Metrabase: a cheminformatics and bioinformatics database for small molecule transporter data analysis and (Q)SAR modeling
© Mak et al. 2015
Received: 8 March 2015
Accepted: 10 June 2015
Published: 23 June 2015
Both metabolism and transport are key elements defining the bioavailability and biological activity of molecules, i.e. their adverse and therapeutic effects. Structured and high quality experimental data stored in a suitable container, such as a relational database, facilitates easy computational processing and thus allows for high quality information/knowledge to be efficiently inferred by computational analyses. Our aim was to create a freely accessible database that would provide easy access to data describing interactions between proteins involved in transport and xenobiotic metabolism and their small molecule substrates and modulators. We present Metrabase, an integrated cheminformatics and bioinformatics resource containing curated data related to human transport and metabolism of chemical compounds. Its primary content includes over 11,500 interaction records involving nearly 3,500 small molecule substrates and modulators of transport proteins and, currently to a much smaller extent, cytochrome P450 enzymes. Data was manually extracted from the published literature and supplemented with data integrated from other available resources. Metrabase version 1.0 is freely available under a CC BY-SA 4.0 license at http://www-metrabase.ch.cam.ac.uk.
KeywordsDatabase Substrate Inhibitor Inducer Transport protein Membrane protein Metabolism CYP Drug transporter Metabolite transporter
Absorption, distribution, metabolism and excretion (ADME) properties of small molecules are vitally important for their function and potential toxicity, hence understanding their interaction with transport proteins as well as metabolizing enzymes is fundamental to the discovery and development of e.g. safe, efficacious medicines and skin products. However, this data is largely dispersed across the literature, with fractions of the data available in current open and proprietary databases. Numerous valuable publicly available transporter-related data sources exist that are either specific to membrane transporters (e.g. TP-search , UCSF-FDA TransPortal , TSdb , Human Transporter Database , HMTD , TCDB , SLC Tables  and Membrane Proteins of Known 3D Structure ) or contain transporter data as part of a broader collection of biological and pharmacological data (e.g. ChEMBL , HMDB , DrugBank , Transformer , KEGG , Recon X , PharmGKB , UniProt , CTD  and TTD ), but most of these databases do not include the additional related data (e.g. tissue expression) and metadata required for in-depth cheminformatics analyses. Ligand-based studies, however, often remain the main or only option for modeling transport proteins as targets, since there are not many 3D structures of small molecule transporters available, especially for human proteins. Moreover, since these are very flexible proteins [19–21], analyses involving the dynamics of the multiple transporter systems is often complex and often beyond the scope of presently available simulation capabilities. Nonetheless, quick and convenient access to chemical structures is often missing in these resources and sometimes only chemical names are provided, e.g. UCSF-FDA TransPortal (focused on FDA-approved drugs) and Transformer (focused on biotransformations of xenobiotics). In QSAR modeling and other cheminformatics applications however, molecular properties are typically calculated using 1D, 2D or 3D representations of molecular structures and thus access to accurate molecular structures is required. Tools that convert chemical names into structures (e.g. NCI/CADD Chemical Identifier Resolver  or OPSIN ) can be used, but due to the complexity of chemical naming they may fail or derive incorrect/inconsistent structures, hence thorough manual checking of structures is highly beneficial. Those few relevant freely and easily accessible resources that do provide chemical structures for download, such as ChEMBL, HMDB and Drugbank, all have different aims and contents (collections of bioactivity data, metabolite data and drug data, respectively). Furthermore, their transporter substrate-related content and transporter specific metadata is in general limited. In a recent study that is most related to the current work, Sedykh et al.  collected and published a significant amount of data pertaining to substrates and modulators of transport proteins with the aim of building predictive models for several transporters. Our efforts overlap, extend and complement theirs. In a single resource, which is not limited to drugs and only positive results, we include cheminformatics-specific data and functionality and also protein tissue expression levels and transporter locations in tissues that are often missing in other comparable databases.
Here we describe Metrabase, in which we offer easily accessible data on small molecule transport and metabolism, publicly available and useful for computational analyses and modeling.
Both metabolism and transport are key elements defining the bioavailability and biological activity of molecules, i.e. their adverse and therapeutic effects. Membrane transport proteins are a large and diverse group of proteins that are responsible for transporting a very diverse collection of molecules, including ions and small molecules across biological membranes. This can be observed in a relatively low-throughput fashion (transporters or carriers, including ATP-powered pumps) and also in a high-throughput fashion, e.g. up to 108 water molecules or ions per second (channels) . Metrabase includes a selection of efflux and influx transporters from the ABC  and SLC  families of transporters. They have attracted significant interest in recent years  and are also included in the revised guidance of the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA)  for the approval of drugs. In the area of metabolism, Metrabase contains a limited set of the cytochrome P450 isozymes (CYPs) that have been selected as a major group of xenobiotic metabolizing enzymes . Since drug absorption and excretion are influenced by both transport and metabolism, transporters and drug metabolizing enzymes can serve as a coordinated export system for toxic compounds and drugs, i.e. as a protective mechanism. Metrabase aims to provide comprehensive structural, physicochemical and biological data that can be used to infer the relationships between these transporters/enzymes and their ligands and its first release is presented here.
Construction and content
A summary of the transporter-related Metrabase content
A list of the action types in Metrabase
Protein activity (transport or catalysis)
Compound activity (affecting protein activity/expression)
Inhibitor/repressor (negative modulators)
Stimulator/inducer (positive modulators)
Non-inhibitor/non-inducer (inactive compounds)
The ‘activities’ table was initialized with data taken from the TP-Search  and ChEMBL  databases, comprising 2,388 TP-Search records and 1,167 records holding action type annotations in ChEMBL versions 12 and 14. We also integrated the relevant data (1,009 records) from the recent publications of Sedykh et al.  and Poongavanam et al. . Every interaction record is linked to the publication that provided the relevant data point. The current release holds only human-related records, but the importance of data related to orthologs is indisputable and therefore scheduled to be included in subsequent releases. There are five key activity fields: cmpd_id, protein_id, action_type, ref_id and species. Other ‘activities’ fields holding additional extracted data, such as assay descriptions, relevant experimental measurements, cell lines, compound concentrations and the substrates used in inhibition assays, may have only been partially completed in this release.
The total number of compounds with recorded interaction data for both transporters and enzymes is 3,438. Their structures are available in MDL molfile format and as absolute SMILES strings (in Kekulé form). The standard InChI and InChI Key strings were computed using v1.04 of the InChI software . The great majority of the compounds are small organic molecules (containing just the following atoms: C, H, O, P, S, N, F, Cl, Br and I) and other types (coordination complexes, inorganic compounds, metalloid-containing compounds, selenium-containing compounds and polymers) are listed in the ‘cmpd_types’ table. Annotations of compounds as other types and subtypes, such as ‘natural product’ and ‘sesquiterpene’, can also easily be added. Stereoisomers, different forms (e.g. a cyclic isomer of glucose) and multi-component structures (e.g. irinotecan hydrochloride; also including mixtures, such as ivermectin) are listed in the ‘cmpd_variants’ table. Where it was not clear which component(s) of a multi-component structure are responsible for the action, the multi-component structure was annotated as a mixture (here only the mixture has been tested and so the biological activity may reside in either one or all of its individual components). Chemical structures were verified using ChemSpider  and SciFinder  as “gold standards”. The ‘properties’ table contains selected molecular properties, which were calculated/predicted for all structures (molecular mass) or just the small organic single-component structures (a selection of the constitutional descriptors, log P and log D) using ChemAxon’s Calculator (cxcalc) . Additional properties, either calculated/predicted or experimental, can easily be added (and defined in the ‘property_types’ table before the records can be inserted). The database is therefore also expandable by other users who may wish to insert their own data or add metadata to the records.
The proteins contained in Metrabase are categorized as either transporters or enzymes and are provided with symbols and names approved by the HUGO Gene Nomenclature Committee (HGNC) , as well as UniProt  accession identifiers. Protein sequences for the indicated isoforms were included from UniProt. Metrabase also contains qualitative information about protein expression levels across healthy human tissues. Part of this data is based on immunohistochemistry using tissue microarrays and comes from the normal_tissue.csv file of the Human Protein Atlas (HPA) v9.0 . All other expression records contain data extracted from the literature.
Utility and discussion
The transporter substrate dataset
Structured and high quality experimental data stored in a container, such as a relational database, can facilitate easy computational processing, thus allowing for high quality information/knowledge to be inferred by computational analyses. In cheminformatics and other disciplines dealing with small molecules, in particular the correctness of chemical structures is vital [41, 42]. As stated by Young et al. , small structural errors can lead to significant errors in predictions and so the predictivity of QSAR models could be substantially increased by thorough curation.
Data mining using resources like Metrabase can be employed to understand, for example, transporter function or the promiscuous nature of compounds binding to transporters competitively or non-competitively. Transporters can be highly promiscuous proteins transporting structurally quite diverse compounds across cell membranes at rates of hundreds to tens of thousands per second . We can infer the relationships between transporters and their ligands, to see what kind of substrates they prefer and do not prefer, and why. The intended purpose of the database is also to facilitate in silico predictive modeling, e.g. tissue distribution of a new compound (and thus its toxicity potential assessed within the scope of the predicted tissue accumulation).
MBTPsubDS1_0 and MBTPsubDS1_0a datasets were created using the data contained in Metrabase v1.0 to allow a structural analysis of the transporter substrates and generation of transporter models that can predict whether or not a compound is a substrate of a range of efflux and uptake transporters (this work will be published elsewhere). All the required information (e.g. stereoisomers, metal complexes or multi-component structures linked to their single-component counterparts) is held in the database, and thus only a processing script written in a suitable programming language is sufficient to produce a version of MBTPsubDS.
read across using similarity to compounds in the database,
given a protein, show interacting compounds (including those where interaction was tested but not observed),
given a compound, show interacting proteins (including those where interaction was tested but not observed),
given a protein, list its tissue expression levels (qualitative only),
given a tissue, list protein expression levels for this tissue (qualitative only);
action type is specified for all Metrabase compounds,
negative action types (incl. inactive compounds) are included,
chemical structures are included in the database, along with compound names,
cell lines are specified for substrate records linked to primary literature references and we aim to expand this for all the ‘activities’ records,
records are linked to the publications, which the data was extracted from (allowing thus for easier verification and for getting further information) and
Metrabase is a publicly available and fully accessible resource.
Use case 1: identification of drug–drug interactions for atorvastatin
Atorvastatin (Lipitor) is a highly prescribed statin for the treatment of high blood cholesterol and for prevention of incidents that are associated with cardiovascular diseases. The mechanism of action of atorvastatin is inhibition of HMG-CoA reductase, and it is also known to be associated with other proteins, among them transporters . In this use case we show how to identify the transporters with which atorvastatin is associated, and cross-reference them with potential inhibitors of these transporters, which could result in high accumulation of the drug and potential toxic drug–drug interactions by increased bioavailability of the drug .
In the “Search by compound” section, run an exact search for “atorvastatin”.
The results show that atorvastatin was found to be a substrate of six transporters (MDR1, MCT1, OATP1B1, OATP1B3, OATP1A2, OATP2B1) and also an inhibitor of five transporters (MDR1, BCRP1, OATP1B1, OATP1B3, OATP2B1) with seven references indicating that it is an OATP1B1 substrate.
In “Search by protein” select “OATP1B1” and both “inhibitor” and “inducer”.
The results reveal a number of potential drug–drug interactions, among them several references to potentially strong inhibitors, such as cyclosporine.
The results of an exact search for cyclosporine (“Search by compound”) reveal a large number of references in support of cyclosporine being an inhibitor of several transporters, including 12 references for OATP1B1 inhibition, suggesting that the dose of atorvastatin should be lowered when co-administered with cyclosporine.
Use case 2: Exploring potential drug-food interactions with green tea
Green tea is known to contain high amounts of flavan-3-ols, including catechins of which (−)-epigallocatechin-gallate (EGCG) is the most abundant catechin . In this use case we are employing the molecular structure of catechin to search for similar compounds with annotated transporter interactions. If found this might indicate potential adverse nutrient-drug interactions instigated by consumption of green tea coupled together with drug intake.
In the “Search by compound” section run a similarity search using the SMILES representation of the catechin molecular structure, (c1cc(c(cc1[C@@H]2[C@H](Cc3c(cc(cc3O2)O)O)O)O)O), and Tanimoto coefficient (TC) cut-off of 0.3.
The results show 317 hits found in Metrabase that exhibit structural similarity to the catechin chemical structure. The hits include known catechins such as EGCG (TC = 0.823) and epicatechin (TC = 1.0), drugs such as nadolol (TC = 0.511) and troglitazone (TC = 0.457), and other natural products such as phlorizin (TC = 0.457).
Metrabase shows that EGCG has been found to be extensively associated with transporters: as a substrate of MRP1/2, OATP1B3 and OATP1A2, as a non-substrate of MDR1, OATP1B1, OATP2B1 and as an inhibitor of several OATPs such as OATP1A2, OATP1B1 and OATP2B1.
Nadolol is annotated as a substrate of MDR1 and OATP1A2 and as an inhibitor of BCRP1.
This potential interaction suggests that uptake of nadolol may be affected by co-administration with green tea and thus result in low plasma concentration due to the inhibition of its uptake transporter.
This nutrient-drug interaction was highlighted in a recent publication (not included in this version of Metrabase), which reported low intestinal uptake of nadolol by inhibition of OATP1A2-mediated uptake caused by green tea ingestion .
Future development plans
Further data curation, comprehensive data coverage and improvements are planned for subsequent Metrabase releases, as well as inclusion of additional biochemical annotations (especially with respect to sequence variants due to their importance in phenotypic variation, e.g. in drug metabolism). Moreover, we aim to include species other than human, as well as protein expression levels for diseased tissues, as this is highly relevant in the context of drug discovery. Even though the first release of Metrabase includes calculated molecular properties only, the ‘properties.type’ field values of ‘c’ and ‘e’ are proposed to designate calculated and experimental properties respectively, for possible future addition of experimental properties. The additional ‘activities’ fields, although incomplete as indicated in the Activities section above, were included in the first release and are planned to be completed in the subsequent releases. The selection of transporters, which was mainly steered by the guidelines of the International Transporter Consortium , is planned to be extended. We first aim to include additional data points for the 13 Metrabase CYPs before extending the protein list to include other Phase I, II and III enzymes involved in xenobiotic metabolism. Further improvements to the web interface can also be expected.
Metrabase offers structured and freely accessible manually extracted data on interactions between transport and metabolism related proteins and chemical compounds (the first version’s emphasis being on the transport proteins). It provides not only actions and measured activities, but also chemical structural information, tissue expression data and negative action types, which are essential in modeling activity. In particular, by creating accessible data that can be used in e.g. biochemistry, pharmacology and toxicology, we hope diverse research communities will find Metrabase useful and valuable. The availability of the raw data in an easily accessible form will allow other research workers in the field to easily include this data in collections of transporter/metabolism datasets as well as allow easy integration with other programs and services.
Availability and requirements
Metrabase is accessible via an online user interface at http://www-metrabase.ch.cam.ac.uk. Users can also install their own local copy of the database, for which a MySQL server installation is required. The Download section provides access to a MySQL dump of Metrabase, its schema, a user manual providing comprehensive information and versions of the transporter substrate dataset, i.e. MBTPsubDS. Metrabase version 1.0 is freely available under a Creative Commons Attribution-ShareAlike 4.0 International license. However, the integrated data retains the licensing of the original data sources. The TP-Search and ChEMBL records may have been modified and augmented based on literature revision or reinterpretation, while the HPA records were included unmodified. The ‘datasource id’ and ‘datasource version’ fields indicate the source of each relevant Metrabase record.
absorption, distribution, metabolism and excretion
European Medicines Agency
US Food and Drug Administration
HUGO Gene Nomenclature Committee
Human Protein Atlas
IUPAC International Chemical Identifier
Metrabase transporter substrate dataset
(quantitative) structure–activity relationship
structured query language
RCG and WK conceived the project. LM developed the database and carried out data integration and processing. LM and DM participated in data collection and curation. DM defined the use cases. AH developed the web interface. LM drafted the manuscript and DM, AB and RCG revised it critically. GY, GD, WK, AB and RCG acted as actively involved advisors throughout the project and carefully proofread the manuscript. All authors have read and approved the final manuscript.
We thank Unilever for funding and Eisai Ltd. for their contribution toward summer student funding. We thank Claire Dickson, Joseph Dixon, Ivan Lam, Richard Lewis, Callum Picken, Claudia Pop, Heyao Shi, Emma Stirk, Yasmin Surani, Paddy Szeto, Nathaniel Wand, Julian Willis and Jing Xiangyi (at the time 2nd and 3rd year undergraduate students of the University of Cambridge) for their contributions toward the Metrabase development during the summers of 2011–2013. We also thank the authors of the TP-Search, Human Protein Atlas and ChEMBL databases for their permission to distribute their data as part of Metrabase. We thank Louise Birch and Luis Castro (Eisai Ltd.) for constructive discussions. We thank ChemAxon for an academic license.
Compliance with ethical guidelines
Competing interests The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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