this article was provided by the Ministry of Education,
Culture, Sports, Science and Technology of Japan.
Conflict of interest statement. None declared.
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REFERENCES
|
|---|
- George SR, O’Dowd BF, Lee SP. G-protein-coupled receptor oligomerization and its potential for drug discovery. Nature Rev. Drug Discov. (2002) 1:808–820.[CrossRef][ISI][Medline]
- Horn F, Bettler E, Oliveira L, Campagne F, Cohen FE, Vriend G. GPCRDB information system for G protein-coupled receptors. Nucleic Acids Res. (2003) 31:294–297.[Abstract/Free Full Text]
- Strachan R, Ferrara G, Roth BL. Screening the receptorome: an efficient approach for drug discovery and target validation. Drug Discov. Today (2006) 11:708–716.[CrossRef][ISI][Medline]
- Foord SM, Bonner TI, Neubig RR, Rosser EM, Pin JP, Davenport AP, Spedding M, Harmar AJ. International Union of Pharmacology. XLVI. G Protein-Coupled Receptor List. Pharmacol. Rev. (2005) 57:279–288.[Abstract/Free Full Text]
- Wheeler DL, Barrett T, Benson DA, Bryant SH, Canese K, Chetvernin V, Church DM, DiCuccio M, Edgar R, et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. (2007) 35:12.[CrossRef]
- Schreiber SL. Stuart Schreiber: biology from a chemist's perspective. Interview by Joanna Owens. Drug Discov. Today (2004) 9:299–303.[CrossRef][ISI][Medline]
- Goto S, Okuno Y, Hattori M, Nishioka T, Kanehisa M. LIGAND: database of chemical compounds and reactions in biological pathways. Nucleic Acids Res. (2002) 30:D402–D404.[CrossRef]
- Brooksbank C, Cameron G, Thornton J. The European Bioinformatics Institute's data resources: towards systems biology. Nucleic Acids Res. (2005) 33:D46–D53.[Abstract/Free Full Text]
- Zerhouni E. The NIH Roadmap. Science (2003) 302:63–72.[Abstract/Free Full Text]
- Dobson CM. Chemical space and biology. Nature (2004) 432:824–828.[CrossRef][Medline]
- Klabunde T. Chemogenomic approaches to drug discovery: similar receptors bind similar ligands. Br. J. Pharmacol. (2007) 152:5–7.[CrossRef][ISI][Medline]
- Okuno Y, Yang J, Taneishi K, Yabuuchi H, Tsujimoto G. GLIDA: GPCR-ligand database for chemical genomic drug discovery. Nucleic Acids Res. (2006) 34:D673–D677.[Abstract/Free Full Text]
- Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, Chang Z, Woolsey J. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. (2006) 34:D668–D672.[Abstract/Free Full Text]
- Roth BL, Lopez E, Beischel S, Westkaemper RB, Evans JM. Screening the receptorome to discover the molecular targets for plant-derived psychoactive compounds: a novel approach for CNS drug discovery. Pharmacol. Ther. (2004) 102:99–110.[CrossRef][ISI][Medline]
- Civelli O. GPCR deorphanizations: the novel, the known and the unexpected transmitters. Trends Pharmacol. Sci. (2005) 26:15–19.[CrossRef][Medline]
- The UniProt Consortium. The Universal Protein Resource (UniProt). Nucleic Acids Research (2007) 35:D193–D197.[CrossRef][ISI][Medline]
- Maglott D, Ostell J, Pruitt KD, Tatusova T. Entrez Gene: gene-centered information at NCBI. Nucleic Acids Res. (2007) 35:D26–D31.[Abstract/Free Full Text]
- Kanehisa M, Goto S, Kawashima S, Okuno Y, Hattori M. The KEGG resource for deciphering the genome. Nucleic Acids Res. (2004) 32:D277–D280.[Abstract/Free Full Text]
- Hattori M, Okuno Y, Goto S, Kanehisa M. Development of a Chemical Structure Comparison Method for Integrated Analysis of Chemical and Genomic Information in the Metabolic Pathways. J. Am. Chem. Soc. (2003) 125:11853–11865.[CrossRef][ISI][Medline]
- Kotera M, Okuno Y, Hattori M, Goto S, Kanehisa M. Computational assignment of the EC numbers for genomic-scale analysis of enzymatic reactions. J. Am. Chem. Soc. (2004) 126:16487–16498.[CrossRef][ISI][Medline]
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ABSTRACT
INTRODUCTION