Welcome to the GPCRdb documentation

The GPCRdb contains data, diagrams and web tools for G protein-coupled receptors (GPCRs). Users can browse all GPCR crystal structures and the largest collection of receptor mutants. Diagrams can be produced and downloaded to illustrate receptor residues (snake-plot and helix box diagrams) and relationships (phylogenetic trees). Reference (crystal) structure-based sequence alignments take into account helix bulges and constrictions, display statistics of amino acid conservation and have been assigned generic residue numbering for equivalent residues in different receptors.

The source code and source data are freely available on GitHub.

The documentation is organized into four sections:

Receptors and sequences

Receptors pages

This video demonstrates how to use the receptor pages.

  • By moving the pointer over residues in the sequence viewer, more information can be displayed.
  • Snake and Helix box plots can be custom colored and downloaded.
  • A summary of available mutant data is displayed, and a link provided for further analysis.
  • Available structure information is listed.

Questions

  • Look up the receptor page for the beta1-adrenoceptor. How many mutations are there available?
  • Look up the receptor page for your favorite GPCR. Is there any data available?

Structure-based sequence alignments

This video demonstrates how to build a structure-based sequence alignment.

  • Any combination of receptors and/or receptor families can be selected.
  • It is possible to limit the alignment to a particular part of the sequence, e.g. TM6.
  • By moving the pointer over residues in the alignment, more information can be displayed.
  • A consensus sequence (color coded by conservation) is displayed below the alignment.
  • Statistics on residue and residue property conservation is displayed below the consensus sequence.

Question

  • Create an alignment of TM5 for all Class A peptide receptors. How well conserved is position 5.50?

Structures

Structure statistics

This video demonstrates how to view statistics based on available structure data in GPCRdb.

  • The statistics are automatically generated, and always reflect the latest version of the database.
  • By moving the pointer over the plot, more information can be displayed.
  • All plots can be downloaded.

Question

  • How many structures were published in 2014?

Structure browser

This video demonstrates how to use the structure browser to gain an overview of the available structural data in GPCRdb.

  • The structural data is displayed in a table format.
  • The table can be filtered and sorted on any column.
  • The “Show representative” button shows only one selected structure for each protein.
  • To use the “Superpose structures” button, first select (check box) structures to superpose, and highlight (click on the row) the reference structure (the selected structure will be superposed on this structure).
  • To use the “Download” and “Align” buttons, first select (tick box) structures to download or align.

Question

How many structures from Class B are available?

Structure superposition

The following three videos demonstrate how to superpose structures in GPCRdb.

Example 1

  • Only one structure can be selected as a reference, and all selected structures will be superposed on this structure.
  • It is possible to select many structures to superpose using the “Structure(s) to superpose” button (hold down Control on Windows/Linux and Command on Mac while selecting).
  • It is possible to select which parts of the sequence (e.g. all TMs or only TM5) to superpose on.
  • After the structures have been superposed, you can download the full structures, or only a specific part of the sequence (e.g. TM5). In this video, the user downloads the full structure.

Example 2

  • Only one structure can be selected as a reference, and all selected structures will be superposed on this structure.
  • It is possible to select many structures to superpose using the “Structure(s) to superpose” button (hold down Control on Windows/Linux and Command on Mac while selecting).
  • It is possible to select which parts of the sequence (e.g. all TMs or only TM5) to superpose on.
  • After the structures have been superposed, you can download the full structures, or only a specific part of the sequence (e.g. TM5). In this video, the user downloads only a part of the sequence.

Example 3

  • It is also possible to select structures to superpose from the structure browser.
  • First select the structures that should be superposed using the check boxes to the left of each row.
  • To select the reference structure, highlight it by click anywhere on its row (it turns blue).
  • Click “Superpose structures” and continue the superposition workflow as before.

Generic numbering of PDB files

This video demonstrates how to add generic residue numbers to a PDB file using GPCRdb, and visualize the numbers in PyMOL.

  • Upload any PDB file (also homology models).
  • It is possible to download the full structure, or subset of its sequence.
  • On the results page, download the PyMOL visualization script at the bottom of the page.
  • Load the downloaded PDB file into PyMOL.
  • Drag the downloaded script file onto the PyMOL window.
  • Press F2 to view the generic residue numbers (F1 removes the numbers again).

Mutations

Mutation browser

This video demonstrates how use the mutation browser to search for and visualize mutations for a family of receptors.

  • It is possible to limit the search to a particular part of the sequence, e.g. TM6.
  • The mutations are listed in table format, and it is possible to filter and order interactions by the values in each column.
  • By moving the pointer to a ligand name or reference, more details can be shown.
  • Snake and Helix box plots highlighting the mutated residues can be downloaded. The mutated residues are colored on a green-yellow-red scale according to effect on ligand binding.

Questions

  • Look up mutations for histamine receptors. How many are available for generic position 3x32?
  • Look up mutations for your favorite family of GPCRs. How many are available?

Sites

Ligand interactions

This video demonstrates how to visualize receptor-ligand interactions in a PDB file by providing a PDB code, or by uploading your own file.

  • The interactions are listed in table format, and it is possible to filter and order interactions by the values in each column.
  • An interactive 3D viewer showing only the ligand and interacting residues from the receptor is available.
  • Snake and Helix box plots highlighting the interacting residues in red can be downloaded.

Question

  • Look up the receptor ligand interactions in PDB structure 3RZE? How many interactions are found?

Site search - from PDB complex

This video demonstrates how to create a binding site definition by providing a PDB code, or by uploading your own file. The resulting site definition can be used to search for receptors with a similar binding site.

  • A binding site definition can be extracted from a provided PDB code, or an uploaded PDB file.
  • First select the set of receptors that should be compared the the site definition.
  • Once the PDB file has been processed, the site definition can be reviewed and modified.
  • Note the “Min. match” field, which determines how many interactions must match for a receptor to be considered a match.
  • The results page shows a list of matching receptors, followed by a list of non-matching receptors.

Question

  • Create a site definition using PDB structure 3RZE? How many interactions are found?

Receptors and families

The selection page allows users to find a receptor and family by searching or browsing.

The search box displays a list of both families and receptors that match the input keyword. Selecting either a receptor or family will take you to the corresponding receptor/family page.

The browser displays a hierarchical view of the families, and the proteins in each family. Selecting either a receptor or family will take you to the corresponding receptor/family page.

Receptor pages

The page displays basic information about the selected protein and a sequence viewer, as well as helix box and snake diagrams. The diagrams can be colored by properties, mutant information, or ligand interactions extracted from structures.

Family pages

The family pages resemble the protein pages, but the sequence shown on a family page is a consensus sequence for the human sequences in the family.

Signal proteins

Signal protein page

The selection page allows users to find a signal protein or family (grouped in the 4 main G protein families) by searching or browsing.

The browser displays a hierarchical view of the families, and the proteins in each family. Selecting either a receptor or family will take you to the corresponding receptor/family page. Species orthologs can be selected when toggling the Species button to ‘All’

The page displays basic information about the selected protein and a sequence viewer a snake-like diagrams. The diagrams can be colored by properties, receptor interface and barcode information.

GPCR-G protein coupling

The page shows statistics on known coupling preferences as extracted from Guide to PHARMACOLOGY as:

  1. an interactive Venn diagram, which highlights the number of reported receptors for each G protein coupling combination
  2. an interactive phylogenetic tree, for which concentric circles illustrate the G protein-coupling selectivity of each GPCR the four dots depict both primary and secondary G protein coupling (from inside to outside: Gαs, Gαi/o, Gαq/11, Gα12/13). Tree nodes can be highlighted and selected to retrieve clade-specific receptor sets, which can be used in dedicated segment specific sequence alignments.

Highlighting and selection of receptors populates a field, which can be used as an input for dedicated segment specific sequence alignments.

G protein alignments

The “Structure-based alignments” tool allows for alignment of user selected G proteins and sequence segments. Using the tool is a two step process.

  1. The user is first presented with a G protein selection page.
  2. The user is presented with a sequence segment selection page. The user can select one or more sequence segments, and/or expand each segment to select the residues within it individually.

After completing these two steps, an alignment is displayed. To display the sequence number of an aligned residue, as well as generic numbers (CGN numbering), hover the mouse over it. At the bottom of the page, a consensus sequence as well as conservation statistics for amino acids and chemical features are displayed.

Interface mapping

Maps the β2-Gαs complex (PDB: 3SN6) interaction interface onto a snake plot of a selected receptor and highlights conserved and accessible interactions.

  • Flock, T., Hauser, A. S., Lund, N., Gloriam, D. E., Balaji, S., & Babu, M. M., “Selectivity determinants of GPCR–G-protein binding.”, 2017, Nature, May 18;545(7654):317-322 10.1038/nature22070

Sequences

Structure-based alignments

The “Structure-based alignments” tool allows for alignment of user selected receptors and sequence segments. Using the tool is a two step process.

  1. The user is first presented with a receptor selection page. Receptors can be selected individually or by family. The user can select as many receptors as he/she wishes (WARNING: selecting a large number of receptors increases loading time).
  2. After receptors have been selected, the user is presented with a sequence segment selection page. The user can select one or more sequence segments, and/or expand each segment to select the residues within it individually. Residues selected individually are grouped into a custom sequence segment.

After completing these two steps, an alignment is displayed. To display the sequence number of an aligned residue, as well as generic numbers, hover the mouse over it. At the bottom of the page, a consensus sequence as well as conservation statistics for amino acids and chemical features are displayed.

Phylogenetic trees

The phylogenetic tree tool allows for generation of phylogenetic trees based on user selected receptors and sequence segments. Using the tool is a three step process.

  1. The user is first presented with a receptor selection page. Receptors can be selected individually or by family. The user can select as many receptors as he/she wishes (WARNING: selecting a large number of receptors increases loading time).
  2. After receptors have been selected, the user is presented with a sequence segment selection page. The user can select one or more sequence segments, and/or expand each segment to select the residues within it individually. Residues selected individually are grouped into a custom sequence segment.
  3. In the third step, a settings page is displayed. The amount of bootstrapping replicas (0, 10 or 100) and the type of tree (rectangular or circular) are configurable by the user. User are also offered an option to show branch lengths that represent the evolutionary distance between the nodes, or show the same branch length between every node.

To view an alignment of the sequences used to generate the tree after it has been displayed, click the “View alignment” button.

The trees are generated using PHYLIP and jsPhyloSVG.

Similarity search - GPCRdb

The GPCRdb similarity search tools allows a user to find the most similar receptors for a reference sequence, out of all GPCRs, or a subset selected by the user. The tools is more accurate than BLAST search, since it uses curated, structure-based alignments, but only works on sequences that are already in the database. Using the tool is a three step process.

  1. The user is first presented with a reference receptor selection page.
  2. Once a reference receptor has been selected, the user is presented with a sequence segment selection page. The user can select one or more sequence segments, and/or expand each segment to select the residues within it individually. Residues selected individually are grouped into a custom sequence segment.
  3. The third step is selecting a comparison receptor set. The selected receptors will be compared to the reference receptor based on the selected sequence segments, and their similarities computed. The user can select as many receptors as he/she wishes (WARNING: selecting a large number of receptors increases loading time).

After completing these three steps, an alignment is displayed, with the receptors in the comparison set ranked by similarity to the reference receptor. The three columns to the right of the receptor ID show three computed properties:

  • Sequence identity (%I): The percentage of identical amino acids.
  • Sequence similarity (%S): The percentage of similar amino acids (where similar is defined as BLOSUM62 score > 0).
  • Similarity score (S): The sum of every position’s BLOSUM62 score.

To display the sequence number of an aligned residue, as well as generic number indices, hover the mouse over it.

Similarity search - BLAST

The BLAST based similarity search is an alternative to the GPCRdb similarity search that works for any user submitted sequence (the query sequence does not have to be in GPCRdb already). The runs a standard BLAST search on a custom BLAST database that contains every sequence from GPCRdb.

The results page show a list of the best BLAST hits for the submitted query sequence.

Similarity matrix

The similarity matrix tool allows a user to quickly gain an overview of the sequence identity and similarity between all sequences in a receptor family, or a custom selected group of receptors. Using the tool is a two step process.

  1. The user is first presented with a receptor selection page. Receptors can be selected individually or by family. The user can select as many receptors as he/she wishes (WARNING: selecting a large number of receptors increases loading time).
  2. After receptors have been selected, the user is presented with a sequence segment selection page. The user can select one or more sequence segments, and/or expand each segment to select the residues within it individually. Residues selected individually are grouped into a custom sequence segment.

The results are shown as a table of identities and similarities, color-coded in a red-yellow-green color scale ranging from low to high identity/similarity. Identities are shown in the lower-left half of the table, and similarities in the upper-right half.

Sequence signature tool

A sequence signature is a set of residue positions with amino acids or properties thereof that are distinctly conserved in one of two sets of proteins; and is generated to identify determinants of a biological function differing between these two sets.

The GPCRdb sequence signature tool (available under Receptors – Sequences – Similarity) receives a selection of two sets of GPCRs. These are aligned separately to calculate their individual percent conservation of amino acids or properties thereof within each column of the alignment which is indexed with a generic residue number. For each such residue position, the tool then identifies most distinctly conserved amino acid or property in either dataset and assigns a score representing the difference in percent conservation. For example, a conservation of 87 and 27 percent in the two receptor sets gives a score of 60. Finally, the sequence signature is derived by applying a score cut-off (default is 40) to extract the most distinct residue positions. This sequence signature can be downloaded as an Excel spreadsheet for further analysis, for example design of mutagenesis experiments based on signature scores and amino acids/properties. Furthermore, the sequence signature can be matched against the protein sequences of other receptors to predict which of those share the reflected function (as defined by the two original sets of proteins). This provides a sum of signature position scores that is normalised by dividing by the maximum theoretical score (if the GPCR and signature matches in all positions). All receptors are listed in decreasing score order and can be downloaded as an Excel spreadsheet for example to select GPCRs for validating functional characterization.

Structures

Structure browser

The structure table shows an annotated list of published GPCR structures. The table can be sorted by each column by clicking on the header. The search fields below each header can be used to filter the structures, e.g. show only those with a co-crystallized agonist or X-ray resolution < 2.5 Å.

To view an alignment of the selected structure sequences, click the “Align” button. Downloading multiple structures at the same time is available with the “Download” button. The “Show representative” button will filter the table by only showing structures that are the representative structures for the given receptor in each state. These structures are the ones which have the most GPCRdb generic numbered positions present in the structure and have high resolution.

Structure state

A 7TM Open IC was determined for all class A structure templates by subtracting the 3x44-7x52 C alpha distance from the 2x41-6x38 C alpha distance and for all class B structure templates by subtracting the 3x44-7x51 C alpha distance from the 2x41-6x33 C alpha distance. These values are now provided in the Structure Browser (gpcrdb.org/structure) in the 7TM Open IC (Å) column.

TM2 TM6 TM3 TM7 Class Inactive Intermediate Active
2x41 6x38 3x44 7x52 A <2 2<=x<=7.15 >7.15
2x41 6x33 3x44 7x51 B <2.5 2.5<=x<=5.75 >5.75
2x41 6x38 3x44 7x52 F <0 0<=x<=2 >2

All Class C structures are, so far, in the inactive state.

This definition is an approach more based on structure than function. The 7TM Open IC resembles a general openness of the intracellular side of the receptor where coupling of a signaling protein can occur. Exceptions: There are structures where based on ligand properties the structure should be identified as inactive, however due to a fusion protein on the intracellular side of the receptor, the conformation is more similar to an active structure (e.g. 5NDD, 5NJ6). Another unusual example is one of the Platelet-activating factor receptor structures (5ZKP), where H8 turns inward the helical bundle, blocking the chance of signal protein coupling while pushing TM2 outwards.

Structure statistics

The statistics page shown a bar graph showing the number of structures available by year (and grouped by the endogenous ligand type of the receptors), a bar graph showing the resolution ranges of the available structures, and phylogenetic trees for each receptor class, with receptors with determined structures highlighted.

The graphs are automatically updated when new data is added to GPCRdb, making them ideal for use in publications and presentations.

Structure superposition

The superposition tool allows users to upload two or more structures (or models) and superpose them based on a user-specified segment selection. Using the tool is a two step process.

  1. Select structures to upload. Only on reference structure can be uploaded, but multiple structures to superpose on the reference can be uploaded. To select many structures for upload, hold down the Control key (or Command on Mac) while selecting
  2. After structures have been uploaded, the user is presented with a sequence segment selection page. The user can select one or more sequence segments, and/or expand each segment to select the residues within it individually. Residues selected individually are grouped into a custom sequence segment.

PDB file residue numbering

The PDB file residue numbering tool adds generic residue numbers from GPCRdb to any GPCR structure or model. This can be useful when comparing structures visually.

A user simply uploads her structure and downloads a modified version of that structure, where b factors of certain atoms have been replaced with generic numbers. Note that CA atoms will be assigned a number in GPCRdb notation, and N atoms will be annotated with Ballesteros-Weinstein scheme.

On the structure download page, users can download scripts to visualize the generic numbers in PyMOL and Maestro.

Template selection

Using the template selection tool is a one step process. The user is first presented with a reference receptor selection page. The selected reference receptor will be compared to the published GPCR structures, making it a useful tool for selecting templates for homology modeling.

Once a reference receptor has been selected, an annotated table of published GPCR structures, ranked by similarity to the selected reference receptor is shown. The table can be sorted by each column by clicking on the header. The search fields below each header can be used to filter the structures, e.g. show only those with a co-crystallized agonist or X-ray resolution < 2.5 Å.

Structure Constructs

Data

Structure construct data was parsed from PDB files, which were fetched from RCSB PDB (www.rcsb.org). Deleted receptor regions were identified from the missing ranges in the DBREF lines. The PDB file protein sequence was matched to the wild-type sequence as a final validation of deletions and mutations. Residue annotations, including mutations, and additional protein sequences, such as linkers and fusion proteins, were imported from the SIFTS database. Thereafter we fetched data on structure experimental values (resolution, crystal growth, r-factor and method) from the PDBe REST API (https://www.ebi.ac.uk/pdbe/api/pdb/entry/experiment/pdb_code), which also aided the manual annotation effort. We manually annotated a comprehensive collection of GPCR structure experiment methods and reagents from structure publications into a Microsoft Excel file, which was imported to GPCRdb.

Furthermore, inserts not present in the final structure construct and therefore not available from the PDB and the effects of receptor mutations were also annotated and imported in the same way through separate spreadsheets. The completed integrated data can be retrieved at the GPCRdb repository (https://github.com/protwis/gpcrdb_data/tree/master/structure_data/construct_data).

Construct alignments

Construct alignments allow for quickly browse different constructs. There are three main views: 1. Browser – In a table format it allows for quickly sorting and filtering the dataset. Upon sorting/filtering it is possible to switch back to the schematic views with the subset of data. 2. WT (Wild-type) schematic – This view takes the perspective of the wild-type sequence so it becomes clear to see what parts of the proteins have been deleted and mutated. 3. Construct schematic (default view) – This view show the final construct schematic – here additional inserts are shown (Signal-tags, cleavage tags, fusion proteins).

Furthermore, there are filter options on all views where you can filter by several options to narrow the view to the structures of main interest.

If one wants to analyse the sequences themselves, it is possible to select several constructs and segments and with the “Sequence” button align these to get view shown below.

Examples of alignment of some constructs

Figure 1. What can be seen here is the alignment around TM5 / ICL3 segments to compare cut-sites among different constructs.

Construct design tool

Example snakeplot representation of a construct under construction.

Figure 2. Example snakeplot representation of a construct under construction.

When using the Construct design tool on a target receptor, the user first selects where a fusion protein should be inserted. This selection affects the tools design for the given target by only using as a basis the data that were obtained from templates with the same state and region of fusion. For all types of construct modifications the presented suggestions are ranked by target-template homology levels: same receptor, receptor family, or class or a different class (B2 to B1 and Taste 2 receptors to A). Whereas the homology is the default sorting for all types of construct modifications, for targets without a clear closest template users also have the option to instead sort by the frequency that a given modification has been observed among all unique receptors. N- and C-terminal truncation: To allow for cross-receptor comparisons, the lengths of N- and C-termini are defined as the number of residues that have been preserved before the start of the first transmembrane helix (TM1) and after the end of helix 8 (H8), respectively. Data from N-terminal protein fusions, with e.g. T4L or BRIL, are excluded unless the user has selected a fusion in this region. Fusion protein sites: Reflecting the structures obtained hitherto, loop fusions are placed in the second and third intracellular loops for the classes B and C, and A and F, respectively using the GPCRdb generic residue numbering scheme. If the user did not select a loop fusion, long ICL3 loops (>8 residues) are instead assigned suggestions of deletion sites from non-fused and N-terminally fused constructs.

The suggestions of stabilising mutations span a number of specific design rules (https://github.com/protwis/gpcrdb_data/raw/master/structure_data/Mutation_Rules.xlsx) which are both data- and rationale-driven and cover five overall concepts:

  1. Homology: This concept infers a mutant position and amino acid if the target is the same receptor or a member of the same receptor family. For the classes B-F, which are smaller and have less data than class A, mutations will also be informed from any member of the same class.
  2. Common mutations: These are mutations that have been utilised within several distinct receptor families, but not yet that of the selected receptor target.
  3. Conservation: This concept introduces residues that are missing in the target but at least 70% conserved in the receptor family or class. For the positively charged residues H, K and R a lower, 40% conservation is used in order to incorporate multiple position at the tips of the transmembrane helices wherefrom these residues can interact with the polar head groups of the cell membrane. Furthermore, the low-propensity residues G and P are treated separately (below) and cysteine, which can form disulphide bridges, is excluded.
  4. Helix propensity: This concept increase helix propensity by replacing Proline and Glycine residues that are present in the target receptor but poorly conserved in the receptor family or class with Alanine. Glycine residues within four positions from a helix end are preserved, as they can be crucial for the transition to a loop structure.
  5. State switches: These residues form interactions that are unique for either inactive or active receptor states. The state selected by the user will be targeted by adding and removing residues with such interactions.
Example design table for a construct under construction.

Figure 3. Example design table for a construct under construction.

Using the Construct Design Tool

We have three seperate applications modes, as listed below.

  1. Truncation/fusion scan (generates constructs covering all the unique combinations of user-selected truncations and/or fusions).
  2. Mutation scan (first designs one reference construct –which can include mutations with known stabilising effect– and then selects a number of stabilising mutations which are individually added to generate as many constructs).
  3. Custom constructs (constructs are designed one at the time by repeated custom selection of truncations, fusions and mutations).

The two scanning applications, i.e. 1-2, contain the option to automatically select a given number of top ranking suggested truncations/fusions and mutations, respectively. For all three applications it is possible to edit the generated table of constructs, i.e. modify or remove an existing or add a new construct.

Experiment browser

The experiment browser shows all the annotated experimental values for constructs. Since there are so many it is possible to toggle the visibility of several types of data (Expression, Solubilization, Purification and Structure determination). Similarly to other views, it is possible to filter by almost any column to narrow down the dataset to the most relevant.

Truncation & Fusion analysis

These pages are used for detailed analysis for different key features for construct design. It shows the distribution of different cut sites in N-term, C-term and the loops (with and without fusion proteins).

Mutation analysis

We have three pages for mutation analysis in relation to construct structures.

Mutation Browser Is our manually annotated structure mutations which notes whether a mutation has an effect on thermostability, receptor expression and many other features. As with most of our views it is possible to filter and sort by most columns.

StaMutAnalyser (Stabilising Mutation Analyser) Is a tool developed to analyse mutations that cover several receptor to help determine their rationale. Whether the site is known to participate in either Ligand-binding, Arresting-binding or G-protein-binding can be seen to the right most columns with a count of distinct receptors known to have an interaction at a given position. Functional sites, such the Sodium Ion site, is also annotated to correlate that with the mutation.

Mutation substitution matrix shows the frequencies (no. GPCRs) of thermostabilising mutant amino acid substitutions across all positions of the receptor sequences/structures.

Mutations

Mutation browser

The mutant browser allows users to view mutant data for a receptor or receptor family and highlight mutants on receptor diagrams. Using the tool is a two step process.

  1. The user is first presented with a receptor selection page. Receptors can be selected individually or by family. The user can select as many receptors as he/she wishes (WARNING: selecting a large number of receptors increases loading time).
  2. After receptors have been selected, the user is presented with a sequence segment selection page. The user can select one or more sequence segments, and/or expand each segment to select the residues within it individually. Residues selected individually are grouped into a custom sequence segment.

The results page shows a table of mutants for the selected receptors and segments. The table can be ordred and filtered by each column.

Below the table, helix box and snake plots are shown, with the mutated residues highlighted. The sequence in the plots is the consensus sequence of the selected receptors.

Below the plots, a table of every residue in the selected receptors and segments is shown, with the mutated residues highlighted.

Mutation data submission

The GPCRdb already contains the largest available set of GPCR mutants and the goal is to continuously deposit mutants into GPCRdb, now also capturing the pharmacological effect.

You can contribute to the mutational data available in GPCRdb, e.g. with data from your own lab to increase the visibility and thus the number of citations. You can also contribute with data sets gathered from the literature, which can be put into perspective by comparing to mutational effects in e.g. other GPCR subtypes by use of the visualization tools available in GPCRdb. To capture mutational data in a format that enables comparison of effect on e.g. ligand binding affinity, a standardized Excel spreadsheet has been prepared to collect the data. Please download it here, enter your data by following the instructions included in each cell and email the file to Kasper Harpsøe (kasper.harpsoe@sund.ku.dk).

A few examples of entered mutant data are available here and if in doubt please contact Kasper Harpsøe (kasper.harpsoe@sund.ku.dk) via e-mail.

Type of mutation data

The current standardized Excel spreadsheet is made for reporting mutational effects on ligand binding and function but additionally contains the possibility to report mutational effect on surface expression, basal activity and Emax.

Future plans for the GPCRdb mutational database includes the possibility to receive and display data for mutations with effect on thermo-stabilization, biased signaling, G-protein binding and dimerization and more may be added.

How will the mutation data be used?

The mutant browser allows users to browse and search the mutation database for e.g. mutations in a given receptor or sub-family of receptors, mutations in a given generic numbering position, mutations with effect on a given ligand or ligand class (and much more). It is also possible to download the mutation data of interest.

Our main focus is on how mutants affect ligand binding and function. Thus, the first visualization tools that are available in the GPCRdb are snake and helix box diagrams for mapping mutated residues on the 7TM domain plus tables for comparing mutated residues across receptor subtypes. Both diagrams and tables can be color-coded according to the fold-effect of the mutation on the desired ligand property (binding or effect – see examples). It is also possible to combine the diagrams and table with information on ligand-interacting residues annotated from experimental structures to give a structural explanation for the observed mutational effect. Furthermore, it is the intention that future versions of GPCRdb tools will additionally offer mapping of mutational data on crystal structures and homology models in 3D.

Example residue table

Figure 1. Sequence comparison of the 7TM domain binding pocket in the eight mGlu receptor subtypes with all residues that have been mutated. Color-coding: Green indicates increased binding/potency of >5-fold (light green) or >10-fold (dark green), red indicates reduced binding/potency of >5-fold (pink) or >10-fold(red), yellow indicates No/low effect (<5-fold), and grey indicates that no effect is annotated. The first two columns show generic GPCRdb residue numbers for each row of residues.

Example helix box diagram

Figure 2. A helix box diagram of the metabotropic glutamate receptors displaying mutated residue positions from the extracellular side with all residues that have been mutated. Color-coding: Green indicates increased binding/potency of >5-fold (light green) or >10-fold (dark green), red indicates reduced binding/potency of >5-fold (pink) or >10-fold(red), yellow indicates No/low effect (<5-fold), and grey indicates that no effect is annotated.

Example snake diagram

Figure 3. Snake diagram of the human β2 -adrenoceptor showing all residues (grey) for which mutational experiments have been deposited in the GPCRdb.

Sites

Ligand interactions

The ligand interaction workflow allow a user to upload a PDB file and get an analysis of protein-ligand interactions in the complex.

Site search - manual

The site search tool allows a user to search a set of receptors for a sequence motif consisting of residue positions and chemical properties. Using the tools is a two step process.

  1. The user is first presented with a receptor selection page. Receptors can be selected individually or by family. The user can select as many receptors as he/she wishes (WARNING: selecting a large number of receptors increases loading time).

  2. After receptors have been selected, the user is presented with a sequence motif selection page. Site residues should be selected individually. Clicking the down arrow button next to a sequence segment will expand the residues within that segment. Chemical features (Hydrophobic, hydrogen bond donor, etc.) should then be selected for each motif residue. When a feature has been selected, a list of amino acids that match the feature will appear to the right of the residue.

    The selected residues can be organised into separate interactions. An interaction can contain one or more residues. To add an interaction, click the ‘Add interaction’ button. Selected residues will be added to the currently active interaction (shown in bold text). To change the active interaction, click on the name of the interaction. Within an interaction, the number of residues required to match can be specified in the ‘Min. match’ selection box.

After completing these two steps, an alignment is displayed. The sequences of the selected receptors are split into “Matching sequences” and “Non-matching sequences”, according to their match of the selected site. To display the sequence number of an aligned residue, as well as generic number indices, hover the mouse over it.

Site search - from pdb complex

This is a variant of the manual site search tool, where the user can upload a PDB structure and have protein-ligand interactions automatically detected and translated into a site search. After interactions have been detected, the user can edit the definition, and continue as in a manual search.

Pharmacophore generation

The tool is based on the following paper:

K Fidom, V Isberg, A Hauser, S Mordalski, T Lehto, AJ Bojarski, DE Gloriam, “A New Crystal Structure Fragment-Based Pharmacophore Method for G Protein-Coupled Receptors”, 2015, Methods, 71, 104–112. 10.1016/j.ymeth.2014.09.009

Abstract

We have developed a new method for the building of pharmacophores for G protein-coupled receptors, a major drug target family. The method is a combination of the ligand- and target-based pharmacophore methods and founded on the extraction of structural fragments, interacting ligand moiety and receptor residue pairs, from crystal structure complexes. We describe the procedure to collect a library with more than 250 fragments covering 29 residue positions within the generic transmembrane binding pocket. We describe how the library fragments are recombined and inferred to build pharmacophores for new targets. A validating retrospective virtual screening of histamine H1 and H3 receptor pharmacophores yielded area-under-the-curves of 0.88 and 0.82, respectively. The fragment-based method has the unique advantage that it can be applied to targets for which no (homologous) crystal structures or ligands are known. 47% of the class A G protein-coupled receptors can be targeted with at least four-element pharmacophores. The fragment libraries can also be used to grow known ligands or for rotamer refinement of homology models. Researchers can download the complete fragment library or a subset matching their receptor of interest using our new tool in GPCRdb.

Sodium ion site

In many GPCRs, a sodium ion acts as an allosteric modulator stabilising the inactive state. The sodium ion site involves a number of direct and indirect polar interactions. The receptor residues D2x50 and S3x39 are known to interact frequently and directly with the sodium ion, and mutation of these residues causes loss of G protein mediated signalling (White et al., Structure, 2018; 10.1016/j.str.2017.12.013). In the “Structure Browser” under the “Receptors” menu, users can find a “Sodium ion site” column section with two columns. The first shows whether D2x50 and S3x39 are present in the wild type sequence of the structure, the second shows whether there is a sodium ion interacting with the two residues in the given structure.

Generic residue numbering

Sequence-based generic GPCR residue numbering schemes1 exist for class A (Ballesteros-Weinstein, BW2 ) B (Wootten3 ), C (Pin4 ), and F (Wang5 ). In these systems, the first number denotes the helix (1-7) and the second the residue position relative to the most conserved position, which is assigned the number 50. For example, 6.51 denotes a residue in transmembrane helix 6, one position after the most conserved residue (6.50). The reference helix conserved positions differ between the GPCR classes.

Recent GPCR crystal structures have revealed frequent helix bulges and constrictions in several transmembrane helices6 . Structural superimposition makes it clear that these cause a gap that offsets all the following residue numbers when compared to an undistorted helix, i.e. the structurally equivalent residues no longer have the same number (Fig. 1).

Examples of bulges and constrictions

Figure 1. A bulge in helix 2 of the Beta-2 adrenergic receptor (left) and a constriction in helix 4 of the Histamine H1 receptor (right) create offsets in the sequence-based generic numbers when compared to receptors that lack the bulge/constriction.

The GPCRdb numbering scheme1 is the first that is based on crystal structures and corrects for helix bulges and constrictions. GPCRdb numbers are distinguished by a unique separator x and may be used alone, e.g. 5x47, or together with one of the sequence-based schemes, e.g. 5.46x47. A bulge residue is assigned the same number as the preceding residue followed by a 1, e.g. 551 for a bulge following position 55.

GPCRdb offers a suite of tools making it easier to use generic residue numbers:

GPCRdb cross-class alignments contain each of the numbering schemes, which may be distinguished in text by appending the letter of the class, e.g. 2x52ax59b. The Lookup tables tool also provides the alternative class A numbering schemes by Oliveira7 and Baldwin/Schwartz7,8 .

References

  1. V Isberg et al., 2015, Trends Pharmacol Sci, 36(1), 22–31.
  2. JA Ballesteros and H Weinstein, 1995, Methods Neurosci, 25, 366–428.
  3. D Wootten et al., 2013, Proc Natl Acad Sci, 110(13), 5211-5216.
  4. J-P Pin et al., 2003, Pharmacol Ther, 98(3), 325-354.
  5. C Wang et al., 2014, Nat Commun, 5, 4355.
  6. R van der Kant and G Vriend, 2014, Int J Mol Sci, 15(5), 7841-7864.
  7. L Oliveira et al., 1993, J Comput Aided Mol Des, 7(6), 649–658.
  8. JM Baldwin, 1993, EMBO J, 12(4), 1693–703.
  9. TW Schwartz, 1994, Curr Opin Biotechnol, 5(4), 434–44.

Drugs

Drug statistics

The statistics page shows several graphs for FDA-approved drugs and agents in clinical trials including:

  • Class targets
  • Receptor family targets
  • Receptor targets
  • Drug molecule types
  • Mode of action
  • Disease indications
  • Phase distribution of clinical trial agents
  • Approval ovcer time

Drug target mapping

Established targets have approved drugs as defined in the Drugs@FDA database, and targets of agents in clinical trials were collected from manual annotation of CenterWatch’s Drugs in Clinical Trials database, OpenTargets, Drugbank, Pharos and company press releases. Established (red) and phase I–III (green) targets across the G protein-coupled receptor (GPCR) classes, ligand types and receptor families (from the centre to the outer ring) are shown.

The sizes of the circles represent the number of agents. For receptor families, two concentric circles are superimposed: a red circle indicating the number of approved agents (that is, which have an established target in that family) and a green circle indicating the number of agents in trials for the targets in that family. The area over which the two circles overlap is shown in brown. For example:

  • for adrenoceptors, there are 117 approved agents and 41 agents in trials, and so the red circle is larger
  • for chemokine receptors, there are 2 approved agents and 37 agents in trials and so the green circle is larger

At the family level, agents that modulate multiple receptors in the family are only counted once to determine the circle size. For individual receptors (but not families), different shades of green are used for each trial phase.

GPCRs are listed using the protein name (EntryName removed species tag) in UniProt.

Drug browser

The structure table shows the complete annotated list of DRUG-GPCR pairs. The table can be sorted by each column by clicking on the header. The search fields below each header can be used to filter the structures.

  • Hauser, A. S., Misty, A, Mathias, R., Schiöth, H. B., Gloriam, D. E., “Trends in GPCR drug discovery: new agents, targets and indications”, 2017, Nature Reviews Drug Discovery, in print, 10.1038/nrd.2017.178

Sales and prescription (NHS)

NHS sales

Every month, the National Health Service (NHS) in the UK publishes anonymised data about the drugs prescribed by general practitioners. NHS data were retrieved from openprescribing.net (DataLab-EBM, 2017, https://openprescribing.net/) (08/2017) for the list of drugs targeting GPCRs and mapped back to their reported target of therapeutic action. From the 475 queried FDA-approved drugs, data were available for 279 drugs targeting 92 distinct GPCRs (not all FDA-approved drugs are prescribed in the UK due to alternative treatments).

The actual cost is the estimated cost to the NHS, which is usually lower than Net Ingredient Cost (“the basic price of a drug, i.e. the price listed in the Drug Tariff or price lists”). Openprescribing.net provides the actual cost by subtracting the average percentage discount per item received by pharmacists based on the previous month from the Net Ingredient Cost, but adding in the value of a container allowance for each prescription item (DataLab-EBM, 2017).

Indications were grouped according to the British National Formulary (BNF), which is a reference book containing the standard list of medicines prescribed in the UK. Individual drugs can be selected (double click on colored bar chart) in the section page).

Drugs with NHS information can be filtered via the drug browser (column on the far right, NHS=yes).

Sales and variation information per drug is presented within the Estimated economic burden page. Here, sales and prescription averages are presented along with aggregated counts of putative and known functional site variants of all receptors targeted by the drug.

Estimated economic burden

The economic burden estimate was calculated using the following formula:

estimated economic burden per drug (£) = average NHS cost per drug per year (£) x %individuals with a MV in a functional site of the respective drug targets

where:

  • The average NHS cost is the average yearly cost over a 4-year period (2013-2016) per GPCR targeting drug that is listed (n=279). 2012 and 2017 have partial sales data and were not considered.
  • % Individuals is the percentage of affected individuals with a missense variant in a functional site of the respective drug target(s) (n=2,504 individuals from 1,000 Genomes Project genotype data as a representative for the UK population; this data includes non-Caucasian populations as well) (Table S9).
  • The % of affected individuals was calculated using four different criteria by considering individuals who have a variation in (i) known functional sites in both alleles (homozygous), which is the most conservative, (ii) known functional sites in at least one allele (i.e. homozygous and heterozygous), (iii) known or putative functional sites in both alleles (homozygous), and (iv) known or putative functional sites in at least one allele (i.e. homozygous and heterozygous), which is the least conservative.
  • Known functional sites include ligand binding, effector binding, PTM site, sodium binding site and micro-switches. Putative functional site include those predicted to be deleterious based on SIFT or PolyPhen (see above).

More specifically, for each drug we collected the respective targets and computed economic burden using the following four criteria above: considering (i) % individuals with homozygous alleles in known functional sites, (ii) % individuals with at least one variant allele in a known functional site, (iii) % individuals with homozygous alleles in known or putative functional sites and (iv) % individuals with at least one variant allele in a known or putative functional sites.

For these estimates, we have incorporated the following considerations (below). The economic burden estimates will vary if one scales/factors these variables differently:

  1. We have considered that each prescription (NHS data) is made for a unique individual, due to patient anonymity. Furthermore, information about the dose per prescription, and how this has been altered based on patient response is not explicitly modelled.
  2. The effect of known and putative site polymorphisms as well as homozygous/heterozygous conditions are all treated the same way. One could also obtain estimates by weighing these variables differently on a case-by-case basis for each receptor/drug.
  3. The focus has been prescription only from GPs. There might be significant additions to the economic burden if one also considers hospital prescriptions.
  4. We used the data from 1000 Genomes Project as representative of the UK population, which may vary depending on the receptor.
  5. We have not explicitly modeled the age, gender, nature of illness (chronic v/s short-term) and mutations in non-coding regions, which may affect expression level.
  • Hauser, A. S., Chavali, S., Masuho, I., Jahn, L. J., Martemyanov, K., Gloriam, D. E., Babu, M. M., “Pharmacogenomics of GPCR drug targets”, 2017, Cell 10.1016/j.cell.2017.11.033

Genetic variants

Variation statistics

The statistics page shows an overview of all non-olfactory receptors with their absolute and relative numbers of reported genetic missense variants. The average density (absolute count divided by length of receptor) is presented for each receptor family, ligand type and class.

Receptor variant browser

The selection page allows users to find a receptor by searching or browsing. The browser displays all missense and loss of function mutations for the selected receptor. Additional information for each variant is presented such as position, amino acid change, allele counts/frequencies, number of homozygotes, predicted functional impact by SIFT and PolyPhen as well as functional annotation of the specific position (PTM site, ligand-binding site, micro-switch, G protein interaction ..)

All variants are highlighted on helix plot and snake-plot diagrams with predicted deleterious variants (by either SIFT or PolyPhen) highlighted in red or green, respectively.

  • Hauser, A. S., Chavali, S., Masuho, I., Jahn, L. J., Martemyanov, K., Gloriam, D. E., Babu, M. M., “Pharmacogenomics of GPCR drug targets”, 2017, Cell 10.1016/j.cell.2017.11.033

Web services

Most data in GPCRdb is available programmatically via a REST API.

API reference

Each endpoint is described in the API reference.

Examples

Python 3 with requests

This is the recommended approach. Requires installation of the requests library.

import requests

# fetch a protein
url = 'https://gpcrdb.org/services/protein/adrb2_human/'
response = requests.get(url)
protein_data = response.json()
print(protein_data)
print(protein_data['sequence'])

# fetch an alignment
url = 'https://gpcrdb.org/services/alignment/protein/adrb1_human,adrb2_human,adrb3_human/TM3,TM5,TM6/'
response = requests.get(url)
alignment_data = response.json()
for protein, sequence in alignment_data.items():
    print(protein)
    print(sequence)

Python 3 with urllib

from urllib.request import urlopen
import json

# fetch a protein
url = 'https://gpcrdb.org/services/protein/adrb2_human/'
response = urlopen(url)
protein_data = json.loads(response.read().decode('utf-8'))
print(protein_data)
print(protein_data['sequence'])

# fetch an alignment
url = 'https://gpcrdb.org/services/alignment/protein/adrb1_human,adrb2_human,adrb3_human/TM3,TM5,TM6/'
response = urlopen(url)
alignment_data = json.loads(response.read().decode('utf-8'))
for protein, sequence in alignment_data.items():
    print(protein)
    print(sequence)

Python 2 with urllib2

from urllib2 import urlopen
import json

# fetch a protein
url = 'https://gpcrdb.org/services/protein/adrb2_human/'
response = urlopen(url)
protein_data = json.loads(response.read())
print protein_data
print protein_data['sequence']

# fetch an alignment
url = 'https://gpcrdb.org/services/alignment/protein/adrb1_human,adrb2_human,adrb3_human/TM3,TM5,TM6/'
response = urlopen(url)
alignment_data = json.loads(response.read())
for protein, sequence in alignment_data.iteritems():
    print protein
    print sequence

Contributing to the project

We welcome all contributions to the project. If you have an idea for a feature you would like to implement, improvements to make, or data to add/update, please contact us.

As a programmer

We use languages/tools such as Python, Javascript, Django, PostgreSQL, and Git to build GPCRdb. Prior knowledge of these tools is helpful, but not necessary.

As a data curator

Data curation tasks involve e.g. sequence alignments, analysis of protein structures and collection of mutation data.

Local installation

For development

To start working on GPCRdb, fork the source code on GitHub, and use Vagrant to set up a development environment.

Developer guide at GitHub

For internal use

To install GPCRdb for local use at your company or organization, provision a local server with Puppet.

Production guide at GitHub

Coding style

We (mostly) follow the style guide from the the Django project. Unless otherwise specified, follow this guide. Please read this guide, use it, and feel free to point out if existing code does not comply with the style guide.

Examples

  • Max line length is 119 characters

  • Indentation is 4 spaces:

    for protein in proteins:
   print(protein)

  • Comments start with a # and a single space:

    # this is a comment

  • Docstrings use “”“:

    """This is a docstring"""

  • Use lower case letters and underscores for variable and function names, upper case letters and underscores for constants, and InitialCaps for class names:

    this_is_a_variable = True

THIS_IS_A_CONSTANT = True

def this_is_a_function():
   pass

class ThisIsAClass:
   __init__(self):
      pass

  • Class definitions are followed by 2 blank lines:

    class ThisIsAClass:
   __init__(self):
      pass


class ...

  • Import statements are grouped in three categories(django, project, and other), separated by one blank line, and followed by 2 blank lines:

    from django.conf import settings

from protein.models import Protein

import yaml


class ...

Keep your code clean

Before committing, review the changes you have made (using git diff or a GUI like SourceTree) and make sure the code you are committing is working, and relevant. Never commit lines of code that are commented out (comments are for, well, comments), or print statements that you used for debugging.

Reload database from dump

  • Go to the project root directory on your virtual machine:

    cd /protwis/sites/protwis

  • Delete the current database (password: protwis):

    psql -U protwis -h localhost -d protwis -c 'drop schema public cascade; create schema public;'

  • [Optional] Download the newest dump from gpcrdb:

    curl https://files.gpcrdb.org/protwis_sp.sql.gz > ~/protwis.sql.gz
gunzip ~/protwis.sql.gz

  • Load the dump (Either from default location or a location of your choosing):

    psql -U protwis -h localhost -o protwis < ~/protwis.sql;

Building a local database from source data

  • If you have not completed the local installation of GPCRdb, please do so before continuing.

  • Open up a terminal and clone the gpcrdb_data repository from GitHub:

    cd ~/protwis_vagrant
git clone https://github.com/protwis/gpcrdb_data.git shared/data/protwis/gpcr

  • Log into the vagrant VM:

    vagrant ssh
cd /protwis/sites/protwis

  • Clean the current database schema (password: protwis):

    psql -U protwis -h localhost -d protwis -c 'drop schema public cascade; create schema public;'

  • Run migrations:

    /env/bin/python3 manage.py migrate

  • Start the build process:

    /env/bin/python3 manage.py build_all -p 4 -t

This will build a test version of the database using only the proteins for which a structure has been determined. For a full build, remove the -t flag from the build_all command (NOTE: a full build takes a long time, and should not be run on the development virtual machine)

About GPCRdb

GPCRdb offers reference data and easy-to-use web tools and diagrams for a multidisciplinary audience investigating GPCR function, drug design or evolution. It stores a manual annotation of all GPCR crystal structures, the largest collections of receptor mutants and reference sequence alignments. The tools run directly in the web browser allowing for swift analysis of structures, sequence similarities, receptor relationships, and ligand target profiles. Diagrams illustrate receptor sequences (snake-plot and helix box diagrams) and relationships (phylogenetic trees). A visual overview can be seen in the GPCRdb poster.

Background and development

The GPCR database, GPCRdb was started in 1993 by Gert Vriend, Ad IJzerman, Bob Bywater and Friedrich Rippmann. Over two decades, GPCRdb evolved to be a comprehensive information system storing and analysing data. In 2013, the stewardship of GPCRdb was transferred to the David Gloriam group at the University of Copenhagen, backed up by an international team of contributors and developers from the EU COST Action ‘GLISTEN’.

Contact

To contact the authors of GPCRdb, please use the e-mail address: info@gpcrdb.org.

Contributors of data and development

University / Institute Country Collaborators Data Development
MRC Laboratory of Molecular Biology UK Madan Babu G protein alignments  
Paul Scherrer Institute Switzerland Xavier Deupi Crystallisation database  
Philipps-Universität Marburg Germany Peter Kolb   Virtual screening
Pompeu Fabra University Spain Jana Selent   Molecular dynamics
Radboud University Netherlands Gert Vriend   Alignment methods
University of Bonn Germany Anke Schiedel Mutation data  
University of Copenhagen Denmark Hans Bräuner-Osborne Mutation data  
University of Southern California USA Ray Stevens   Construct design tool
University of Warsaw Poland Slawomir Filipek Mutation data  
Uppsala University Sweden Hugo Gutiérrez-de-Terán Mutation data PyMol plugin
VU Amsterdam Netherlands Chris de Graaf Mutation data Knime workflows

Citing GPCRdb

If you use GPCRdb in your work, please cite one or more of the following:

Main reference for GPCRdb

  • Pándy-Szekeres G, Munk C, Tsonkov TM, Mordalski S, Harpsøe K, Hauser AS, Bojarski AJ, Gloriam DE. GPCRdb in 2018: adding GPCR structure models and ligands. 2017, Nucleic Acids Res., Nov 16. 10.1093/nar/gkx1109

Introduction to new users (review)

  • Munk, C., Isberg, V., Mordalski, S., Harpsøe, K., Rataj, K., Hauser, A. S., Kolb, P., Bojarski, A. J., Vriend, G. , and Gloriam, D. E. GPCRdb: the G protein-coupled receptor database – an introduction. 2016, Br J Pharmacol, May 8. 10.1111/bph.13509

Structure-based alignments and generic residue numbering

  • V Isberg, C de Graaf, A Bortolato, V Cherezov, V Katritch, F Marshall, S Mordalski, J-P Pin, RC Stevens, G Vriend, DE Gloriam, “Generic GPCR Residue Numbers - Aligning Topology Maps While Minding The Gaps”, 2015, Trends Pharmacol Sci, 36(1), 22–31. 10.1016/j.tips.2014.11.001
  • R van der Kant, G Vriend, “Alpha-Bulges in G Protein-Coupled Receptors”, 2014, Int J Mol Sci, 15(5), 7841-7864. 10.3390/ijms15057841

GPCR drugs and targets

  • Hauser, A. S., Misty, A, Mathias, R., Schiöth, H. B., Gloriam, D. E., “Trends in GPCR drug discovery: new agents, targets and indications”, 2017, Nature Reviews Drug Discovery, in print, 10.1038/nrd.2017.178

GPCR-G protein selectivity

  • Flock, T., Hauser, A. S., Lund, N., Gloriam, D. E., Balaji, S., & Babu, M. M., “Selectivity determinants of GPCR–G-protein binding.”, 2017, Nature, May 18;545(7654):317-322 10.1038/nature22070

Mutation design tool

  • C Munk, K Harpsøe, A Hauser, V Isberg, DE Gloriam, “Integrating structural and mutagenesis data to elucidate GPCR ligand binding”, 2016, Curr Opin Pharcol, 30, 51–58. 10.1016/j.coph.2016.07.003

Crystal structure fragment-based pharmacophore models

  • K Fidom, V Isberg, A Hauser, S Mordalski, T Lehto, AJ Bojarski, DE Gloriam, “A New Crystal Structure Fragment-Based Pharmacophore Method for G Protein-Coupled Receptors”, 2015, Methods, 71, 104–112. 10.1016/j.ymeth.2014.09.009

GPCR specific PDF reader

  • B Vroling, D Thorne, P McDermott, TK Attwood, G Vriend, S Pettifer, “Integrating GPCR-specific information with full text articles”, 2011, BMC Bioinformatics, 12, 362. 10.1186/1471-2105-12-362

Older GPCRdb articles

  • V Isberg, S Mordalski, C Munk, K Rataj, K Harpsøe, AS Hauser, B Vroling, AJ Bojarski, G Vriend, DE Gloriam. “GPCRdb: an information system for G protein-coupled receptors”, 2016, Nucleic Acids Res., 44, D356-D364. 10.1093/nar/gkv1178
  • V Isberg, B Vroling, R van der Kant, K Li, G Vriend* and DE Gloriam*, “GPCRDB: an information system for G protein-coupled receptors”, 2014, Nucleic Acids Res., 42 (D1), D422-D425. 10.1093/nar/gkt1255
  • B Vroling, M Sanders, C Baakman, A Borrmann, S Verhoeven, J Klomp, L Oliveira, J de Vlieg, G Vriend, “GPCRDB: information system for G protein-coupled receptors”, 2011, Nucleic Acids Res., 39(suppl 1), D309-19. 10.1093/nar/gkq1009
  • F Horn, E Bettler, L Oliveira, F Campagne, FE Cohen, G Vriend, “GPCRDB information system for G protein-coupled receptors”, 2003, Nucleic Acids Res., 31(1), 294-297. 10.1093/nar/gkg103
  • F Horn, G Vriend, FE Cohen, “Collecting and harvesting biological data: the GPCRDB and NucleaRDB information systems”, 2001, Nucleic Acids Res., 29(1), 346-349. 10.1093/nar/29.1.346
  • F Horn, J Weare, MW Beukers, S Hörsch, A Bairoch, W Chen, Ø Edvardsen, F Campagne, G Vriend, “GPCRDB: An information system for G protein-coupled receptors”, 1998, Nucleic Acids Res., 26(1), 275-279. 10.1093/nar/26.1.275

Acknowledgements

Welcome to the GPCRdb (G Protein-Coupled Receptor database) acknowledgement page, which has two sections written by the current and former heads of GPCRdb, David E. Gloriam and Gerrit Vriend, respectively.

GPCRdb versions since 2013

By David E. Gloriam, University of Copenhagen, Denmark

Firstly, we would like to thank the founding father and two-decade protector of GPCRdb, Gerrit Vriend for so generously giving us the database as a gift without reservations. We promise to do the best to build on its legacy, going forward in the spirit of engaging and serving the GPCR community. The former lead developer Bas Vroling played a large role in making the transition of the data and previous codebase possible.

The first Copenhagen version of GPCRdb was the Tools subsite published in NAR, 2014. This sprung mainly from a series of computational drug design data and tools developed by Vignir Isberg during his PhD studies. As a lead developer he has driven the database far beyond anticipation, coordinating a team of international developers with enthusiasm and persistence. The past and current members in the Gloriam group have preserved the cross-fertilisation between developers and users. Kasper Harpsøe has taken a main role in the user expertise and development of the new format for mutant data submissions and storage. With the risk of forgetting someone along the way to the current wholly re-coded current version, we simply refer to the author lists of the various GPCRdb publications.

GPCRdb would not have been where it is without the GLISTEN EU Cost Action, coordinated by Peter Kolb and Chris de Graaf. You brought us into the party – allowing us to have satellite meetings for the international GPCRdb developers and contributors at each of the biannual GLISTEN meetings. The local organisers have so kindly provided room and practical coordination. Furthermore, the GLISTEN financial support made possible a number of in- and outgoing short-term scientific visits to set-up and build collaborations.

A big thanks goes Andrzej Bojarski and his group to whom we own thanks for most of the work behind the GPCRdb structure tools and phylogenetic trees. You generously shared so much of your time, and arranged for short- and long-term visits to facilitate the joint programming.

The whole GuideToPharmacology team is acknowledged for its openness to setting up our collaboration with mutual cross-linking, web services and GPCRdb’s adoption of the official receptor nomenclature. You have served as a true inspiration from a much larger resource that has walked many of the paths of database development and curation before.

Finally, we would like to extend thanks to newly established collaborations. Xavier Deupi and his lab are acknowledged for choosing to work with GPCRdb, while sharing the local expertise. We thank Raymond C. Stevens and Michael Hanson for welcoming GPCRdb as a partner to the GPCR Consortium, which holds great promise to be synergistic resources/initiatives.

GPCRdb versions 1993-2013

By Gerrit Vriend, Radboud University, Nijmegen, Netherlands

The GPCRdb was started in the early 90’s when Bob Bywater, Ad IJzerman, Friedrich Rippmann, and Gert Vriend organized a series of small GPCR workshops at the EMBL. Before the introduction of the first browsers, the GPCRdb worked as an automatic Email answering system that could send sequences, alignments, and homology models to the users.

In 1994 the internet was firmly established in its present form, and money was obtained from the fourth EU framework to set up the GPCRdb. Florence Horn joined us to do this project. When she left us at the end of a four-year post-doc period the GPCRdb was firmly established as the prime source of information for GPCR data.

Examples of bulges and constrictions

Figure 1. GPCRdb until 2006.

In 2007 TIPharma offered us the possibility to revive the GPCRdb. Bas Vroling joined the team and revived the GPCRdb. We would also like to thank NBIC for their support. This page would not be complete without Laerte Oliveira. Ever since the start of the GPCRdb project Laerte has been our GPCR dictionary. He knows the literature, he knows all sequences by hearth, he is responsible for the alignments, and for a series of innovations. Laerte recently retired, but he is still our full-time adviser.

Examples of bulges and constrictions

Figure 2. GPCRdb 2007-2013.

Many people have contributed over the years to the shape of the GPCRdb that you see now. Rob Hooft was, en Maarten Hekkelman now is our bit and byte guru. Maarten also wrote the profile BLAST. Fabien Campagne wrote the snake plot software for us. Margot Beukers, Fred Cohen, Oyvind Edvardsen, Kurt Kristiansen, have been involved in the mutant section of the GPCRdb; Oyvind and Kurt made tinyGRAP that now is integrated in the GPCRdb. Wilma Kuipers, Nora vd Wenden, Mike Singer, and Frank Kolakowsky were good colleagues and intellectual sparring partners that helped shape the GPCRdb in its early days. Lisa Holm, Karl Aberer, Amos Bairoch, Nigel Brown, Antonio Paiva, Thure Etzold, and Antoine Daruvar have over the last two decades all contributed to the GPCRdb.

Meetings with GPCRdb representation

2020

2019

2018

  • Jul, 9th GLISTEN meeting in Berlin, Germany.

2017

2016

2015

2014

2013

Linking to GPCRdb

To link GPCRdb protein pages, download the Uniprot mapping file and use the following link format:

https://gpcrdb.org/protein/{gpcrdb_id}/

For example, for the 5-HT2A receptor, the link is:

https://gpcrdb.org/protein/5ht2a_human/

External GPCR sites

Modeling servers

GPCRM

GPCRM is a novel method for fast and accurate generation of GPCR models using averaging of multiple template structures and profile-profile comparison. In particular, GPCRM is the first GPCR structure predictor incorporating two distinct loop modeling techniques: Modeller and Rosetta together with the filtering of models based on the Z-coordinate.

scPDB

To assist structure-based approaches in drug design, we have processed the PDB to identify binding sites suitable for the docking of a drug-like ligand and we have so created a database called sc-PDB. The sc-PDB database provides separated MOL2 files for the ligand, its binding site and the corresponding protein chain(s). Ions and cofactors at the vicinity of the ligand are included in the protein.

GPCR-SSFE

The GPCR-Sequence-Structure-Feature-Extractor (SSFE) database provides template suggestions and homology models of the helical regions of 5025 family A GPCRs. SSFE is based on our published workflow for identifying key sequence and structural motifs in family A GPCRs which is used to guide template selection and build homology models.

GOMoDo

This webtool performs automatic homology modeling and ligand docking of GPCR receptors. It uses HHsearch package 1.5.1 for performing sequence alignment. Only GPCR templates are chosen to build 3D model of given sequence by using Modeller 9.10. The obtained 3D model can be verified also with the VADAR server, and then docked with ligands uploaded by users with both Autodock VINA or HADDOCK. Binding pockets can be predicted by the FPOCKET, and structural alignment of models needed for VINA docking is performed by LOVOALIGN.

GPCR-ModSim

This server was created to allow any researcher with interest in GPCRs to obtain the most accurate structural and dynamic information for a given receptor. Here, you can generate a homology-based 3D model of your query GPCR sequence, and/or further equilibrate your GPCR structure with our all-atom Molecular Dynamics simulation protocol.

Others

Guide to Pharmacology

Founded in 1959 as a section of the International Union of Physiological Sciences, the International Union of Basic and Clinical Pharmacology (IUPHAR) has been independent since 1966. IUPHAR is a member of the International Council for Science (ICSU) and participates in the work of its scientific committees. It receives international recognition, particularly by the United Nations Educational, Scientific and Cultural Organization (UNESCO).