Target Information

Target General Information

Target ID

T07931

Target Name

Leukocyte immunoglobulin-like receptor B2 (LILRB2)

Synonyms

LIR-2; Leukocyte immunoglobulin-like receptor 2; CD85 antigen-like family member D; Immunoglobulin-like transcript 4; ILT-4; Monocyte/macrophage immunoglobulin-like receptor 10; MIR-10; DE AltName: CD_antigen=CD85d

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Gene Name

LILRB2

Target Type

Clinical trial target

[1]

Disease

[+] 1 Target-related Diseases

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

Function

Receptor for class I MHC antigens. Recognizes a broad spectrum of HLA-A, HLA-B, HLA-C, HLA-G and HLA-F alleles. Involved in the down-regulation of the immune response and the development of tolerance. Recognizes HLA-G in complex with B2M/beta-2 microglobulin and a nonamer self-peptide (peptide-bound HLA-G-B2M) triggering differentiation of type 1 regulatory T cells and myeloid-derived suppressor cells, both of which actively maintain maternal-fetal tolerance. Competes with CD8A for binding to class I MHC antigens. Inhibits FCGR1A-mediated phosphorylation of cellular proteins and mobilization of intracellular calcium ions.

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UniProt ID

LIRB2_HUMAN

Sequence

MTPIVTVLICLGLSLGPRTHVQTGTIPKPTLWAEPDSVITQGSPVTLSCQGSLEAQEYRL
YREKKSASWITRIRPELVKNGQFHIPSITWEHTGRYGCQYYSRARWSELSDPLVLVMTGA
YPKPTLSAQPSPVVTSGGRVTLQCESQVAFGGFILCKEGEEEHPQCLNSQPHARGSSRAI
FSVGPVSPNRRWSHRCYGYDLNSPYVWSSPSDLLELLVPGVSKKPSLSVQPGPVVAPGES
LTLQCVSDVGYDRFVLYKEGERDLRQLPGRQPQAGLSQANFTLGPVSRSYGGQYRCYGAH
NLSSECSAPSDPLDILITGQIRGTPFISVQPGPTVASGENVTLLCQSWRQFHTFLLTKAG
AADAPLRLRSIHEYPKYQAEFPMSPVTSAHAGTYRCYGSLNSDPYLLSHPSEPLELVVSG
PSMGSSPPPTGPISTPAGPEDQPLTPTGSDPQSGLGRHLGVVIGILVAVVLLLLLLLLLF
LILRHRRQGKHWTSTQRKADFQHPAGAVGPEPTDRGLQWRSSPAADAQEENLYAAVKDTQ
PEDGVEMDTRAAASEAPQDVTYAQLHSLTLRRKATEPPPSQEREPPAEPSIYATLAIH

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3D Structure

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AlphaFold

Drugs and Modes of Action

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Clinical Trial Drug(s)

[+] 2 Clinical Trial Drugs

NGM707

Drug Info

Phase 2

Aggressive cancer

[2]

MK-4830

Drug Info

Phase 1

Solid tumour/cancer

[3]

Mode of Action

[+] 2 Modes of Action

Antagonist

[+] 1 Antagonist drugs

NGM707

Drug Info

[4]

Inhibitor

[+] 1 Inhibitor drugs

MK-4830

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: Benzamidine

Ligand Info

Structure Description

LilrB2 D1D2 domains complexed with benzamidine

PDB:6BCS

Method

X-ray diffraction

Resolution

2.10 Å

Mutation

[5]

PDB Sequence

TIPKPTLWAE

¹¹

PDSVITQGSP

²¹

VTLSCQGSLE

³¹

AQEYRLYREK

⁴¹

KSASWITRIR

⁵¹

PELVKNGQFH

⁶¹

IPSITWEHTG

⁷¹

RYGCQYYSRA

⁸¹

RWSELSDPLV

⁹²

LVMTGAYPKP

¹⁰²

TLSAQPSPVV

¹¹²

TSGGRVTLQC

¹²²

ESQVAFGGFI

¹³²

LCKEGEDEHP

¹⁴²

QCLNSQPHAR

¹⁵²

GSSRAIFSVG

¹⁶²

PVSPNRRWSH

¹⁷²

RCYGYDLNSP

¹⁸²

YVWSSPSDLL

¹⁹²

ELLVPG

Residue

Distance (Å)

ASP13

2.524

SER14

3.352

VAL15

3.454

TYR38

3.990

ARG39

4.360

GLU40

3.190

ILE47

4.093

ARG72

3.363

TYR73

3.392

GLY74

3.402

LEU87

4.022

PRO90

4.243

TYR99

3.273

PRO100

3.477

Residue

Distance (Å)

VAL126

3.538

ALA127

2.888

PHE128

3.460

ILE132

3.511

CYS134

3.706

PRO142

3.699

CYS144

3.421

ASN146

2.750

TYR175

4.876

TYR177

3.554

ASP178

2.615

PRO182

3.203

TYR183

4.315

TRP185

3.752

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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

Protein Name	Pfam ID	Percentage of Identity (%)	E value
MHC class I NK cell receptor 3DL2 (CD158k)	PF00047	36.864 (181/491)	2.12E-72
Killer cell immunoglobulin-like receptor 3DL3 (KIR3DL3)	PF00047	34.244 (163/476)	4.08E-72
Killer cell immunoglobulin-like receptor 3DL1 (KIR3DL1)	PF00047	35.000 (168/480)	1.73E-66
Killer cell immunoglobulin-like receptor 3DS1 (KIR3DS1)	PF00047	38.120 (146/383)	4.65E-66
Putative killer cell immunoglobulin-like receptor-like protein KIR3DX1 (KIR3DX1)	PF00047	36.697 (120/327)	8.88E-56
MHC class I NK cell receptor 2DS2 (CD158j)	PF00047	39.117 (124/317)	2.24E-52
MHC class I NK cell receptor 2DL3 (CD158b2)	PF00047	34.121 (130/381)	4.30E-50
Killer cell immunoglobulin-like receptor 2DS5 (KIR2DS5)	PF00047	37.975 (120/316)	3.20E-49
MHC class I NK cell receptor 2DL2 (CD158b1)	PF00047	34.483 (130/377)	2.62E-48
Killer cell immunoglobulin-like receptor 2DS3 (KIR2DS3)	PF00047	38.486 (122/317)	3.06E-48
MHC class I NK cell receptor 2DS1 (CD158h)	PF00047	37.700 (118/313)	6.21E-48
MHC class I NK cell receptor 2DL1 (CD158A)	PF00047	34.383 (131/381)	1.40E-47
Killer cell immunoglobulin-like receptor 2DS4 (KIR2DS4)	PF00047	38.095 (120/315)	2.10E-47
Killer cell immunoglobulin-like receptor 2DL4 (KIR2DL4)	PF00047	33.424 (123/368)	7.59E-41
Natural cytotoxic triggering receptor 1 (NCR1)		37.555 (86/229)	1.25E-37
Killer cell immunoglobulin-like receptor 2DL5A (KIR2DL5A)	PF00047	31.830 (120/377)	4.70E-36
Killer cell immunoglobulin-like receptor 2DL5B (KIR2DL5B)	PF00047	31.565 (119/377)	2.94E-35
Immunoglobulin alpha Fc receptor (FCAR)		37.559 (80/213)	1.77E-25
V-set and immunoglobulin domain-containing protein 10 (VSIG10)	PF13927 ; PF07686	25.430 (74/291)	5.00E-03
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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
Osteoclast differentiation	hsa04380	Affiliated Target
Class: Organismal Systems => Development and regeneration	Pathway Hierarchy
B cell receptor signaling pathway	hsa04662	Affiliated Target
Class: Organismal Systems => Immune system	Pathway Hierarchy

Degree	7	Degree centrality	7.52E-04	Betweenness centrality	2.07E-04
Closeness centrality	1.77E-01	Radiality	1.29E+01	Clustering coefficient	2.86E-01
Neighborhood connectivity	1.27E+01	Topological coefficient	2.54E-01	Eccentricity	13
Download	Click to Download the Full PPI Network of This Target

References

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REF 1

National Cancer Institute Drug Dictionary (drug name MK4830).

REF 2

ClinicalTrials.gov (NCT04913337) A Phase 1/2 Dose Escalation/Expansion Study of NGM707 as Monotherapy and in Combination With Pembrolizumab in Advanced or Metastatic Solid Tumor Malignancies. U.S.National Institutes of Health.

REF 3

ClinicalTrials.gov (NCT03564691) Study of MK-4830 as Monotherapy and in Combination With Pembrolizumab (MK-3475) in Participants With Advanced Solid Tumors (MK-4830-001). U.S. National Institutes of Health.

REF 4

Clinical pipeline report, company report or official report of NGM Biopharma

REF 5

Inhibiting amyloid-beta cytotoxicity through its interaction with the cell surface receptor LilrB2 by structure-based design. Nat Chem. 2018 Dec;10(12):1213-1221.

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