SynSys is "A European expertise Network on building the synapse".
SynSys scientists have attracted money from the EU to do novel work on the
synapse. Only by bringing together a large European group of specialists, each
having their own expertise in synapse experiments and computation SynSys can
achieve a new level of understanding of the synapse. In addition, SynSys will
collaborate closely with other consortia that study the synapse and the brain.
The consortium has 16 partners
Europe that work together to understand the function of the synapse.
SynSysNet provides a highly curated online database for synaptic proteins.
This database provides adequate definitions of pre- and post-synaptic proteins, proteins
present in sub-domains of the synapse, e.g. the synaptic vesicle and associated proteins,
lipid rafts and postsynaptic density. In addition to data that was and will be gathered from
the experiments conducted within SynSys - A European expertise Network on building the synapse
we have extracted and manually curated all relevant data on these proteins from other sources
and provided an ontology for these. Novel splice forms are being identified that can be matched with proteomics data.
SynSysNet data is stored in a relational MySQL
database. To handle the chemical information within the database,
package is used. MyChem relies for most of its functions on the
toolbox. The website is built using PHP
; web access is enabled via
Apache HTTP Server
. We recommend a recent verion of Mozilla Firefox; Google Chrome,
Java is required to use all features of the site.
A synapse is is a junction between two neurons or between a neuron and a muscle cell, which is the transmission
of electrical or chemical signals, which either excite or inhibit the postsynaptic neuron.
A synapse consists of three elements:
- Presynapse: the part that causes the excitation
- Postsynapse: the part that receives the excitation.
- Synaptic cleft: The space that lies between the two structures.
In general, drugs will inhibit or activate processes in the synapse. This may happen via binding to active sites of synaptic proteins or to protein-protein binding sites. The goal of this project is to develop drugs that have beneficial effects on neurological disorders and neurodegenerative diseases. Therefore, it is important to know about the interactions between the proteins in the central and peripheral nervous system, such as ion channels, receptor proteins, enzymes, neuromodulators and many more. PPI networks can be exploited to find promising therapeutic targets and to judge their druggability.
The website gives you insight of a huge Database of synaptic proteins and compounds
which have synaptic proteins as targets. You can browse either Proteins
at the appropriate buttons. At the Interactions button you can create an Interactive
to visualize the interactions between the compounds and their synaptic targets.
The button Diseases will show you examples of neurological diseases
, to illustrate the
role of SynSysNet in the medication.
If you have any questions, which are not answered in the FAQs, please feel free to contact us
Using this category you will find all synaptic proteins compiled in SynSysNet.
In the Gene/Protein Search
you can search them by name, IDs or function.
The Universal Protein Resource (UniProt) is a comprehensive resource for protein
sequence and annotation data. The UniProt ID is the primary (citable) accession
number of a entry in this database.
The GeneID is an identification number for genes maintained by the NCBI.
PDB stands for Protein Data Bank and provides 3D structures from proteins and
nucleic acids. The PDB ID is the identification number for these structures in the PDB database.
allows you to search proteins by their synaptic localization and function.
For synaptic proteins we built our own browsing section. We defined useful categories
to list proteins grouped by special criteria. It is possible to display all proteins
with a coiled-coil region, according to their specific function, their structure technique
and whether they have a PDB structure or not.
First choose a property you are looking for. Then choose a range and click on "Go" and the
results will be listed. For detailed information of a protein you can select it and click
on the "Find" button. You can select more than one protein by holding down the Shift- or
Ctrl - button on your keyboard.
The results were clustered for an approximately equal number of hits in the specific result set.
This page allows you to search for drugs and compounds with their substance names,
ATC-code or PubChem ID (CID). If the search was successful, you will get to the results,
which give you detailed information about the compound, such as the IDs, the chemical
structure and properties and last but not least the interacting synaptic proteins which are
displayed with their Uniprot ID. To come to a network of your compound and the interacting
targets, click on the light green Button: "Network View" above the table. Please wait about 10 seconds, it takes some time to load the neighbors...
The Anatomical Therapeutic Chemical (ATC) Classification System is used for the classification of drugs,
which is controlled by the WHO Collaborating Centre for Drug Statistics Methodology (WHOCC).
This pharmaceutical coding system divides drugs into different groups according to the organ or
system on which they act and/or their therapeutic and chemical characteristics. Each bottom-level
ATC code stands for a pharmaceutically used substance in a single indication (or use). This means
that one drug can have more than one code. On the other hand, several different brands share the
same code if they have the same active substance and indications.
Below the search-box there is the ATC-Tree of Nervous system
. For example if you want to see detailed
information of all of the Anti-Parkinson Drugs, just click on the underlined code: "N04"
and you will
come to a result page of all 20 anti-parkinson drugs. If you click on the little "plus" you will see the
subdivisions. In brackets are the numbers of the drugs we provide in each category.
The PubChem ID is the identification number of the PubChem database. This database provides information of chemical structures,
substance information and their bioactivity. This page is maintained by the National Center for Biotechnology Information (NCBI).
Before you can create an "Interactive Network" you have to know the IDs of the Protein you are interested in (UniProt ID);
or the Drug you are interested in (PubChem CID). To find out the IDs go to "Search Gene/Proteine
Once you have the IDs you can type in them either in the corresponding field. If you like to show more than one Protein
or Drug in your Interactiv Network you can easily type in all of them just devided by a semicolon ";".
Load the Network by clicking on "Search".
Step 1 shows a network-example. On the right side, references with links to PubMed are displayed (see blue circle). The orange circles correspond to self-interactions. The weight of 8.0 (see green circle) and the thickness of the circle in the graph correspond to the experimental confidence score of synuclein-synuclein interactions. In step 2 additional interactors of alpha-synuclein were loaded. The network can be exported in its current status in XGMML format via the button in the right upper corner (see purple circle). Detailed information on the compounds can be displayed in a new tab (see yellow circle). At the bottom of the details page, pathways for different diseases are listed (see brown circle) and can be viewed as shown here for Parkinson's disease (enriched KEGG pathway). Yellow boxes indicate KEGG targets with drug info in SynSysNet. Mouse-over results in a tool-tip (pop-up) containing detailed drug-target information.
Drag the pressed right
Drag the little circle in the scale "Zoom" left or right.
The circle around a protein (for example D(2) dopamine receptor) means, that the protein interacts with itself.
Just click on the edge between the Compound and the Protein you are interested and you will find the reference on the right upper corner.
The confidence scores rely on the confidence values given in the HIPPIE database. These values are rescaled to the range 0 to 10 to be easily differntiable in the network view using correpsonding edge thickniss. The formula of this scaling is: (hippie-score - 0.5) * 20.
Our contributing partners provide unpublished experimental data about the protein-protein-interactions, which we included to our
database. Only the owners have access to these pages. After publication of the data they will become enabled.
If a network is loaded where no interactions between the nodes exist, the neighbours of the nodes are loaded and displayed
automatically. If for example one ID is entered, the resulting network contains of exactely one node and thus cannot contain
any interactions. In this case, the neighbours of the initial node are loaded and displayed as well.
To put the compound-target relations contained in the database into a cellular context, the information is mapped onto KEGG Pathway maps. The user can choose a pathway from lists of either all human pathways or a subset of 30 synapse related pathways. The targets in the pathway are highlighted in different colors depending on wether they are synapse-specific proteins or not. Additionally, the color of synapse specific targets indicate, whether interacting compounds for the targets are known.
To get additional information about the targeting compounds place the mouse pointer on a synapse-specific target. Compounds are shown with their name and structure; clicking on a compound or target name opens their details page. An additional table summarizing the compound-target relations is shown below the pathway.
In this category you will find exemplary neurological diseases, which illustrate the usage of SynSysNet for the
medication. Everyone can use our Database and our tools to become aware of the interactions between drugs and synaptic
proteins and between themselves.
First we give an outline of the disease then we list examples of drugs out of the WHO guidelines and their interactions
with synaptic proteins. We show these drug examples and their interactions in a network-picture. If you click on it, you
will get to the Interactive Network
You can download most of the data contained in SynSysNet by using the Download menu.
To evaluate the quality of the PPIs the HIPPIE score was applied.
The HIPPIE score tries to reflect the quality of the experimental evidence behind a PPI [Reference Schaefer et al. 2012]. In short, this score is computed using a function that takes into account three components: the number of studies that report a given PPI, the type of technique used for the detection of the PPI, and the number of species in which orthologs of the human PPI partners have been experimentally verified to interact. The evaluation of the techniques was done in collaboration with experts in the production of PPI data, and methods were scored higher the more direct they were (e.g. X-ray crystallography). The function was optimized to score reproducible interactions highest using leave-one-study-out resampling. Scores range between 0 and 1, and values above 0.73 can be considered to indicate high-confidence interactions.
Reference: Schaefer, M.H., J.F. Fontaine, A. Vinayagam, P. Porras, E.E. Wanker and M.A. Andrade-Navarro. 2012. HIPPIE: integrating protein interaction networks with experiment based quality scores. PLoS One. 7, e31826.
Yeast two-hybrid is a molecular technique used to study protein-protein interactions (PPI). Two different plasmids are transfected into a mutant strain yeast which then expresses the encoded proteins. One of the proteins is fused to the binding domain of a transcrition factor whereas the other protein is fused to the activation domain of the transcription factor. In case the two proteins interact, the domains of the transcription factor connect, which starts the transcription of a reporter gene and thereby changes the phenotype of the cell which can be mesured. If the two proteins do not interact, there will be no transcription of the reporter gene.
The term Proteomics stands for a study of all proteins, which were expressed by a genome. This study includes the identification, their structure and functions (especially the physiological and pathophysiological function). Proteome stands thereby for the entire protein complement with all modifications caused by an organism. Specifically the word Proteomics is used for methods of studying proteins like the mass spectrometry and the protein purification.
The dataset is created by manual curation through the SYNSYS Consortium.
For the entire homology modeling modeller 9v10 was used.
To obtain template structures for modeling, a BLAST search (Basic Local Alignment Search) against the PDB was performed
by using the default parameters. Here, all non-redundant PDB sequences were selected with a sequence identity of 95%. A
file containing these structures has been acquired by Modeller.
The generation of the model is based on one PDB structure, which has the highest identity and the lowest E-value. After
alignment of the model with the template structure, the 3D model was calculated and evaluated by the DOPE score.
To describe the quality of the model, a DOPE-score plot from the model and the template structure was created.