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Hypothesis

New Insight into Drugs to Alleviate Atopic March via Network Pharmacology-Based Analysis

by
Ki-Kwang Oh
,
Md. Adnan
and
Dong-Ha Cho
*
Department of Bio-Health Convergence, College of Biomedical Science, Kangwon National University, Chuncheon 24341, Korea
*
Author to whom correspondence should be addressed.
Curr. Issues Mol. Biol. 2022, 44(5), 2257-2274; https://doi.org/10.3390/cimb44050153
Submission received: 25 April 2022 / Revised: 14 May 2022 / Accepted: 16 May 2022 / Published: 18 May 2022
(This article belongs to the Collection Application of Natural and Pseudo Natural Products in Drug Discovery and Development)
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Abstract

:
In the present study, a subject of atopic dermatitis (AD) is exposed progressively to allergic rhinitis (AR) and asthma (AS), which is defined as atopic march (AM). However, both the targets and compounds against AM are still largely unknown. Hence, we investigated the overlapping targets related directly to the occurrence and development of AD, AR, and AS through public databases (DisGeNET, and OMIM). The final overlapping targets were considered as key targets of AM, which were visualized by a Venn diagram. The protein–protein interaction (PPI) network was constructed using R package software. We retrieved the association between targets and ligands via scientific journals, and the ligands were filtered by physicochemical properties. Lastly, we performed a molecular docking test (MDT) to identify the significant ligand on each target. A total of 229 overlapping targets were considered as AM causal elements, and 210 out of them were interconnected with each other. We adopted 65 targets representing the top 30% highest in degree centrality among 210 targets. Then, we obtained 20 targets representing the top 30% greatest in betweenness centrality among 65 targets. The network analysis unveiled key targets against AM, and the MDT confirmed the affinity between significant compounds and targets. In this study, we described the significance of the eight uppermost targets (CCL2, CTLA4, CXCL8, ICAM1, IL10, IL17A, IL1B, and IL2) and eight ligands (Bindarit, CTLA-4 inhibitor, Danirixin, A-205804, AX-24 HCl, Y-320, T-5224, and Apilimod) against AM, providing a scientific basis for further experiments.

1. Introduction

Atopic lesions, including atopic dermatitis (AD), allergic rhinitis (AR), and allergic asthma (AA), have seen elevated incidence rates for some decades, impacting around 30% of the world population [1]. The atopic march (AM) occurs in a time-based sequence: from AD in infancy to AR and AA in childhood, which is developed from the skin to the respiratory tract [2]. Firstly, AD is a chronic autoimmune skin disorder caused by increases in serum Immunoglobulin E (IgE) levels and hypersensitivity to a diverse array of allergens, including food and bacteria [3,4]. AD is an initial step in the development of AM, and IgE is a key biomarker of allergic inflammation [5].
The first line drug of AD is topical corticosteroids such as Pimecrolimus and Tacrolimus, the mechanism of which is to block the T cell’s activation to dampen immune responses [6,7].
Secondly, AR is an inflammatory disease of the nasal mucous membrane mediated by IgE [8]. The first line drug of AR is H1 antihistamine drugs such as cetirizine, levocetirizine, desloratadine, loratadine, and fexofenadine [9,10]. Thirdly, AA is a final step of AM, and the recommended drugs to alleviate AM are inhaled corticosteroids which suppress airway inflammation [11,12]. Collectively, a key symptom of AM is the inflammatory response, and the optimal strategy to relieve AM is to elucidate overlapping targets from AD, AR, and AA.
The analysis of protein–protein interaction (PPI) networks for drug repositioning is a significant approach, providing clues about causal relationships between targets that carry out certain functions [13]. Therefore, this research has focused on the topological properties of PPI networks to uncover the relatively important nodes among the overlapping targets from AD, AR, and AA.

2. Hypothesis

The targets on AD, AR, and AA were retrieved through DisGeNET, OMIM, and the literature, suggesting that the overlapping targets from the three diseases were considered as AM-related targets. We hypothesize that targets with the 30% greatest betweenness centrality (BC) from targets with the top 30% highest degree centrality (DC) values [14] in the overlapping targets are core targets to alleviate AM, thereby identifying the most stably binding ligands on them via molecular docking test (MDT).

3. Methods

The targets associated with AD, AR, and AA were acquired through DisGeNET (https://www.disgenet.org/) (accessed on 2 December 2021), and OMIM (https://www.omim.org/) (accessed on 3 December 2021), which are therapeutic relevant components. The overlapping targets from the three diseases (AD, AR, and AA) were considered as core targets of AM. The core targets were analyzed using the STRING database (https://string-db.org/) (accessed on 4 December 2021). We performed the topological analysis of AM on a PPI network. The workflow is as below.
Step 1:
Identifying of targets (AD, AR, and AA) from DisGeNET and OMIM.
Step 2:
Identifying of overlapping targets (AD, AR, and AA) via a Venn diagram.
Step 3:
The overlapping targets were screened on the top 30% by degree centrality (DC), and the PPI network was constructed on RPackage software.
Step 4:
After constructing the PPI network of the top 30% DC, the core targets were identified, and the PPI network of the top 30% by betweenness centrality (BC) in the top 30% DC were constructed on RPackage software.
Step 5:
Suggesting that targets with the highest DC in the top 30% BC targets were considered as the promising targets against AM.
Step 6:
The retrieval of ligands from Selleckchem.com (https://www.selleckchem.com/) (accessed on 24 April 2022).
Step 7:
The first screening of ligands based on TPSA <140Å2 or Lipinski’s rule.
Step 8:
The second screening of Step 7 ligands according to the docking score (<−6.0 kcal/mol) or the lowest binding energy (the highest negative value) of each target.
Step 9:
The identified ligands were converted to .sdf format from PubChem into .pdb format utilizing Pymol, and the ligands were converted into .pdbqt format via AutoDock. Then, the PDB ID of targets were obtained via RCSB PDB (https://www.rcsb.org/) (accessed on 7 December 2021). The AutoDockTools-1.5.6 software was utilized to evaluate the affinity of the promising targets and ligands. The ligands were docked with targets using autodock4 by arranging 4 energy ranges and 8 exhaustiveness as default to identify 10 different poses of ligands [15].
The docking site was set in a cubic box on the center: CCL2 (x = 61.620, y = 0.356, z = 0.582), CTLA4 (x = 22.471, y = 17.954, z = −35.403), CXCL8 (x = 0.977, y = −7.328, z = 2.055), ICAM1 (x = −5.082, y = −12.607, z = 35.679), IL10 (x = 17.574, y = 49.569, z = 27.605), IL17A (x = 77.528, y = −4.484, z = -51.759), IL1B (x = 19.495, y = 2.991, z = 73.516), IL2 (x = 6.750, y = 25.891, z = 14.189)
The grid box size was set to 40 Å × 40 Å × 40 Å. The LigPlot+ v.2.2 (https://www.ebi.ac.uk/thornton-srv/software/LigPlus/) (accessed on 8 December 2021) was used to parameterize 2D binding interactions [16]. The adopted ligands on key targets were regarded as key compounds against AM. The methodology workflow is represented in Figure 1.

4. Results

A total of 999 (AD), 517 (AR), and 2374 targets (AA) were retrieved by DisGeNET (https://www.disgenet.org/) (accessed on 4 December 2021) and OMIM (accessed on 4 December 2021) (https://www.omim.org/), of which there were 229 overlapping targets (AM) (Figure 2). The target information is profiled in Supplementary Table S1. The 229 targets were considered as core targets against AM, which was comprised of 210 nodes and 4346 edges (Figure 3A) (Supplementary Table S2), and the subnetwork was obtained via selecting the top 30% by degree centrality (DC), containing 65 nodes and 1325 edges (Figure 3B) (Supplementary Table S3). After filtering the top 30% of the subnetwork by betweenness centrality (BC), the key network was assembled, including 20 nodes and 185 edges (Figure 3C) (Supplementary Table S4).
Based on the top 30% of the subnetwork by betweenness centrality (BC), 14 out of 20 targets had the same degree centrality (19) (Table 1), which were determined as crucial targets in the PPI network. Then, 8 out of 14 targets were reported with ligands, and 6 other targets had no reported ligands. A total of 8 targets were associated with the 34 ligands (Table 2). A total of 34 ligands related directly to the uppermost 8 targets were retrieved via chemical supplies websites and literature (Figure 4A). The 34 ligands were filtered into 29 ligands with TPSA < 140 Å2 or Lipinski’s rule (Figure 4B). MDT was performed on the 29 filtered ligands to select the most stable binding ligands for each target, as a consequence revealing that 8 ligands were critical compounds (Figure 4C).
The MDT of eight ligands demonstrated that (1) Bindarit (PubChem ID: 71354) had the greatest affinity of -7.2 kcal/mol on CCL2 (PDB ID: 1DON) (Figure 5A), (2) CTLA4 inhibitor (PubChem ID: 101136468) had the highest affinity of −7.9 kcal/mol on CTLA4 (PDB ID: 5XJ3) (Figure 5B), (3) Danirixin (PubChem ID: 24780598) had the greatest affinity of −7.0 kcal/mol on CXCL8 (PDB ID: 2IL8) (Figure 5C), (4) A-205804 (PubChem ID: 9839311) had the highest affinity of -8.0 kcal/mol on ICAM1 (PDB ID: 5MZA) (Figure 5D). Most notably, Anemoside B4 (PubChem ID: 71307558) had the greatest affinity of −13.5 kcal/mol on IL10 (PDB ID: 1LK3), but it was violated on three (Hydrogen bond acceptor <10; Hydrogen bond donor ≤ 5; and TPSA <140 Å2) by Lipinski’s rule. Hence, (5) AX-024 HCl (PubChem ID: 129909862), with −7.5 kcal/mol on IL10 (PDB ID: 1LK3), was ligand with the most potential on IL10 (PDB ID: 1LK3) (Figure 5E). (6) Y-320 (PubChem ID: 22227931) had the greatest affinity of −7.7 kcal/mol on IL17A (PDB ID: 2VXS) (Figure 5F). (7) T-5224 (PubChem ID: 23626877) had the highest affinity of −8.0 kcal/mol on IL1B (PDB ID: 1HIB) (Figure 5G). (8) Apilimod (PubChem ID: 10173277) had the greatest affinity of −6.2 kcal/mol on IL2 (PDB ID: 1M47) (Figure 5H). The MDT confirmed the stable binding between eight core ligands as well as eight targets (Table 3).

5. Discussion

As a matter of fact, targets with a high betweenness centrality (BC) (the measurement of the shortest route between other targets) are the most significant element to be associated with vital multiple therapeutic utilization and then are a noteworthy value to be repositioned [13]. There is an observational result that the eight core targets (CCL2, CTLA4, CXCL8, ICAM1, IL10, IL17A, IL1B, and IL2) overlapped among AD, AR, and AA were the therapeutic targets with the most potential against AM. In addition, the compounds–targets relationship suggested that the therapeutic effect of AM was directly associated with eight ligands, including three benzenoids (Danirixin, Y-320, T-5224), two heterocyclic compounds (Bindarit, CTLA-4 inhibitor), one organosulfur (A-205804), one organic nitrogen compound (Apilimod), and one phenylpropanoid and polyketide (AX-024 HCl). It was reported that benzenoids isolated from Antrodia cinnamomea have a potent anti-inflammatory effects on AD, AR, and AA [17,18]. This implies that the benzenoids classification might be a good ligand to alleviate AM. Investigations into the treatment of heterocyclic compounds have revealed that the compounds are useful for the alleviation of inflammatory and allergic disorders including AD, AR, and AA [19]. Study indicated that organosulfur compounds are inflammatory mediators to dampen interleukin (IL)1B and IL17, particularly, AD, AR, and AA [20,21,22,23]. It was reported that nitrogen-containing compounds such as Apilimod are potent selective inhibitors for AD, AR, and AA [24,25,26]. A report indicated that plant-derived phenylpropanoids (PPPs) are anti-inflammatory agents to modulate underlying inflammatory reactions in vitro or in vivo [27].
CCL2 (C-C motif chemokine ligand 2) is the initial chemokine stimulated by itching and pain symptoms on keratinocytes, fibroblasts, and endothelial cells’ infiltered monocytes and lymphocytes [28]. The overexpression of CCL2 is a trigger to respond to allergies and asthma [29]. It could be thus speculated that the inhibition of CCL2 might be a good strategy for the treatment of AM. Study shows that miR-155 as a CTLA-4 inhibitor has inhibitory effects on AD that could potentially disturb the development of AM [30]. Thus, a CTLA-T inhibitor might be a promising target against AM. There is an observational report suggesting that AD, AR, and AA increased the expression level of CXCL8 in responding to innate immunity [31,32,33]. A report suggested that the intercellular adhesion molecule-1 (ICAM1) is upregulated in AD, AR, and AA, indicating that the ICAM1 is associated directly with AM [34,35,36]. IL10 is multiple-functional cytokine during allergic symptoms and a significant element in the progression of severe allergic diseases, including AD, AR, and AA [37,38,39]. IL17A plays a vital role in the occurrence and development of allergic diseases, particularly, AD, AR, and AA [40,41]. IL1B is a proinflammatory agent, mediated by a rise in the expression of ICAM-1, and thereby is related directly to the inflammatory responses such as AD, AR, and AA [42,43,44]. IL2 increased in severe AD, which secretes pruritus in almost all patients [45]. In addition, after treatment of the intranasal corticosteroid, the IL-2 were considerably diminished during 24 h, contained by significant enhancement in the upper airway function [43].
Collectively, this study indicates the potential ligands and critical targets to alleviate AM via orchestrating targets of AD, AR, and AA, providing theoretical clues for further experimental evaluation.

6. Conclusions

In summary, eight promising compounds (Bindarit, CTLA-4 inhibitor, Danirixin, A-205804, AX-24 HCl, Y-320, T-5224, and Apilimod) and eight targets (CCL2, CTLA4, CXCL8, ICAM1, IL10, IL17A, IL1B, and IL2) have been uncovered to alleviate AM with synergistic effects. Besides, the topological analysis on AM was orchestrated with a network-based concept. We conclude that multi-target agents are a potential strategy to treat comorbid AM, and network-based analysis especially provides critical clues to select targets on AM. Given the limitations of network pharmacology perspectives, the potential therapeutic ligands of the treatment of AM are predicted by topological analysis on PPI networks, and its efficacy in vivo was not validated, which needs to be further evaluated through therapeutic and clinical tests.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/cimb44050153/s1: Table S1: The number of 999 Atopic Dermatitis (AD) targets; The number of 517 Allergic Rhinitis (AR) targets; The number of 2374 Allergic Asthma (AA) targets, and The number of 229 Atopic March (AM) targets. Table S2: The DC-descending order of the number of 210 targets related to AM. Table S3: The top 30% DC-descending order of 65 targets extracted from Supplementary Table S2. Table S4: The top 30% BC-descending order of 20 targets extracted from Supplementary Table S3.

Author Contributions

Conceptualization, methodology, formal analysis, investigation, visualization, data curation, writing—original draft, K.-K.O.; software, investigation, data curation, K.-K.O. and M.A.; validation, writing—review and editing, M.A.; supervision, project administration, D.-H.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data generated or analyzed during this study are included in this published article (and its Supplementary Information files).

Acknowledgments

This study has been made possible with the support of Kangwon National University in 2022.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

ADAtopic Dermatitis
AMAtopic March
ARAllergic Rhinitis
ASAsthma
BCBetweenness Centrality
DCDegree Centrality
MDTMolecular Docking Test
IgEImmunoglobulin E
PPIProtein-Protein Interaction

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Cimb 44 00153 g001 550
Figure 1. The workflow of this study.
Figure 1. The workflow of this study.
Cimb 44 00153 g001
Cimb 44 00153 g002 550
Figure 2. A total of 229 targets related to Atopic March (AM). (A) A total of 999 targets related to Atopic Dermatitis (AD). (B) A total of 517 targets related to Allergic Rhinitis (AR). (C) A total of 2374 targets related to Allergic Asthma (AA).
Figure 2. A total of 229 targets related to Atopic March (AM). (A) A total of 999 targets related to Atopic Dermatitis (AD). (B) A total of 517 targets related to Allergic Rhinitis (AR). (C) A total of 2374 targets related to Allergic Asthma (AA).
Cimb 44 00153 g002
Cimb 44 00153 g003 550
Figure 3. Topological analysis of AM on the PPI network. (A) The PPI network of AM targets (210 nodes, 4346 edges). (B) The PPI network of significant targets filtered from A (65 nodes, 1325 edges). (C) The PPI network of the uppermost targets against AM filtered from B (20 nodes, 185 edges).
Figure 3. Topological analysis of AM on the PPI network. (A) The PPI network of AM targets (210 nodes, 4346 edges). (B) The PPI network of significant targets filtered from A (65 nodes, 1325 edges). (C) The PPI network of the uppermost targets against AM filtered from B (20 nodes, 185 edges).
Cimb 44 00153 g003
Cimb 44 00153 g004 550
Figure 4. The filtering processing of significant compounds against AM. (A) A total of 34 compounds were related to the uppermost 8 targets. (B) A total of 29 compounds were filtered from A (TPSA <140 Å2 or Lipinski’s rule). (C) A total of the uppermost 8 compounds were filtered from B (docking score < −6.0 kcal/mol or the lowest docking score).
Figure 4. The filtering processing of significant compounds against AM. (A) A total of 34 compounds were related to the uppermost 8 targets. (B) A total of 29 compounds were filtered from A (TPSA <140 Å2 or Lipinski’s rule). (C) A total of the uppermost 8 compounds were filtered from B (docking score < −6.0 kcal/mol or the lowest docking score).
Cimb 44 00153 g004
Cimb 44 00153 g005a 550Cimb 44 00153 g005b 550Cimb 44 00153 g005c 550Cimb 44 00153 g005d 550
Figure 5. The molecular docking figure of the core eight ligands on key eight targets (A) CCL2 (PDB ID: 1DON)—Bindarit (PubChem ID: 71354). (B) CTLA-4 (PDB ID: 5XJ3)—CTLA-4 inhibitor (PubChem ID: 101136468). (C) CXCL8 (PDB ID: 2IL8)—Danirixin (PubChem ID: 24780598). (D) ICAM1 (PDB ID: 5MZA)—A-205804 (PubChem ID: 9839311). (E) IL10 (PDB ID: 1LK3)—AX-024 HCl (PubChem ID: 129909862). (F) IL17A (PDB ID: 2VXS)—Y-320 (PubChem ID: 22227931). (G) IL1B (PDB ID: 1HIB)—T-5224 (PubChem ID: 23646877). (H) IL2 (PDB ID: 1M47)—Apilimod (PubChem ID: 10173277).
Figure 5. The molecular docking figure of the core eight ligands on key eight targets (A) CCL2 (PDB ID: 1DON)—Bindarit (PubChem ID: 71354). (B) CTLA-4 (PDB ID: 5XJ3)—CTLA-4 inhibitor (PubChem ID: 101136468). (C) CXCL8 (PDB ID: 2IL8)—Danirixin (PubChem ID: 24780598). (D) ICAM1 (PDB ID: 5MZA)—A-205804 (PubChem ID: 9839311). (E) IL10 (PDB ID: 1LK3)—AX-024 HCl (PubChem ID: 129909862). (F) IL17A (PDB ID: 2VXS)—Y-320 (PubChem ID: 22227931). (G) IL1B (PDB ID: 1HIB)—T-5224 (PubChem ID: 23646877). (H) IL2 (PDB ID: 1M47)—Apilimod (PubChem ID: 10173277).
Cimb 44 00153 g005aCimb 44 00153 g005bCimb 44 00153 g005cCimb 44 00153 g005d
Table
Table 1. Degree of value of the top 30% by betweenness centrality (BC) in PPI.
Table 1. Degree of value of the top 30% by betweenness centrality (BC) in PPI.
No.TargetDegree of Value
1CCL219
2CCL519
3CSF219
4CTLA419
5CXCL819
6FOXP319
7ICAM119
8IFNA119
9IFNG19
10IL1019
11IL1319
12IL17A19
13IL1B19
14IL219
15CCL2018
16CXCL1018
17CXCR418
18IL1818
19CCR217
20EGFR15
Table
Table 2. The physicochemical properties of 34 ligands related to 8 core targets.
Table 2. The physicochemical properties of 34 ligands related to 8 core targets.
No.CompoundsLipinski RulesLipinski’s Violations Bioavailability Score TPSA Compound Classification
MWHBAHBDMLogP
<500<10≤5≤4.15≤1>0.1<140 Å2
1Bindarit 324.37 412.3400.8564.35 Organoheterocyclic compounds
2Pirfenidone185.22 102.4900.5522.00 Organoheterocyclic compounds
3CTLA-4 inhibitor427.35 714.6110.5568.50 Organoheterocyclic compounds
4AZD5069476.52 1031.9300.55158.56 Organosulfur compounds
5Danirixin441.90 642.8100.55115.91 Benzenoids
6A-205804300.40 212.6400.55109.52 Organosulfur compounds
7NECA308.29 74−2.600.55148.41 Nucleosides, nucleotides, and analogues
8Anemoside B41221.38 2615−4.5830.17412.82 Lipids and lipid-like molecules
9AX-024 HCl375.86 403.8600.5521.70 Phenylpropanoids and polyketides
10Y-320505.01 512.410.552.40 Benzenoids
11A-740003474.55 631.2900.55120.66 Benzenoids
12Diacerein368.29 811.1400.55124.04 Benzenoids
13AUDA392.58 333.9500.5578.43 Lipids and lipid-like molecules
14o-Phenanthroline180.21 201.8600.5525.78 Organoheterocyclic compounds
15BC-1215394.51 422.5700.5549.84 Organoheterocyclic compounds
163-Deazaadenosine hydrochloride302.71 64−1.7800.55126.65 Nucleosides, nucleotides, and analogues
17GIBH-130360.41 501.5500.5575.11 Organoheterocyclic compounds
18Falcarindiol260.37 223.3300.5540.16 Lipids and lipid-like molecules
19Muscone238.41 103.9200.5517.07 Organic oxygen compounds
20T-5224517.53 832.5210.55139.06 Benzenoids
21Madecassic acid504.70 653.3310.55118.22 Lipids and lipid-like molecules
22RN-1734353.31 412.8900.5557.79 Benzenoids
23Stylopine323.34 502.5600.5540.16 Alkaloids and derivatives
24Andrographolide350.45 531.9800.5586.99 Organoheterocyclic compounds
25Dilmapimod456.42 833.6900.55100.27 Organoheterocyclic compounds
26Donepezil379.49 403.0600.5538.77 Organoheterocyclic compounds
27Etiprednol dicloacetate485.40 613.0800.5589.90 Lipids and lipid-like molecules
28Minocycline457.48 85−1.600.11164.63 Phenylpropanoids and polyketides
29Talmapimod513.00 502.5810.5565.86 Organoheterocyclic compounds
30VX-702404.32 723.1700.55102.31 Organoheterocyclic compounds
31VX-765509.00 631.1910.55140.06 Organic acids and derivatives
32Apilimod418.49 611.6900.5584.76 Organic nitrogen compounds
33RO2959 hydrochloride463.93 612.9600.5599.25 Benzenoids
34SU 5201290.14 113.8200.5529.10 Organoheterocyclic compounds
Table
Table 3. The binding energy of the eight targets related to eight ligands in the top 30% by betweenness centrality (BC).
Table 3. The binding energy of the eight targets related to eight ligands in the top 30% by betweenness centrality (BC).
Grid BoxHydrogen Bond InteractionsHydrophobic
Interactions
Protein LigandPubChem IDBinding Energy(kcal/mol)CenterDimensionAmino Acid Residue Amino Acid
Residue
CCL2
(PDB ID: 1DON)		Bindarit (★)71354−6.2x = 61.620x = 40Asn6, Ala4 Pro2, Ile5, Ile51
y = 0.356y = 40 Ile20, Arg24, Lys49
z = 0.582z = 40 Asp3
Pirfenidone40362−5.5x = 61.620x = 40Thr16Asn6, Arg18, Ile20
y = 0.356y = 40 Arg24, Lys49, Ile51
z = 0.582z = 40 Ala4
CTLA4
(PDB ID: 5XJ3)		CTLA-4 inhibitor (★)101136468−7.8x = 22.471x = 40Ser32His39, Tyr33, Phe53
y = 17.954y = 40 Ile43, Gly144
z = −35.403z = 40
CXCL8
(PDB ID: 2IL8)		Danirixin (★)24780598−7.0x = −0.977x = 40Asp52Glu70, Ser72, Lys54
y = −7.328y = 40 Asn36, Cys34, Cys7
z = 2.055z = 40 Glu38, Cys9, Leu51
Thr37, Thr12, Pro53
Asn71
ICAM1
(PDB ID: 5MZA)		A-205804 (★)9839311−8.0x = −5.082x = 40Gln998, Lys961Asp911, Pro997, Pro1098
y = −12.607y = 40 Ala752, Gln755,
Asp962
z = 35.679z = 40 Asp994, Tyr995, Ile996
IL10
(PDB ID: 1LK3)		AX-024 HCl (★)129909862−7.5x = 17.574x = 40His41Ser91,Thr116,Tyr181
y = 49.569y = 40 Leu114, Glu154, Gln39
z = 27.605z = 40 Asn93, Ala92
IL17A
(PDB ID: 2VXS)		Y-320 (★)22227931−7.7x = 77.528x = 40N/AGln93, Ser40, Glu95
y = −4.484y = 40 Pro63, Tyr62, Pro59
z = −51.759z = 40 Ser64, Val65, Pro37
Trp67, Thr35, Gln94
AX-024 HCl129909862−7.1x = 77.528x = 40N/AGln93, Ser40, Asn36
y = −4.484y = 40 Pro63, Val65, Trp67
z = −51.759z = 40 Ile66, Ile96, Gln94
Glu95, Pro37
IL1B
(PDB ID: 1HIB)		T-5224 (★)23626877−8.0x = 19.495x = 40Leu80, Val132, Gly136Thr79, Tyr24, Glu25
y = 2.991y = 40Thr137, Leu134Phe133, Trp120, Gly135
z = 73.516z = 40 Lys77
Dilmapimod10297982−7.7x = 19.495x = 40Leu80,Leu26, Val132Glu25, Phe133, Leu134
y = 2.991y = 40 Thr79, Tyr24, Gln81
z = 73.516z = 40
Stylopine6770−7.6x = 19.495x = 40N/ASer125, Pro131, Val132
y = 2.991y = 40 Leu80, Glu25, Phe133
z = 73.516z = 40
Talmapimod9871074−7.4x = 19.495x = 40Ser125, Thr79Leu134, Glu25, Lys74
y = 2.991y = 40 Phe133, Pro131, Lys77
z = 73.516z = 40
A-74000311351968−7.3x = 19.495x = 40Gly136Thr137, Gly135, Asp142
y = 2.991y = 40 Phe133, Pro131, Lys77
z = 73.516z = 40 Thr79, Leu134, Trp120
GIBH-13050938773−7.3x = 19.495x = 40Ser43Asn66, Leu62, Tyr68
y = 2.991y = 40 Leu6, Asn7, Ser5
z = 73.516z = 40 Pro91
VX-70210341154−7.3x = 19.495x = 40Ser43, Ser5, Glu64Asn7, Pro91, Pro87
y = 2.991y = 40Asn66Tyr68, Tyr90, Lys63
z = 73.516z = 40
Madecassic acid73412−7.1x = 19.495x = 40Leu80, Leu134, Lys77Thr79, Phe133, Gly135
y = 2.991y = 40 Asp142, Thr137, Gly136
z = 73.516z = 40
Diacerein26248−7.0x = 19.495x = 40Pro78, Thr79, Ser125Pro131, Phe133, Gly135
y = 2.991y = 40 Lys77
z = 73.516z = 40
Donepezil3152−7.0x = 19.495x = 40Ser125Pro131, Met130, Phe133
y = 2.991y = 40 Thr79, Lys74, Lys77
z = 73.516z = 40 Leu134, Pro78
Andrographolide5318517−6.8x = 19.495x = 40Gly136, Thr137Met130, Phe133, Pro131
y = 2.991y = 40 Trp120, Gly135, Asp142
z = 73.516z = 40 Ser125
BC-121572201045−6.7x = 19.495x = 40Thr79Pro131, Phe133, Ser125
y = 2.991y = 40 Asp142, Gly135, Leu134
z = 73.516z = 40 Lys77
3-Deazaadenosine hydrochloride134828261−6.4x = 19.495x = 40Asn66, Leu62, Glu64Val85, Pro87, Gly61
y = 2.991y = 40 Ser43, Lys63, Tyr68
z = 73.516z = 40 Ser5, Pro91, Tyr90
Etiprednol dicloacetate9935073−6.0x = 19.495x = 40Thr79Leu80, Tyr24, Leu134
y = 2.991y = 40 Lys77, Phe133, Glu25
z = 73.516z = 40
Muscone10947−5.9x = 19.495x = 40N/APro131, Phe133, Leu134
y = 2.991y = 40 Pro78, Thr79, Leu80
z = 73.516z = 40
AUDA10069117−5.7x = 19.495x = 40Tyr90, Ser43, Pro87Asn66, Leu62, Glu64
y = 2.991y = 40 Lys65, Lys63, Ser5
z = 73.516z = 40 Val3, Pro91, Tyr68
o-Phenanthroline1318−5.5x = 19.495x = 40Ser43Gly61, Tyr68, Pro91
y = 2.991y = 40 Asn66, Leu62
z = 73.516z = 40
Falcarindiol5281148−5.0x = 19.495x = 40Thr79, Glu25Lys77, Pro78, Phe133
y = 2.991y = 40 Pro131, Leu80, Leu26
z = 73.516z = 40 Leu82, Val132
RN-17343601086−4.8x = 19.495x = 40Glu64, Ser43Tyr68, Leu62, Val85
y = 2.991y = 40 Pro87, Tyr90, Asn66
z = 73.516z = 40 Lys63
IL2
(PDB ID: 1M47)		Apilimod (★)10173277−6.2x = 6.750x = 40Glu62, Lys43Phe42, Pro65, Tyr107
y = 25.891y = 40 Tyr45
z = 14.189z = 40
RO2959 hydrochloride45274292−6.1x = 6.750x = 40Asp20, His16Asp84, Leu80, Leu12
y = 25.891y = 40 Gln13, Lys9, Asn88
z = 14.189z = 40
SU 5201429070−5.9x = 6.750x = 40N/AGlu62, Tyr107, Tyr45
y = 25.891y = 40
z = 14.189z = 40
(★): The most significant ligand on each target.
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Oh, K.-K.; Adnan, M.; Cho, D.-H. New Insight into Drugs to Alleviate Atopic March via Network Pharmacology-Based Analysis. Curr. Issues Mol. Biol. 2022, 44, 2257-2274. https://doi.org/10.3390/cimb44050153

AMA Style

Oh K-K, Adnan M, Cho D-H. New Insight into Drugs to Alleviate Atopic March via Network Pharmacology-Based Analysis. Current Issues in Molecular Biology. 2022; 44(5):2257-2274. https://doi.org/10.3390/cimb44050153

Chicago/Turabian Style

Oh, Ki-Kwang, Md. Adnan, and Dong-Ha Cho. 2022. "New Insight into Drugs to Alleviate Atopic March via Network Pharmacology-Based Analysis" Current Issues in Molecular Biology 44, no. 5: 2257-2274. https://doi.org/10.3390/cimb44050153

APA Style

Oh, K. -K., Adnan, M., & Cho, D. -H. (2022). New Insight into Drugs to Alleviate Atopic March via Network Pharmacology-Based Analysis. Current Issues in Molecular Biology, 44(5), 2257-2274. https://doi.org/10.3390/cimb44050153

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