Target Information
| Target General Information | Top | |||||
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| Target ID |
T47594
(Former ID: TTDI03011)
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| Target Name |
Activin receptor type IB (ACVR1B)
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| Synonyms |
Serine/threonine-protein kinase receptor R2; SKR2; Activin receptor-like kinase 4; Activin receptor type-1B; ALK4; ALK-4; ACVRLK4; ACTR-IB
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| Gene Name |
ACVR1B
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| Target Type |
Patented-recorded target
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| Function |
Transduces the activin signal from the cell surface to the cytoplasm and is thus regulating a many physiological and pathological processes including neuronal differentiation and neuronal survival, hair follicle development and cycling, FSH production by the pituitary gland, wound healing, extracellular matrix production, immunosuppression and carcinogenesis. Activin is also thought to have a paracrine or autocrine role in follicular development in the ovary. Within the receptor complex, type-2 receptors (ACVR2A and/or ACVR2B) act as a primary activin receptors whereas the type-1 receptors like ACVR1B act as downstream transducers of activin signals. Activin binds to type-2 receptor at the plasma membrane and activates its serine-threonine kinase. The activated receptor type-2 then phosphorylates and activates the type-1 receptor such as ACVR1B. Once activated, the type-1 receptor binds and phosphorylates the SMAD proteins SMAD2 and SMAD3, on serine residues of the C-terminal tail. Soon after their association with the activin receptor and subsequent phosphorylation, SMAD2 and SMAD3 are released into the cytoplasm where they interact with the common partner SMAD4. This SMAD complex translocates into the nucleus where it mediates activin-induced transcription. Inhibitory SMAD7, which is recruited to ACVR1B through FKBP1A, can prevent the association of SMAD2 and SMAD3 with the activin receptor complex, thereby blocking the activin signal. Activin signal transduction is also antagonized by the binding to the receptor of inhibin-B via the IGSF1 inhibin coreceptor. ACVR1B also phosphorylates TDP2. Transmembrane serine/threonine kinase activin type-1 receptor forming an activin receptor complex with activin receptor type-2 (ACVR2A or ACVR2B).
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| BioChemical Class |
Kinase
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| UniProt ID | ||||||
| EC Number |
EC 2.7.11.30
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| Sequence |
MAESAGASSFFPLVVLLLAGSGGSGPRGVQALLCACTSCLQANYTCETDGACMVSIFNLD
GMEHHVRTCIPKVELVPAGKPFYCLSSEDLRNTHCCYTDYCNRIDLRVPSGHLKEPEHPS MWGPVELVGIIAGPVFLLFLIIIIVFLVINYHQRVYHNRQRLDMEDPSCEMCLSKDKTLQ DLVYDLSTSGSGSGLPLFVQRTVARTIVLQEIIGKGRFGEVWRGRWRGGDVAVKIFSSRE ERSWFREAEIYQTVMLRHENILGFIAADNKDNGTWTQLWLVSDYHEHGSLFDYLNRYTVT IEGMIKLALSAASGLAHLHMEIVGTQGKPGIAHRDLKSKNILVKKNGMCAIADLGLAVRH DAVTDTIDIAPNQRVGTKRYMAPEVLDETINMKHFDSFKCADIYALGLVYWEIARRCNSG GVHEEYQLPYYDLVPSDPSIEEMRKVVCDQKLRPNIPNWWQSYEALRVMGKMMRECWYAN GAARLTALRIKKTLSQLSVQEDVKI Click to Show/Hide
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| 3D Structure | Click to Show 3D Structure of This Target | AlphaFold | ||||
| Cell-based Target Expression Variations | Top | |||||
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| Cell-based Target Expression Variations | ||||||
| Different Human System Profiles of Target | Top |
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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 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 Pathway Affiliation
Biological Network Descriptors
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| KEGG Pathway | Pathway ID | Affiliated Target | Pathway Map |
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| Cytokine-cytokine receptor interaction | hsa04060 | Affiliated Target |
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| Class: Environmental Information Processing => Signaling molecules and interaction | Pathway Hierarchy | ||
| TGF-beta signaling pathway | hsa04350 | Affiliated Target |
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| Class: Environmental Information Processing => Signal transduction | Pathway Hierarchy | ||
| Signaling pathways regulating pluripotency of stem cells | hsa04550 | Affiliated Target |
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| Class: Cellular Processes => Cellular community - eukaryotes | Pathway Hierarchy | ||
| Degree | 4 | Degree centrality | 4.30E-04 | Betweenness centrality | 1.21E-06 |
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| Closeness centrality | 2.12E-01 | Radiality | 1.37E+01 | Clustering coefficient | 6.67E-01 |
| Neighborhood connectivity | 4.13E+01 | Topological coefficient | 3.72E-01 | Eccentricity | 12 |
| Download | Click to Download the Full PPI Network of This Target | ||||
| Chemical Structure based Activity Landscape of Target | Top |
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| Drug Property Profile of Target | Top | |
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| (1) Molecular Weight (mw) based Drug Clustering | (2) Octanol/Water Partition Coefficient (xlogp) based Drug Clustering | |
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| (3) Hydrogen Bond Donor Count (hbonddonor) based Drug Clustering | (4) Hydrogen Bond Acceptor Count (hbondacc) based Drug Clustering | |
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| (5) Rotatable Bond Count (rotbonds) based Drug Clustering | (6) Topological Polar Surface Area (polararea) based Drug Clustering | |
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| "RO5" indicates the cutoff set by lipinski's rule of five; "D123AB" colored in GREEN denotes the no violation of any cutoff in lipinski's rule of five; "D123AB" colored in PURPLE refers to the violation of only one cutoff in lipinski's rule of five; "D123AB" colored in BLACK represents the violation of more than one cutoffs in lipinski's rule of five | ||
| Target Regulators | Top | |||||
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| Target-regulating microRNAs | ||||||
| Target-interacting Proteins | ||||||
| References | Top | |||||
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| REF 1 | Synthesis and structure-activity relationships of a novel and selective bone morphogenetic protein receptor (BMP) inhibitor derived from the pyrazolo[1.5-a]pyrimidine scaffold of dorsomorphin: the discovery of ML347 as an ALK2 versus ALK3 selective MLPCN probe. Bioorg Med Chem Lett. 2013 Jun 1;23(11):3248-52. | |||||
| REF 2 | BMP inhibitors and methods of use thereof. US9682983. | |||||
| REF 3 | Compounds and methods of use. US10030004. | |||||
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