Welcome to GPCRdb’s documentation

GPCRdb contains data, diagrams and web tools for G protein-coupled receptors (GPCRs). Users can browse all GPCR crystal structures and the largest collections 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 in 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 lates 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 structure data in GPCRdb.

  • The structure 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, hightlight 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 recptor 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.

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.

Phylogeneric 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 similarites, 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 similarites in the upper-right half.

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 structures’ sequences, click the “View alignment” button.

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.

Mutations

Mutation browser

The mutant browser allows users to view mutant data for a receptor or receptor family and hightlight 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 selecetion 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.

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.

Web services

Most data in GPCRdb is available pragrammatically 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 requests module.

import requests

# fetch a protein
url = 'http://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 = 'http://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 = 'http://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 = 'http://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 = 'http://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 = 'http://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.

Instructions on GitHub

For internal use

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

Instructions on 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 http://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.

Citing GPCRdb

If you use GPCRdb in your work, please cite:

  • 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
  • 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

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-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, in press. 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, 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

2016

2015

2014

2013

<<<<<<< .merge_file_DY1J7d .. _1st GLISTEN meeting: http://www.biomodellab.eu/1glisten/welcome/ ======= .. _1st GLISTEN meeting: http://www.biomodellab.eu/1glisten/welcome/ >>>>>>> .merge_file_uujUNu

Linking to GPCRdb

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

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

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

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

External GPCR 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.