Target Information

Target General Information

Target ID

T15572 (Former ID: TTDR00799)

Target Name

Adiponectin (ADIPOQ)

Synonyms

Gelatin-binding protein; GBP28; ApM-1; Adipose most abundant gene transcript 1 protein; Adipose most abundant gene transcript 1; Adipocyte, C1q and collagen domain-containing protein; Adipocyte, C1q and collagen domain containingprotein; Adipocyte complement-related 30 kDa protein; APM1; ACRP30; ACDC; 30 kDa adipocyte complement-related protein

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Gene Name

ADIPOQ

Target Type

Literature-reported target

[1]

Function

Stimulates AMPK phosphorylation and activation in the liver and the skeletal muscle, enhancing glucose utilization and fatty-acid combustion. Antagonizes TNF-alpha by negatively regulating its expression in various tissues such as liver and macrophages, and also by counteracting its effects. Inhibits endothelial NF-kappa-B signaling through a cAMP-dependent pathway. May play a role in cell growth, angiogenesis and tissue remodeling by binding and sequestering various growth factors with distinct binding affinities, depending on the type of complex, LMW, MMW or HMW. Important adipokine involved in the control of fat metabolism and insulin sensitivity, with direct anti-diabetic, anti-atherogenic and anti-inflammatory activities.

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BioChemical Class

Adiponectin protein

UniProt ID

ADIPO_HUMAN

Sequence

MLLLGAVLLLLALPGHDQETTTQGPGVLLPLPKGACTGWMAGIPGHPGHNGAPGRDGRDG
TPGEKGEKGDPGLIGPKGDIGETGVPGAEGPRGFPGIQGRKGEPGEGAYVYRSAFSVGLE
TYVTIPNMPIRFTKIFYNQQNHYDGSTGKFHCNIPGLYYFAYHITVYMKDVKVSLFKKDK
AMLFTYDQYQENNVDQASGSVLLHLEVGDQVWLQVYGEGERNGLYADNDNDSTFTGFLLY
HDTN

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3D Structure

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AlphaFold

ADReCS ID

BADD_A02367

HIT2.0 ID

T95IYZ

Cell-based Target Expression Variations

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Cell-based Target Expression Variations

Cell-based Target Expression Variations Info

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

Different Human System Profiles of Target

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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 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

Protein Name	Pfam ID	Percentage of Identity (%)	E value
Acetylcholinesterase collagenic tail peptide (COLQ)		44.118 (30/68)	5.52E-06
Pulmonary surfactant-associated protein A2 (SFTPA2)	PF00059	36.036 (40/111)	6.21E-06
Pulmonary surfactant-associated protein A1 (SFTPA1)	PF00059	41.463 (34/82)	8.70E-05


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
PPAR signaling pathway	hsa03320	Affiliated Target
Class: Organismal Systems => Endocrine system	Pathway Hierarchy
AMPK signaling pathway	hsa04152	Affiliated Target
Class: Environmental Information Processing => Signal transduction	Pathway Hierarchy
Longevity regulating pathway	hsa04211	Affiliated Target
Class: Organismal Systems => Aging	Pathway Hierarchy
Adipocytokine signaling pathway	hsa04920	Affiliated Target
Class: Organismal Systems => Endocrine system	Pathway Hierarchy

Degree	9	Degree centrality	9.67E-04	Betweenness centrality	3.22E-04
Closeness centrality	2.18E-01	Radiality	1.38E+01	Clustering coefficient	1.67E-01
Neighborhood connectivity	2.28E+01	Topological coefficient	1.53E-01	Eccentricity	11
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Target Regulators

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Target-interacting Proteins

Target-interacting Proteins Info

References

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REF 1

Biomarkers of diabetic nephropathy, the present and the future. Curr Diabetes Rev. 2012 Sep;8(5):317-28.

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