Target Information
Target General Information | Top | |||||
---|---|---|---|---|---|---|
Target ID |
T22128
(Former ID: TTDI02320)
|
|||||
Target Name |
Glutamate decarboxylase 2 (GAD2)
|
|||||
Synonyms |
Glutamate decarboxylase 65 kDa isoform; GAD65; GAD2; 65 kDa glutamic acid decarboxylase
Click to Show/Hide
|
|||||
Gene Name |
GAD2
|
|||||
Target Type |
Clinical trial target
|
[1] | ||||
Disease | [+] 1 Target-related Diseases | + | ||||
1 | Diabetes mellitus [ICD-11: 5A10] | |||||
Function |
Catalyzes the production of GABA.
Click to Show/Hide
|
|||||
BioChemical Class |
Carbon-carbon lyase
|
|||||
UniProt ID | ||||||
EC Number |
EC 4.1.1.15
|
|||||
Sequence |
MASPGSGFWSFGSEDGSGDSENPGTARAWCQVAQKFTGGIGNKLCALLYGDAEKPAESGG
SQPPRAAARKAACACDQKPCSCSKVDVNYAFLHATDLLPACDGERPTLAFLQDVMNILLQ YVVKSFDRSTKVIDFHYPNELLQEYNWELADQPQNLEEILMHCQTTLKYAIKTGHPRYFN QLSTGLDMVGLAADWLTSTANTNMFTYEIAPVFVLLEYVTLKKMREIIGWPGGSGDGIFS PGGAISNMYAMMIARFKMFPEVKEKGMAALPRLIAFTSEHSHFSLKKGAAALGIGTDSVI LIKCDERGKMIPSDLERRILEAKQKGFVPFLVSATAGTTVYGAFDPLLAVADICKKYKIW MHVDAAWGGGLLMSRKHKWKLSGVERANSVTWNPHKMMGVPLQCSALLVREEGLMQNCNQ MHASYLFQQDKHYDLSYDTGDKALQCGRHVDVFKLWLMWRAKGTTGFEAHVDKCLELAEY LYNIIKNREGYEMVFDGKPQHTNVCFWYIPPSLRTLEDNEERMSRLSKVAPVIKARMMEY GTTMVSYQPLGDKVNFFRMVISNPAATHQDIDFLIEEIERLGQDL Click to Show/Hide
|
|||||
3D Structure | Click to Show 3D Structure of This Target | PDB | ||||
HIT2.0 ID | T57RCH |
Drugs and Modes of Action | Top | |||||
---|---|---|---|---|---|---|
Clinical Trial Drug(s) | [+] 1 Clinical Trial Drugs | + | ||||
1 | Diamyd | Drug Info | Phase 3 | Type-1 diabetes | [2] | |
Mode of Action | [+] 2 Modes of Action | + | ||||
Immunomodulator | [+] 1 Immunomodulator drugs | + | ||||
1 | Diamyd | Drug Info | [1], [2] | |||
Inhibitor | [+] 1 Inhibitor drugs | + | ||||
1 | s-allylglycine | Drug Info | [3] |
Cell-based Target Expression Variations | Top | |||||
---|---|---|---|---|---|---|
Cell-based Target Expression Variations |
Different Human System Profiles of Target | Top |
---|---|
Human Similarity Proteins
of target is determined by comparing the sequence similarity of all human proteins with the target based on BLAST. The similarity proteins for a target are defined as the proteins with E-value < 0.005 and outside the protein families of the target.
A target that has fewer human similarity proteins outside its family is commonly regarded to possess a greater capacity to avoid undesired interactions and thus increase the possibility of finding successful drugs
(Brief Bioinform, 21: 649-662, 2020).
Human Tissue Distribution
of target is determined from a proteomics study that quantified more than 12,000 genes across 32 normal human tissues. Tissue Specificity (TS) score was used to define the enrichment of target across tissues.
The distribution of targets among different tissues or organs need to be taken into consideration when assessing the target druggability, as it is generally accepted that the wider the target distribution, the greater the concern over potential adverse effects
(Nat Rev Drug Discov, 20: 64-81, 2021).
Human Pathway Affiliation
of target is determined by the life-essential pathways provided on KEGG database. The target-affiliated pathways were defined based on the following two criteria (a) the pathways of the studied target should be life-essential for both healthy individuals and patients, and (b) the studied target should occupy an upstream position in the pathways and therefore had the ability to regulate biological function.
Targets involved in a fewer pathways have greater likelihood to be successfully developed, while those associated with more human pathways increase the chance of undesirable interferences with other human processes
(Pharmacol Rev, 58: 259-279, 2006).
Biological Network Descriptors
of target is determined based on a human protein-protein interactions (PPI) network consisting of 9,309 proteins and 52,713 PPIs, which were with a high confidence score of ≥ 0.95 collected from STRING database.
The network properties of targets based on protein-protein interactions (PPIs) have been widely adopted for the assessment of target’s druggability. Proteins with high node degree tend to have a high impact on network function through multiple interactions, while proteins with high betweenness centrality are regarded to be central for communication in interaction networks and regulate the flow of signaling information
(Front Pharmacol, 9, 1245, 2018;
Curr Opin Struct Biol. 44:134-142, 2017).
Human Similarity Proteins
Human Tissue Distribution
Human Pathway Affiliation
Biological Network Descriptors
|
There is no similarity protein (E value < 0.005) for this target
|
Note:
If a protein has TS (tissue specficity) scores at least in one tissue >= 2.5, this protein is called tissue-enriched (including tissue-enriched-but-not-specific and tissue-specific). In the plots, the vertical lines are at thresholds 2.5 and 4.
|
KEGG Pathway | Pathway ID | Affiliated Target | Pathway Map |
---|---|---|---|
Alanine, aspartate and glutamate metabolism | hsa00250 | Affiliated Target |
|
Class: Metabolism => Amino acid metabolism | Pathway Hierarchy | ||
beta-Alanine metabolism | hsa00410 | Affiliated Target |
|
Class: Metabolism => Metabolism of other amino acids | Pathway Hierarchy | ||
Taurine and hypotaurine metabolism | hsa00430 | Affiliated Target |
|
Class: Metabolism => Metabolism of other amino acids | Pathway Hierarchy | ||
Butanoate metabolism | hsa00650 | Affiliated Target |
|
Class: Metabolism => Carbohydrate metabolism | Pathway Hierarchy | ||
GABAergic synapse | hsa04727 | Affiliated Target |
|
Class: Organismal Systems => Nervous system | Pathway Hierarchy |
Degree | 2 | Degree centrality | 2.15E-04 | Betweenness centrality | 2.17E-05 |
---|---|---|---|---|---|
Closeness centrality | 1.51E-01 | Radiality | 1.20E+01 | Clustering coefficient | 0.00E+00 |
Neighborhood connectivity | 7.50E+00 | Topological coefficient | 5.42E-01 | Eccentricity | 13 |
Download | Click to Download the Full PPI Network of This Target | ||||
Target Affiliated Biological Pathways | Top | |||||
---|---|---|---|---|---|---|
BioCyc | [+] 2 BioCyc Pathways | + | ||||
1 | Glutamate dependent acid resistance | |||||
2 | GABA shunt | |||||
KEGG Pathway | [+] 7 KEGG Pathways | + | ||||
1 | Alanine, aspartate and glutamate metabolism | |||||
2 | beta-Alanine metabolism | |||||
3 | Taurine and hypotaurine metabolism | |||||
4 | Butanoate metabolism | |||||
5 | Metabolic pathways | |||||
6 | GABAergic synapse | |||||
7 | Type I diabetes mellitus | |||||
NetPath Pathway | [+] 1 NetPath Pathways | + | ||||
1 | IL2 Signaling Pathway | |||||
Panther Pathway | [+] 1 Panther Pathways | + | ||||
1 | Gamma-aminobutyric acid synthesis | |||||
WikiPathways | [+] 3 WikiPathways | + | ||||
1 | Biogenic Amine Synthesis | |||||
2 | GABA synthesis, release, reuptake and degradation | |||||
3 | Alanine and aspartate metabolism |
References | Top | |||||
---|---|---|---|---|---|---|
REF 1 | Clinical pipeline report, company report or official report of Diamyd Medical. | |||||
REF 2 | ClinicalTrials.gov (NCT00751842) A Phase III Study to Investigate the Impact of Diamyd in Patients Newly Diagnosed With Type 1 Diabetes (USA)- DIAPREVENT. U.S. National Institutes of Health. | |||||
REF 3 | URL: http://www.guidetopharmacology.org Nucleic Acids Res. 2015 Oct 12. pii: gkv1037. The IUPHAR/BPS Guide to PHARMACOLOGY in 2016: towards curated quantitative interactions between 1300 protein targets and 6000 ligands. (Target id: 1273). |
If You Find Any Error in Data or Bug in Web Service, Please Kindly Report It to Dr. Zhou and Dr. Zhang.