Target Information

Target General Information

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Target ID

T73582

Target Name

Ubiquitin carboxyl-terminal hydrolase 10 (USP10)

Synonyms

Deubiquitinating enzyme 10; Ubiquitin thioesterase 10; Ubiquitin-specific-processing protease 10

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Gene Name

USP10

Target Type

Preclinical target

[1]

Disease

[+] 1 Target-related Diseases

Postoperative inflammation [ICD-11: 1A00-CA43]

Function

Hydrolase that can remove conjugated ubiquitin from target proteins such as p53/TP53, BECN1, SNX3 and CFTR. Acts as an essential regulator of p53/TP53 stability: in unstressed cells, specifically deubiquitinates p53/TP53 in the cytoplasm, leading to counteract MDM2 action and stabilize p53/TP53. Following DNA damage, translocates to the nucleus and deubiquitinates p53/TP53, leading to regulate the p53/TP53-dependent DNA damage response. Component of a regulatory loop that controls autophagy and p53/TP53 levels: mediates deubiquitination of BECN1, a key regulator of autophagy, leading to stabilize the PIK3C3/VPS34-containing complexes. In turn, PIK3C3/VPS34-containing complexes regulate USP10 stability, suggesting the existence of a regulatory system by which PIK3C3/VPS34-containing complexes regulate p53/TP53 protein levels via USP10 and USP13. Does not deubiquitinate MDM2. Deubiquitinates CFTR in early endosomes, enhancing its endocytic recycling. Involved in a TANK-dependent negative feedback response to attenuate NF-kappaB activation via deubiquitinating IKBKG or TRAF6 in response to interleukin-1-beta (IL1B) stimulation or upon DNA damage. Deubiquitinates TBX21 leading to its stabilization.

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BioChemical Class

Peptidase

UniProt ID

UBP10_HUMAN

EC Number

EC 3.4.19.12

Sequence

MALHSPQYIFGDFSPDEFNQFFVTPRSSVELPPYSGTVLCGTQAVDKLPDGQEYQRIEFG
VDEVIEPSDTLPRTPSYSISSTLNPQAPEFILGCTASKITPDGITKEASYGSIDCQYPGS
ALALDGSSNVEAEVLENDGVSGGLGQRERKKKKKRPPGYYSYLKDGGDDSISTEALVNGH
ANSAVPNSVSAEDAEFMGDMPPSVTPRTCNSPQNSTDSVSDIVPDSPFPGALGSDTRTAG
QPEGGPGADFGQSCFPAEAGRDTLSRTAGAQPCVGTDTTENLGVANGQILESSGEGTATN
GVELHTTESIDLDPTKPESASPPADGTGSASGTLPVSQPKSWASLFHDSKPSSSSPVAYV
ETKYSPPAISPLVSEKQVEVKEGLVPVSEDPVAIKIAELLENVTLIHKPVSLQPRGLINK
GNWCYINATLQALVACPPMYHLMKFIPLYSKVQRPCTSTPMIDSFVRLMNEFTNMPVPPK
PRQALGDKIVRDIRPGAAFEPTYIYRLLTVNKSSLSEKGRQEDAEEYLGFILNGLHEEML
NLKKLLSPSNEKLTISNGPKNHSVNEEEQEEQGEGSEDEWEQVGPRNKTSVTRQADFVQT
PITGIFGGHIRSVVYQQSSKESATLQPFFTLQLDIQSDKIRTVQDALESLVARESVQGYT
TKTKQEVEISRRVTLEKLPPVLVLHLKRFVYEKTGGCQKLIKNIEYPVDLEISKELLSPG
VKNKNFKCHRTYRLFAVVYHHGNSATGGHYTTDVFQIGLNGWLRIDDQTVKVINQYQVVK
PTAERTAYLLYYRRVDLL

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3D Structure

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AlphaFold

HIT2.0 ID

T81MSV

Drugs and Modes of Action

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Preclinical Drug(s)

[+] 1 Preclinical Drugs

Spautin 1

Drug Info

Preclinical

Inflammation

[2]

Mode of Action

[+] 1 Modes of Action

Inhibitor

[+] 1 Inhibitor drugs

Spautin 1

Drug Info

[1]

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). 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 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).

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

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.

Degree	4	Degree centrality	4.30E-04	Betweenness centrality	1.90E-04
Closeness centrality	2.32E-01	Radiality	1.41E+01	Clustering coefficient	0.00E+00
Neighborhood connectivity	7.38E+01	Topological coefficient	2.56E-01	Eccentricity	12
Download	Click to Download the Full PPI Network of This Target

References

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REF 1

Potent USP10/13 antagonist spautin-1 suppresses melanoma growth via ROS-mediated DNA damage and exhibits synergy with cisplatin. J Cell Mol Med. 2020 Apr;24(7):4324-4340.

REF 2

Deubiquitylating enzymes and drug discovery: emerging opportunities. Nat Rev Drug Discov. 2018 Jan;17(1):57-78.

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