Exercises in ChemProt and network pharmacology
During this session you will have hands-on training on our new web-server,
ChemProt. ChemProt gives you bioactivity information associated to small
compounds. One specificity of ChemProt is that you can get biological information for a protein
when is part of disease complexes.
The second part of the exercise will focus on the network pharmacology profile of a
specific drug compound, based on protein-protein interaction data extracted from
the InWeb interactome network of CBS. The networks will be visualized and analyzed using Cytoscape.
The query drug is citalopram, which is a well-known antidepressant defined as SSRI
(Selective Serotonin-Reuptake Inhibitor).
⇒ Go to the ChemProt server
⇒ Type Citalopram in the "Type a compound name:" field or paste in the SMILES for the Citalopram in the
text box (1).
Click on Submit (2).
The output of ChemProt contains known bioactivity information associated to this drug that is gathered from diverse sources and for different species.
Small molecules that are structurally similar to the query compound are also given at the end of the list.
By moving the cursor on the Compound ID (in pink) you can see the 2D structure and general physico-chemical properties associated to the compound.
By clicking on Disease Complexes (last column) you will be directed to a server that takes the protein
target as input and retrieves its protein-protein interaction complexes. If you click on a complex, you can see both the schematic
view of the network as well as relevant biological information from a variety of sources (OMIM, GO terms, HPA,...).
Q1: How many proteins associated to Citalopram?
Hint: You can download the output file and sort the information by proteins in Excel
Click on one disease complex associated to a protein target (e.g. Human Dopamine Receptor D4).
Q2: What are the diseases associated to this target?
Q3: In which tissues is its protein-protein interaction complex essentially expressed?
Network pharmacology of citalopram
You need to download and install this version of Cytoscape 2.6.3 to get the plugins to work.
In the second part of the exercise, we will use Cytoscape to visualize and analyze the network pharmacology of the antidepressant citalopram.
We gathered from the literature 49 genes annotated to citalopram and used them as queries to form a protein-protein interaction network. The output network consists of 629 proteins with 4141 interactions.
First, you need to download the following plugins for Cytoscape:
NetworkAnalyzer.zip (Download and unzip
it and save the all 24 JAR-files in your plugin-directory.
Mac OS X /Applications/Cytoscape_v2.6.0/plugins
Windows XP C:/Program Files/Cytoscape_2.6.0/plugins)
MCODE v.1.31 (install from Cytoscape)
jActiveModules.jar v. 2.23 (install from Cytoscape)
The two last plugins can be downloaded directly from Cytoscape: (Plugins ⇒ Manage Plugins ⇒ Analysis)
To visualize the citalopram network, open Cytoscape and load the file citalo_ppi.cys by selecting from the menu: File--> Open --> citalo_ppi.cys.
Network layout and nodes selection
You can try different layouts (circular, organic, hierarchical and random) by selecting the appropriate layout in
the "yFiles" under Layout. One of the most common layouts for network biology is the organic layout (similar to the spring layout).
In the main Cytoscape window (with the network view) you can select and move nodes by clicking and dragging them with the left mouse button.
Each node has an ID label. The node representing serotonin transporter is number 14424. Instead of searching by ID we can change the
search option by clicking on:
Now we can search after gene names (5).
- Configure search option (1)
- click on dropdown menu (2)
- change to Label (3)
- click on Apply (4)
Enter SLC6A4 in the search field (5) and node 14424 will be highlighted in yellow on the network.
Then you can select the first neighbors of this node: Select --> Nodes --> First neighbors of selected nodes. You should see a network with several yellow nodes in the center.
- Q4: How many proteins interact directly with SERT (SLC6A4)?
- Q5: Have these proteins been already associated to SERT in the literature? (search in PubMed)
- Q6: Have they been associated to citalopram?
In this part you will work with the NetworkAnalyzer plugin.
First deselect all nodes in the network (just click somewhere on the white space). Then apply the NetworkAnalyzer
plugin to the network by selecting Network Analysis from the Plugins menu followed by Analyze Network (treat the
network as undirected). Ignore the update version of Networkanalyzer (press OK). This should open a "Simple Parameters" window with some statistics. Browse through the various network statistics/parameters and try to answer the following questions:
- Q7: What is the average degree (connectivity) of the network?
- Q8: What is the network diameter, radius and density?
NetworkAnalyzer Online Help
Identification of clusters
As the average cluster coefficient is relatively high, we expect that some clusters in the network are formed. We will try to identify clusters using the MCODE algorithm.
To start MCODE: Plugins--> MCODE --> Start MCODE. At the bottom of the MCODE panel (bottom left), click Analyze.
This will identify several complexes. By clicking on complex 1, the complex will be highlighted in yellow on the network
- Q9: Is SERT or a protein neighborhood of SERT part of the cluster?
- Q10: How many proteins are included in this cluster?
- Q11: After both exercises, what do you think about the definition of citalopram as a SSRI (Selective Serotonin-Reuptake Inhibitor)?