Target Information

Target General Information

Target ID

T85544 (Former ID: TTDNC00529)

Target Name

Target of rapamycin complex 2 MAPKAP1 (MTORC2)

Synonyms

mSIN1; Target of rapamycin complex 2 subunit MAPKAP1; TORC2 subunit MAPKAP1; Stress-activated map kinase-interacting protein 1; SIN1; SAPK-interacting protein 1; Mitogen-activated protein kinase 2-associated protein 1; MIP1

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Gene Name

MAPKAP1

Target Type

Clinical trial target

[1]

Disease

[+] 4 Target-related Diseases

Solid tumour/cancer [ICD-11: 2A00-2F9Z]

Breast cancer [ICD-11: 2C60-2C6Y]

Pleural mesothelioma [ICD-11: 2C26]

Retinopathy [ICD-11: 9B71]

Function

mTORC2 is activated by growth factors, but, in contrast to mTORC1, seems to be nutrient-insensitive. mTORC2 seems to function upstream of Rho GTPases to regulate the actin cytoskeleton, probably by activating one or more Rho-type guanine nucleotide exchange factors. mTORC2 promotes the serum-induced formation of stress-fibers or F-actin. mTORC2 plays a critical role in AKT1 'Ser-473' phosphorylation, which may facilitate the phosphorylation of the activation loop of AKT1 on 'Thr-308' by PDK1 which is a prerequisite for full activation. mTORC2 regulates the phosphorylation of SGK1 at 'Ser-422'. mTORC2 also modulates the phosphorylation of PRKCA on 'Ser-657'. Within mTORC2, MAPKAP1 is required for complex formation and mTORC2 kinase activity. MAPKAP1 inhibits MAP3K2 by preventing its dimerization and autophosphorylation. Inhibits HRAS and KRAS signaling. Enhances osmotic stress-induced phosphorylation of ATF2 and ATF2-mediated transcription. Involved in ciliogenesis, regulates cilia length through its interaction with CCDC28B independently of mTORC2 complex. Subunit of mTORC2, which regulates cell growth and survival in response to hormonal signals.

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UniProt ID

SIN1_HUMAN

Sequence

MAFLDNPTIILAHIRQSHVTSDDTGMCEMVLIDHDVDLEKIHPPSMPGDSGSEIQGSNGE
TQGYVYAQSVDITSSWDFGIRRRSNTAQRLERLRKERQNQIKCKNIQWKERNSKQSAQEL
KSLFEKKSLKEKPPISGKQSILSVRLEQCPLQLNNPFNEYSKFDGKGHVGTTATKKIDVY
LPLHSSQDRLLPMTVVTMASARVQDLIGLICWQYTSEGREPKLNDNVSAYCLHIAEDDGE
VDTDFPPLDSNEPIHKFGFSTLALVEKYSSPGLTSKESLFVRINAAHGFSLIQVDNTKVT
MKEILLKAVKRRKGSQKVSGPQYRLEKQSEPNVAVDLDSTLESQSAWEFCLVRENSSRAD
GVFEEDSQIDIATVQDMLSSHHYKSFKVSMIHRLRFTTDVQLGISGDKVEIDPVTNQKAS
TKFWIKQKPISIDSDLLCACDLAEEKSPSHAIFKLTYLSNHDYKHLYFESDAATVNEIVL
KVNYILESRASTARADYFAQKQRKLNRRTSFSFQKEKKSGQQ

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3D Structure

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PDB

HIT2.0 ID

T77BI5

Drugs and Modes of Action

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Clinical Trial Drug(s)

[+] 1 Clinical Trial Drugs

ME-344

Drug Info

Phase 1/2

Breast cancer

[1]

Mode of Action

[+] 1 Modes of Action

Inhibitor

[+] 1 Inhibitor drugs

ME-344

Drug Info

[1]

Cell-based Target Expression Variations

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Cell-based Target Expression Variations

Cell-based Target Expression Variations Info

Drug Binding Sites of Target

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Ligand Name: Selenomethionine

Ligand Info

Structure Description

Crystal Structure of KRAS4b-Q61R (GMPPNP-bound) in complex with the RAS-binding domain (RBD) of SIN1

PDB:7LC2

Method

X-ray diffraction

Resolution

2.70 Å

Mutation

Yes

[7]

PDB Sequence

SLFVRINAAH

²⁸⁷

GFSLIQVDNT

²⁹⁷

KVTMKEILLK

³⁰⁷

AVKRRKGSQQ

³²²

YRLEKQSEPN

³³²

VAVDLDSTLE

³⁴²

SQSAWEFCLV

³⁵²

RENSSR

Residue

Distance (Å)

SER357

3.322

Residue

Distance (Å)

ARG358

3.264

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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

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
mTOR signaling pathway	hsa04150	Affiliated Target
Class: Environmental Information Processing => Signal transduction	Pathway Hierarchy

Degree	11	Degree centrality	1.18E-03	Betweenness centrality	9.41E-06
Closeness centrality	2.33E-01	Radiality	1.41E+01	Clustering coefficient	7.82E-01
Neighborhood connectivity	4.16E+01	Topological coefficient	1.66E-01	Eccentricity	12
Download	Click to Download the Full PPI Network of This Target

Drug Property Profile of Target

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(1) Molecular Weight (mw) based Drug Clustering

(2) Octanol/Water Partition Coefficient (xlogp) based Drug Clustering

(3) Hydrogen Bond Donor Count (hbonddonor) based Drug Clustering

(4) Hydrogen Bond Acceptor Count (hbondacc) based Drug Clustering

(5) Rotatable Bond Count (rotbonds) based Drug Clustering

(6) Topological Polar Surface Area (polararea) based Drug Clustering

"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

Co-Targets

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Co-Targets

Co-Targets Info

Target Regulators

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Target-interacting Proteins

Target-interacting Proteins Info

References

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REF 1

Clinical pipeline report, company report or official report of the Pharmaceutical Research and Manufacturers of America (PhRMA)

REF 2

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. (Ligand id: 7699).

REF 3

ClinicalTrials.gov (NCT01793636) A Study Comparing AZD2014 vs Everolimus in Patients With Metastatic Renal Cancer. U.S. National Institutes of Health.

REF 4

REF 5

VS-5584, a novel and highly selective PI3K/mTOR kinase inhibitor for the treatment of cancer. Mol Cancer Ther. 2013 Feb;12(2):151-61.

REF 6

ClinicalTrials.gov (NCT01033721) Phase I Study of Palomid 529 a Dual TORC1/2 Inhibitor of the PI3K/Akt/mTOR Pathway for Advanced Neovascular Age-Related Macular Degeneration (P52901). U.S. National Institutes of Health.

REF 7

RAS interaction with Sin1 is dispensable for mTORC2 assembly and activity. Proc Natl Acad Sci U S A. 2021 Aug 17;118(33):e2103261118.

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