Open Access

In silico target fishing and pharmacological profiling for the isoquinoline alkaloids of Macleaya cordata (Bo Luo Hui)

Chinese Medicine201510:37

https://doi.org/10.1186/s13020-015-0067-4

Received: 28 October 2014

Accepted: 10 November 2015

Published: 17 December 2015

Abstract

Background

Some isoquinoline alkaloids from Macleaya cordata (Willd). R. Br. (Bo Luo Hui) exhibited antibacterial, antiparasitic, antitumor, and analgesic effects. The targets of these isoquinoline alkaloids are undefined. This study aims to investigate the compound–target interaction network and potential pharmacological actions of isoquinoline alkaloids of M. cordata by reverse pharmacophore database screening.

Methods

The targets of 26 isoquinoline alkaloids identified from M. cordata were predicted by a pharmacophore-based target fishing approach. Discovery Studio 3.5 and two pharmacophore databases (PharmaDB and HypoDB) were employed for the target profiling. A compound–target interaction network of M. cordata was constructed and analyzed by Cytoscape 3.0.

Results

Thirteen of the 65 predicted targets identified by PharmaDB were confirmed as targets by HypoDB screening. The targets in the interaction network of M. cordata were involved in cancer (31 targets), microorganisms (12 targets), neurodegeneration (10 targets), inflammation and autoimmunity (8 targets), parasitosis (5 targets), injury (4 targets), and pain (3 targets). Dihydrochelerythrine (C6) was found to hit 23 fitting targets. Macrophage migration inhibitory factor (MIF) hits 15 alkaloids (C12, C1116, C1925) was the most promising target related to cancer.

Conclusion

Through in silico target fishing, the anticancer, anti-inflammatory, and analgesic effects of M. cordata were the most significant among many possible activities. The possible anticancer effects were mainly contributed by the isoquinoline alkaloids as active components.

Background

Macleaya cordata (Willd). R. Br. (Bo Luo Hui) (Fig. 1) has been used for the treatment of cancer [1], insect bites [2], and ringworm infection [3] in Mainland China, North America, and Europe. Phytochemical and pharmacological studies demonstrated that the isoquinoline alkaloids derived from M. cordata are its major active components [4]. Thirty isoquinoline alkaloids have been isolated from M. cordata (Fig. 2), including chelerythrine (C12), sanguinarine (C15), sanguidimerine (C17), chelidimerine (C18), berberine (C21), coptisine (C23), allocryptopine (C24, C25), and protopine (C26). These alkaloids exhibited a broad spectrum of biological activities, such as antitumor [58], anti-inflammatory [911], antimicrobial [1214], analgesic [15], and antioxidant [16] activities.
Fig. 1

The original plant of Macleaya cordata

Fig. 2

The isoquinoline alkaloids of Macleaya cordata

In our previous study [17], we found that M. cordata could be counted not only as one of the richest resources in Mainland China among all species of the tribe Chelidonieae, but also as one of the most promising natural resources for drug discovery. M. cordata has gained the attention of pharmacognosists since early 1990s (Fig. 3). However, its obscure molecular actions have hindered its use in drug development.
Fig. 3

The statistics of Pubmed publications on Macleaya cordata between 1972 and 2014

Although protein–ligand docking techniques have been available in virtual drug screening for specific targets, such as tumor necrosis factor α-converting enzyme (TACE) [18], inducible nitric oxide synthase (iNOS) [19], and Janus-activated kinase 2 (JAK2) [20], these docking approaches to virtual screening are often too computationally expensive [21].

This study aims to investigate the compound-target interaction network of isoquinoline alkaloids of M. cordata by reverse pharmacophore database screening technology, and outline its potential action mechanisms.

Methods

Workflow

Figure 4 shows the workflow of this study. The structures and bioactivities of the isoquinoline alkaloids of M. cordata were collected by literature review [17]. The alkaloids were then applied to target fishing with two pharmacophore and target databases, PharmaDB and HypoDB. The hit pharmacophore models were picked out according to the threshold of a predetermined fit value. The results from PharmaDB screening were compared with those from HypoDB screening. After analysis of the hit targets and their associated pathways and diseases, as well as the interactions between the alkaloids and the targets, an action network of M. cordata was constructed. Literature retrieval was simultaneously carried out to verify the findings.
Fig. 4

The workflow of this study

Compound collection

The active components of M. cordata were collected from our own database [17] and the literature. All 26 isoquinoline alkaloids of M. cordata and their bioactivities are listed in Table 1. As shown in Fig. 2, the alkaloids were divided into three classes: benzo[c]phenanthridines (Ben, C1–C18), protoberberines (Ber, C19–C23), and protopines (Pro, C24–C26). Based on the replacement of the C-ring, C1–C9 belong to the dihydro-benzo[c]phenanthridines, C10 is a N-demethyl subtype, and C11–C16 are quaternary ammonium bases that share an iminium moiety (C=N+). The remaining two bisbenzo[c]phenanthridines (BisBen, C17–C18) are epimers to one another.
Table 1

Basic information of the isoquinoline alkaloids in M. cordata

No.

Compounds

Bioactivities

Virtual hitting targets

1

6-Acetonyl-dihydrosanguinarine

Anti-bacteria

Insecticidal

MIF; TTR; NQO1

2

6-Acetonyl-dihydrochelerythrine

Anti-oxidant

Anti-HIV

HSD1; MIF; PDE4D; NQO1; PLA2s; nAChR 7α; AknH; TtgR

3

6-Methoxy-dihydrochelerythrine

Anti-cancer

Anti-parasitic

CAR/RXR; MR; ERα; JNK3; SHBG; AR; 15S-LOX; MMP12; PPARγ; SARS M(pro); Scy D; MAO-A

4

6-Methoxy-dihydrosanguinarine

Anti-bacteria

Anti-cancer

Anti-platelet aggregation

MR; ERα; FNR; MAO-A

5

Bocconoline

Anti-bacteria

Anti-fungal

Opsin 2; HSD1; CAR/RXR; MD; ERα; JNK3; SHBG; Chk1; AR; 15S-LOX; CDK2; CAMKII; Aurora A; PIM1; MMP12; Tankyrase 2; SARS M(pro); PfENR; FabZ; DHODH; CDPKs; FNR; ENR; Scy D; MAO-A

6

Dihydrochelerythrine

Anti-bacteria

Anti-fungal

CAR/RXR; MR; ERα; PPO; TTR; JNK3; SHBG; NQO1; RBP4; 15S-LOX; CK2; PIM1; FabZ; DHODH; SnoaL; FNR; ENR; Scy D; MAO-A; MAO-B; AchE; HIV-1 RT; OSBP

7

Dihydrosanguinarine

Anti-bacteria

Anti-fungal

MR; ERα; PPO; SHBG; 15S-LOX; CDK2; CK2; MAO-A; AchE

8

Oxysanguinarine

Anti-platelet aggregation

PIM1; CK2

9

Oxychelerythrine

Cytotoxic

CAR/RXR; TTR; JNK3; SHBG; 15S-LOX; CLK1; CK2; PIM1; MMP12; MAPK p38; COMP; FabZ; SonaL; FNR; ENR; MAO-A; MAO-B; AchE; OSBP

10

Norsanguinarine

Anti-fungal

CK2; NmrA

11

6-ethoxychelerythrine

Anti-bacteria

Anti-fungal

MIF; TTR; JNK3; GAPDH; nAChR 7α; FabZ; CAT; LmrR; HS5B Pol

12

Chelerythrine

Anti-bacteria

Anti-fungal

Anti-parasitic

Anti-cancer

MIF; TTR; FabZ; HS5B Pol

13

Chelirubine

Anti-proliferative

MIF; NQO1; GR; ZipA-FtsZ; AknH; opdA

14

Macarpine

Cytotoxic

Anti-proliferative

PDE4B; PDE 4B; MIF; TTR; NQO1; PIM1; MAPK p38; GR; ZipA-FtsZ; AknH

15

Sanguinarine

Anti-bacteria

Anti-fungal

Anti-parasitic

Anti-cancer

Anti-oxidant

Hepatotoxicity

MIF; nAChR 7α

16

Sanguilutine

Anti-proliferative

HSD1; MIF; PDE4D; PLA2s; FabZ

17

Sanguidimerine

Unreported

ATTP

18

Chelidimerine

Unreported

MDR HIV-1 Protease

19

Chelanthifoline

Anti-malarial

ALR; ERα; ERβ; MIF; PDK-1; CK2; PIM1; Pi3 Kγ; GR; nAChR 7α; TEM-1; ActR; MAO-B; HIV-1 RT; OSBP

20

Dehydrocicanthifoline

Unreported

HSD1; MR; PDE4B; PDE4D; PPO; MIF; TTR; JNK3; CRBP-2; MAPK p38; AR; PIM1; ZipA-FtsZ; HS5B Pol; HIV-1 RT

21

Berberine

Anti-fungal

Anti-malarial

Anti-cancer

Cytotoxic

Anti-inflammatory

Anti-Alzheimer’s

Anti-fertility

Anti-diabetes

MIF; FabZ; Scy D; AchE

22

Dehydrochelanthifoline

Anti-virus

ERα; ERβ; MIF; GSK-3β; TTR; CDK2; PLA2s; MAO-B

23

Coptisine

Cytotoxic

Anti-diabetes

CYP2D6 inhibition

Anti-oxidative

Anti-spasmodic

MIF

24

α-Allocryptopine

Anti-funga

Anti-arrhythmic

HSD1; MIF; HS5B Pol; Scy D; BACE1

25

β-Allocryptopine

Anti-parasitic

Anti-hepatic fibrosis

HSD1; MIF; HS5B POl; Scy D; BACE1; CRALBP; PPO; TTR; nAChR 7α

26

Protopine

Anti-malarial

Anti-parasitic

Anti-fertility

Anti-spasmodic

NQO1; PfENR; TtgR

Conformation analysis

The structures of all 26 alkaloid candidates were prepared in MOL format, and converted from 2D drawings to 3D models. Their energies were minimized by the software Discovery Studio (DS, v3.5) developed by BIVIA (USA) with the CHARMM force field. A Monte Carlo-based conformational analysis (FAST mode) was performed to generate conformers from the initial conformations. The maximal 255 conformers were allowed with an energy interval of 20 kcal/mol. These alkaloid molecules were rigid, and the number of conformers for each compound was much fewer than 255. Hence, a total of 135 conformers were generated for the 26 isoquinoline alkaloids.

Ligand profiling

A pharmacophore model represented a series of common features of a set of ligands with a special pharmacological target. The features of a pharmacophore model reflected the target–ligand interaction mode. Pharmacophore-based virtual screening was an alternative to docking. By fitting a compound against a panel of pharmacophore models derived from multiple pharmacological targets, the potential targets of the compound can be outlined.

Automated ligand profiling was available in DS 3.5 as the so-called “Ligand Profiler” protocol. The software offered automated pharmacophore-based activity profiling and reporting [22]. In this study, the default parameters of DS 3.5 were used. For each candidate ligand, three or more features were mapped.

Pharmacophore databases

DS 3.5 was equipped with two available pharmacophore databases, i.e., HypoDB [23] and PharmaDB [24]. HypoDB contained about 2500 pharmacophore models derived from protein–ligand 3D complex structures as well as structural data on small bioactive organic molecules. PharmaDB was created from the sc-PDB, a well-accepted data source in structure-based profiling protocols. The sc-PDB was a collection of 3D structures of binding sites found in the Protein Data Bank (PDB). The binding sites were extracted from crystal structures in which a complex between a protein cavity and a small molecule ligand could be identified. PharmaDB consisted of about 68,000 pharmacophores derived from 8000 protein–ligand complexes from the sc-PDB dataset. PharmaDB is a new and updated pharmacophore database developed in collaboration with Prof. Didier Rognan [25, 26]. The target and pharmacophore models from PharmaDB and HypoDB were not entirely consistent. PharmaDB had a larger quantity of targets, while the models in the HypoDB were fewer and described as being experimentally validated. Therefore, in this study, PharmaDB was employed in the target fishing, and HypoDB was used to validate the results.

Regarding PharmaDB, multiple pharmacophores with shape or excluded volume constraints were generated for each protein target. For the pharmacophores with shape constraints, the suffix “-s” was added to the name. In addition, a numerical suffix referred to the ranking of selectivity evaluated by a default algorithm in DS v3.5. In this study, only the best models with “−1” in their names were employed in the ligand profiling [23]. For each pharmacophore database, a classification tree was available, from which the individual models could be selected.

Parameters

In the profiling with PharmaDB, all the pharmacophore models with the shape of the binding pocket were selected for the virtual screening with default settings. The RIGID mode was used as the molecular mapping algorithm. No molecular features were allowed to be missed while mapping these ligands to the pharmacophore models to increase selectivity. The minimal inter-feature distance was set at 0.5 Å. Parallel screening technology for one or more compounds against a multitude of pharmacophore models was available as a Pipeline Pilot protocol. The number of parallel processing procedures was set at 4. The whole calculation was carried on a T5500 workstation (DELL inc., USA).

Binding mode refinement

All the poses of the ligands mapped into the pharmacophore were preserved. A series of target-ligand pairs were selected as emphasis for further examinations. The selection was based upon compatibility with the reported pharmacological activities, as well as traditional usage of M. cordata. A further refinement was carried out in Molecular Operating Environment (MOE) developed by CCG (Canada) to identify the protein–ligand binding modes. Energy minimization was carried out by conjugated gradient minimization with the MMFF94x force field, until an RMSD of 0.1 kcal mol−1 Ǻ−1 was reached.

Network construction

An interaction table between alkaloids and targets was presented as the ligand profiling results. For each target, the name and pathway information were collected from the PDB and KEGG. The diseases related to the targets were collected from the Therapeutic Target Database (TTD; http://bidd.nus.edu.sg/group/cjttd/) [27] and DrugBank (http://www.drugbank.ca/) [28] databases. Compound-Target-Pathway networks were generated by Cytoscape 3.0 (Cytoscape Consortium, USA) [29]. In the networks, nodes represented the compounds, targets, and biological pathways. The edges linking the compound-target and target-pathway represented their relationships and were marked with different types of lines. After the network was built, the basic parameters of the network were computed and analyzed.

Results and discussion

The profiling results are presented in two HTML tables, designated MoleculeFits and PharmacophoreFits. Two descriptors, fit value and shape similarity, were used to measure the fitness of the ligand and pharmacophore. A fit value equal to or greater than 0.3 was used as a heuristic threshold to select targets from the activity profiler. For each pharmacophore model, the classification information of the target can be indicated in a HTML table created by DS 3.5 called as Pharmacophores. Finally, 98 pharmacophore models were mapped. The models belonged to 65 protein targets, and were involved in 60 pathways. A complete list of the 241 target-ligand pairs is shown in Table 2. The name and indication information of the targets are shown in Table 3. The 13 targets verified by HypoDB screening are marked with an asterisk in Table 3.
Table 2

The results of ligand profiling

Class

CMD-ID

ph4

Target short name

Gene

Uniprot-AC

Fit value

Shape similarity

Ben

1

3cfn

TTR

TTHY_HUMAN

P02766

0.750635

0.508475

Ben

1

1h69

NQO1

NQO1_HUMAN

P15559

0.923086

0.536437

Ben

2

3kba

Progesterone receptor

PRGR_HUMAN

P06401

0.334698

0.506897

Ben

2

1xom

PDE4D

PDE4D_HUMAN

Q08499

0.346437

0.527574

Ben

2

2wnj

nAChR 7α

Q8WSF8_APLCA

Q8WSF8

0.43985

0.505495

Ben

2

1h69

NQO1

NQO1_HUMAN

P15559

0.928518

0.504604

Ben

3

2oz7

AR

ANDR_HUMAN

P10275

0.360685

0.500849

Ben

3

2a3i

MR

MCR_HUMAN

P08235

0.375601

0.528195

Ben

3

5std

ScyD

SCYD_MAGGR

P56221

0.418672

0.543119

Ben

3

1l2i

ERα

ESR1_HUMAN

P03372

0.420147

0.542969

Ben

3

1xvp

CAR/RXR

NR1I3_HUMAN

Q14994

0.460385

0.534672

Ben

3

3lmp

PPARγ

PPARG_HUMAN

P37231

0.526039

0.500787

Ben

3

1d2s

SHBG

SHBG_HUMAN

P04278

0.558685

0.563525

Ben

3

2gz7

SARS M(pro)

R1AB_CVHSA

P0C6X7

0.559512

0.547348

Ben

3

2p0m

15S-LOX

LOX15_RABIT

P12530

0.639897

0.537344

Ben

3

3f15

MMP12

MMP12_HUMAN

P39900

0.725254

0.50503

Ben

3

2bxr

MAO-A

AOFA_HUMAN

P21397

0.799156

0.521008

Ben

3

2o2u

JNK3

MK10_HUMAN

P53779

0.835637

0.577825

Ben

4

2bgi

FNR

Q9L6V3_RHOCA

Q9L6V3

0.427793

0.516878

Ben

4

1l2i

ERα

ESR1_HUMAN

P03372

0.452535

0.593291

Ben

4

2bxr

MAO-A

AOFA_HUMAN

P21397

0.793632

0.533917

Ben

5

2ol4

PfENR

Q9BH77_PLAFA

Q9BH77

0.315455

0.518182

Ben

5

3g0u

DHODH

PYRD_HUMAN

Q02127

0.369491

0.508604

Ben

5

2bxr

MAO-A

AOFA_HUMAN

P21397

0.387312

0.544118

Ben

5

2a3i

MR

MCR_HUMAN

P08235

0.387489

0.563771

Ben

5

7std

ScyD

SCYD_MAGGR

P56221

0.422146

0.504744

Ben

5

2uue

CDK2

CDK2_HUMAN

P24941

0.424268

0.567108

Ben

5

2oz7

AR

ANDR_HUMAN

P10275

0.426337

0.510961

Ben

5

3coh

Aurora-A

STK6_HUMAN

O14965

0.4511

0.531532

Ben

5

3kr8

Tankyrase 2

TNKS2_HUMAN

Q9H2K2

0.464801

0.548729

Ben

5

1d2s

SHBG

SHBG_HUMAN

P04278

0.480784

0.571721

Ben

5

1l2i

ERα

ESR1_HUMAN

P03372

0.493882

0.57529

Ben

5

2wel

CAMKII

KCC2D_HUMAN

Q13557

0.493929

0.516729

Ben

5

3fne

ENR

INHA_MYCTU

P0A5Y6

0.50498

0.546169

Ben

5

1xvp

CAR/RXR

NR1I3_HUMAN

Q14994

0.508683

0.576427

Ben

5

5std

ScyD

SCYD_MAGGR

P56221

0.522671

0.566972

Ben

5

2brg

Chk1

CHK1_HUMAN

O14757

0.541427

0.51711

Ben

5

3doz

FabZ

Q5G940_HELPY

Q5G940

0.546379

0.507843

Ben

5

2bgi

FNR

Q9L6V3_RHOCA

Q9L6V3

0.553334

0.549296

Ben

5

3fnf

ENR

INHA_MYCTU

P0A5Y6

0.562321

0.511494

Ben

5

3fnh

ENR

INHA_MYCTU

P0A5Y6

0.614823

0.507547

Ben

5

2p0m

15S-LOX

LOX15_RABIT

P12530

0.673148

0.541414

Ben

5

3dp1

FabZ

Q5G940_HELPY

Q5G940

0.687924

0.53816

Ben

5

2gz7

SARS M(pro)

R1AB_CVHSA

P0C6X7

0.694125

0.551789

Ben

5

2o2u

JNK3

MK10_HUMAN

P53779

0.805153

0.553719

Ben

5

3f15

MMP12

MMP12_HUMAN

P39900

0.893862

0.507187

Ben

6

3fj6

DHODH

PYRD_HUMAN

Q02127

0.341464

0.566038

Ben

6

5std

ScyD

SCYD_MAGGR

P56221

0.364451

0.555556

Ben

6

1d2s

SHBG

SHBG_HUMAN

P04278

0.367805

0.529289

Ben

6

2a3i

MR

MCR_HUMAN

P08235

0.368001

0.559289

Ben

6

1xvp

CAR/RXR

NR1I3_HUMAN

Q14994

0.423023

0.572534

Ben

6

2v60

MAO-B

AOFB_HUMAN

P27338

0.436774

0.511294

Ben

6

1l2i

ERα

ESR1_HUMAN

P03372

0.451108

0.529175

Ben

6

1rbp

RBP4

RET4_HUMAN

P02753

0.486949

0.516378

Ben

6

2nsd

ENR

INHA_MYCTU

P0A5Y6

0.509088

0.530738

Ben

6

1kgj

TTR

TTHY_RAT

P02767

0.522255

0.529412

Ben

6

1tv6

HIV-1 TR

POL_HV1B1

P03366

0.564494

0.529981

Ben

6

2bgi

FNR

Q9L6V3_RHOCA

Q9L6V3

0.636419

0.536325

Ben

6

2p0m

15S-LOX

LOX15_RABIT

P12530

0.663082

0.545045

Ben

6

3imu

TTR

TTHY_HUMAN

P02766

0.690803

0.577011

Ben

6

3dp1

FabZ

Q5G940_HELPY

Q5G940

0.705916

0.565401

Ben

6

2o2u

JNK3

MK10_HUMAN

P53779

0.705945

0.507463

Ben

6

1h69

NQO1

NQO1_HUMAN

P15559

0.755827

0.508911

Ben

6

2bxr

MAO-A

AOFA_HUMAN

P21397

0.795152

0.541573

Ben

6

1sjw

SnoaL

Q9RN59_STRNO

Q9RN59

0.904111

0.661572

Ben

6

2j3q

AChE

ACES_TORCA

P04058

0.992134

0.661327

Ben

7

2x1n

CDK2

CDK2_HUMAN

P24941

0.329358

0.521253

Ben

7

1d2s

SHBG

SHBG_HUMAN

P04278

0.340019

0.542857

Ben

7

1l2i

ERα

ESR1_HUMAN

P03372

0.465563

0.553846

Ben

7

2j3q

AChE

ACES_TORCA

P04058

0.470546

0.67

Ben

7

2p0m

15S-LOX

LOX15_RABIT

P12530

0.639874

0.545254

Ben

7

2bxr

MAO-A

AOFA_HUMAN

P21397

0.822509

0.548694

Ben

8

3bgp

PIM-1

PIM1_HUMAN

P11309

0.659102

0.52193

Ben

9

2wu7

CLK1

CLK3_HUMAN

P49761

0.333353

0.541053

Ben

9

1d2s

SHBG

SHBG_HUMAN

P04278

0.40988

0.526096

Ben

9

2nsd

ENR

INHA_MYCTU

P0A5Y6

0.417703

0.522727

Ben

9

1tha

TTR

TTHY_HUMAN

P02766

0.42188

0.505071

Ben

9

1xvp

CAR/RXR

NR1I3_HUMAN

Q14994

0.459386

0.600775

Ben

9

1fbm

COMP

COMP_RAT

P35444

0.540756

0.509542

Ben

9

2p0m

15S-LOX

LOX15_RABIT

P12530

0.609094

0.548596

Ben

9

2bxr

MAO-A

AOFA_HUMAN

P21397

0.636415

0.524336

Ben

9

3iw7

MAPK p38

MK14_HUMAN

Q16539

0.671331

0.532803

Ben

9

1sjw

SnoaL

Q9RN59_STRNO

Q9RN59

0.679871

0.665953

Ben

9

2bgi

FNR

Q9L6V3_RHOCA

Q9L6V3

0.68446

0.509554

Ben

9

3dp1

FabZ

Q5G940_HELPY

Q5G940

0.723083

0.601732

Ben

9

2v60

MAO-B

AOFB_HUMAN

P27338

0.818911

0.501006

Ben

9

2o2u

JNK3

MK10_HUMAN

P53779

0.878824

0.532609

Ben

9

2j3q

AChE

ACES_TORCA

P04058

0.992667

0.679157

Ben

10

2wmd

NmrA

NMRL1_HUMAN

Q9HBL8

0.635677

0.601671

Ben

11

3doz

FabZ

Q5G940_HELPY

Q5G940

0.380298

0.514677

Ben

11

3kvx

JNK3

MK10_HUMAN

P53779

0.408174

0.518987

Ben

11

2wnj

nAChR 7α

Q8WSF8_APLCA

Q8WSF8

0.408648

0.512476

Ben

11

3doy

FabZ

Q5G940_HELPY

Q5G940

0.456816

0.515444

Ben

11

1k3t

GAPDH

G3PG_TRYCR

P22513

0.56209

0.500931

Ben

11

3lmp

PPARγ

PPARG_HUMAN

P37231

0.648243

0.508527

Ben

11

1qca

CAT

CAT3_ECOLX

P00484

0.780585

0.505747

Ben

11

3f8f

LmrR

A2RI36_LACLM

A2RI36

0.817481

0.51932

Ben

13

1xan

GR

GSHR_HUMAN

P00390

0.573697

0.520833

Ben

13

3kba

Progesterone receptor

PRGR_HUMAN

P06401

0.651629

0.522059

Ben

13

1h69

NQO1

NQO1_HUMAN

P15559

0.811055

0.503055

Ben

13

3a3w

opdA

Q93LD7_RHIRD

Q93LD7

0.833715

0.510158

Ben

14

3huc

MAPK p38

MK14_HUMAN

Q16539

0.345074

0.534091

Ben

14

1xan

GR

GSHR_HUMAN

P00390

0.517848

0.510823

Ben

14

1h69

NQO1

NQO1_HUMAN

P15559

0.723867

0.515504

Ben

14

1xom

PDE4D

PDE4D_HUMAN

Q08499

0.745933

0.503704

Ben

14

1xlx

PDE4B

PDE4B_HUMAN

Q07343

0.796555

0.52037

Ben

15

2wnj

nAChR 7α

Q8WSF8_APLCA

Q8WSF8

0.495424

0.509356

Ben

16

3kba

Progesterone receptor

PRGR_HUMAN

P06401

0.342606

0.537671

Ben

16

1xom

PDE4D

PDE4D_HUMAN

Q08499

0.816692

0.539427

BisBen

17

1r5 l

ATTP

TTPA_HUMAN

P49638

0.32356

0.514156

BisBen

18

1rq9

MDR HIV-1 Protease

Q5RTL1_9HIV

Q5RTL1

0.621229

0.507743

Ber

19

1u3s

ERβ

ESR2_HUMAN

Q92731

0.408794

0.535377

Ber

19

2j3q

AChE

ACES_TORCA

P04058

0.485705

0.596737

Ber

19

3l54

Pi3 Kγ

PK3CG_HUMAN

P48736

0.498919

0.59589

Ber

19

1pzo

TEM-1

BLAT_ECOLX

P62593

0.523404

0.526667

Ber

19

2ikg

ALR

ALDR_HUMAN

P15121

0.561704

0.507109

Ber

19

1c1c

HIV-1 TR

POL_HV1H2

P04585

0.577074

0.542373

Ber

19

2r7b

PDK-1

PDPK1_HUMAN

O15530

0.587223

0.533049

Ber

19

1yye

ERβ

ESR2_HUMAN

Q92731

0.679767

0.56691

Ber

19

1qkt

ERα

ESR1_HUMAN

P03372

0.681913

0.56351

Ber

19

1xan

GR

GSHR_HUMAN

P00390

0.715506

0.548544

Ber

19

2wnj

nAChR 7α

Q8WSF8_APLCA

Q8WSF8

0.87937

0.501031

Ber

19

3b6c

ActR

Q53901_STRCO

Q53901

0.880577

0.597561

Ber

19

1x78

ERβ

ESR2_HUMAN

Q92731

0.909059

0.522565

Ber

20

1xm4

PDE4B

PDE4B_HUMAN

Q07343

0.402388

0.569138

Ber

20

1tha

TTR

TTHY_HUMAN

P02766

0.45615

0.514286

Ber

20

1tv6

HIV-1 TR

POL_HV1B1

P03366

0.459002

0.521154

Ber

20

2nw4

AR

ANDR_RAT

P15207

0.463505

0.541203

Ber

20

1opb

CRBP2

RET2_RAT

P06768

0.485837

0.534653

Ber

20

2waj

JNK3

MK10_HUMAN

P53779

0.572085

0.603104

Ber

20

1kgj

TTR

TTHY_RAT

P02767

0.740087

0.56531

Ber

20

1xom

PDE4D

PDE4D_HUMAN

Q08499

0.811727

0.542406

Ber

20

1xlx

PDE4B

PDE4B_HUMAN

Q07343

0.859016

0.51341

Ber

20

3i6d

PPO

PPOX_BACSU

P32397

0.97618

0.570499

Ber

21

5std

ScyD

SCYD_MAGGR

P56221

0.324086

0.505682

Ber

21

2j3q

AChE

ACES_TORCA

P04058

0.992907

0.672209

Ber

22

1di8

CDK2

CDK2_HUMAN

P24941

0.406566

0.501094

Ber

22

1u3s

ERβ

ESR2_HUMAN

Q92731

0.661777

0.509434

Pro

24

5std

ScyD

SCYD_MAGGR

P56221

0.538905

0.543636

Pro

24

3ine

BACE1

BACE1_HUMAN

P56817

0.54561

0.522968

Pro

25

1tyr

TTR

TTHY_HUMAN

P02766

0.356425

0.526412

Pro

25

3inf

BACE1

BACE1_HUMAN

P56817

0.37118

0.504303

Pro

25

2wnj

nAChR 7α

Q8WSF8_APLCA

Q8WSF8

0.553029

0.533461

Pro

25

3ine

BACE1

BACE1_HUMAN

P56817

0.597712

0.51259

Pro

25

3hx3

CRALBP

RLBP1_HUMAN

P12271

0.604625

0.513158

Pro

25

5std

ScyD

SCYD_MAGGR

P56221

0.763034

0.522523

Pro

26

2ow2

PfENR

MMP9_HUMAN

P14780

0.302479

0.507865

Pro

26

2f1o

NQO1

NQO1_HUMAN

P15559

0.432407

0.52183

Table 3

The targets identified

Targets

Short name

Type

Pathway

Diseases

Retinaldehyde-binding protein

CRALBP

Research

Retinaldehyde metabolism

Retinitis pigmentosa

Rhodopsin

Opsin 2

Research

Retina metabolism

Retinitis pigmentosa

11-Beta-hydroxysteroid dehydrogenase

HSD1

Successful

Glucocorticoid concentration

Diabetes

Osteoporosis

Hepatotoxicity

CAR/RXR heterodimer

CAR/RXR

Research

Triglyceride metabolism

Diabetes

Hepatitis

Aldose reductase

ALR

Successful

Glucolipid metabolism

Diabetes

Pain

Mineralocorticoid receptors

MR

Successful

Na+/K+ equilibrium

Inflammatory, autoimmune disease

Injury

Phosphodiesterase 4B

PDE4B

Successful

AKT/mTOR pathway

Cancer

Obesity

Phosphodiesterase 4D

PDE4D

Successful

Intracellular cAMP//CREB signaling

Cancer

Alzheimer’s

Protoporphyrinogen oxidase

PPO

Research

Heme biosynthesis

Cancer

Parasitosis

Transthyretin

TTR

Clinic Trial

Thyroxine carrier

Cancer

Alzheimer’s

Mitogen-activated protein kinase 10

JNK3

Research

GbRH/ErbB/MAPK/insulin signaling pathway

Cancer

Alzheimer’s

Sex hormone-binding globulin

SHBG

Research

Sex steroids biosynthesis

Cancer

NAD(P)H:quinone oxidoreductase

NQO1

Research

Quinones metabolism

Cancer

Cellular retinol binding protein II

CRBP2

Research

Retinol metabolism

Cancer

Estrogen receptor alphaa

ERαa

Successful

Estrogen metabolism

Insulin-like growth factor pathway

Cancer

Alzheimer’s

Injury

Osteoporosis

Alpha-tocopherol (alpha-T) transfer protein

ATTP

Research

α-Tocopherol metabolism

Cancer

Human serum retinol binding protein 4

RBP4

Research

Retinol metabolism

Cancer

Estrogen receptor betaa

ERβa

Successful

Estrogen metabolism

MAPK, PI3K signaling

Cancer

Alzheimer’s

Injury

Checkpoint kinase 1a

Chk1a

Research

DNA damage response

Cancer

Androgen receptor

AR

Successful

Hormone metabolism

Cancer

Reticulocyte 15S-lipoxygenase

15S-LOX

Research

Arachidonic acid metabolism

Cancer

3-Phosphoinositide-dependent kinase-1a

PDK-1a

Research

Phosphatidylinositol 3 kinase (PI3K) signaling

Cancer

Casein kinase 2a

CK2a

Research

Ser/Thr pathway

Cancer

Cyclin dependent kinase 2a

CDK2a

Research

Cell cycle

Cancer

Calcium/calmodulin dependent protein kinase II delta

CAMKII

Research

NF-κB-mediated inflammatory response

Ca2+-linked signaling

Cancer

Inflammatory, autoimmune disease

Dual-specificity protein kinase 1

CLK1

Research

Nuclear redistribution of SR proteins

Cancer

Proto-oncogene serine threonine kinasea

PIM-1a

Research

Cell cycle regulation JAK/STAT pathway

Cancer

Aurora kinase A

Aurora-A

Clinical trial

Cell cycle arrest

Cancer

Matrix metalloproteinases

MMP12

Research

Cell invasion, metastasis

Cancer

Inflammatory, autoimmune disease

Phospholipase A2

PLA2s

Successful

VEGF/MAPK/GnRH signaling

Cancer

Inflammatory, autoimmune disease

Mitogen-Activated Protein Kinases p38

MAPK p38

Clinical trial

MAPK signaling

Cancer

Pain

Inflammatory, autoimmune disease

Dermatosis

Tankyrase 2

Tankyrase 2

Research

Canonical Wnt signaling

Cancer

Phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit gamma isoform

Pi3Kγ

Research

Cancer migration, invasion

Inositol phosphate metabolism

Cancer

Inflammatory, autoimmune disease

PPARgamma-LBDa

PPARγa

Research

LPS-induced iNOS expression

Cancer

Inflammatory, autoimmune disease

Osteoporosis

Cartilage oligomeric matrix protein

COMP

Research

Bone regeneration

Autoimmune disease

Injury

Severe acute respiratory syndrome coronavirus (SARS-CoV) main protease (M(pro))

SARS M(pro)

Research

Virus maturation

Virus infection

Glycosomal glyceraldehyde-3-Phosphate Dehydrogenase

GAPDH

Successful

Glyceraldehydes metabolism

Parasitosis

Glutathione disulfide oxidoreductase

GR

Research

Glutathione metabolism

Parasitosis

Acyl carrier protein reductasea

PfENRa

Successful

Fatty acid biosynthesis

Parasitosis

Acetylcholine binding protein alpha7

nAChR 7α

Successful

Calcium signaling pathway

Alzheimer’s

Pain

3R-hydroxyacyl-acyl carrier protein dehydratase

FabZ

Research

Fatty acid biosynthesis

Parasitosis

Dihydroorotate dehydrogenase

DHODH

Successful

Pyrimidine metabolism

Parasitosis

TEM-1 Beta-Lactamasea

TEM-1a

Successful

Cefotaxime metabolism

Bacterial infection

Chloramphenicol acetyltransferase

CAT

Research

Chloramphenicol metabolism

Bacterial infection

Polyketide cyclase SnoaL

SnoaL

Research

Nogalamycin biosynthesis

Bacterial infection

ZipA attaches FtsZ protein

ZipA-FtsZ

Research

Cell division

Bacterial infection

Ferredoxin-NADP+ reductase

FNR

Successful

Redox metabolism

Bacterial infection

Polyketide cyclase AknH

AknH

Research

Aclacinomycin biosynthesis

Bacterial infection

Enoyl-acyl carrier protein reductase

ENR

Successful

Fatty acid biosynthesis

Bacterial infection

Multidrug binding protein TtgR

TtgR

Research

Active extrusion of drug

Bacterial infection

NmrA-like family domain

NmrA

Research

Transcriptional repress

Fungal infection

Bacterial phosphotriesterase

opdA

Research

Organophosphate metabolism

Bacterial infection

Streptomyces coelicolor TetR family protein ActRa

ActRa

Research

Transcriptional repress

Bacterial infection

Multidrug binding transcriptional regulator LmrR

LmrR

Research

Autoregulatory mechanism

Bacterial infection

Scytalone Dehydratase

ScyD

Research

Fungicide

Fungal infection

Human monoamine oxidase A

MAO-A

Successful

Monoamines metabolism

Depression

Acetylcholin esterase

AChE

Successful

Glycerophospholipid metabolism

Alzheimer’s

Parkinson’s

β-Site amyloid precursor protein cleaving enzyme

BACE1

Clinical trial

Neuregulin processing

Alzheimer’s

Multidrug-resistant HIV-1 proteasea

MDR HIV-1 proteasea

Successful

Self-activation

AIDs

HIV-1 reverse transcriptase

HIV-1 TR

Successful

ATP-dependent excision, pyrophosphorolysis

AIDs

Oxysterol binding protein

OSBP

Research

Intracellular lipid homeostasis

Signal conduction

Virus infection

Cancer

Rhodopsin

Opsin 2

Research

Rod photoreceptor

Retinitis pigmentosa

Macrophage migration inhibitory factor

MIF

Clinical trial

Phenylalanine, tyrosine metabolism

Cancer

Inflammatory, autoimmune disease

Glycogen synthase kinase-3 beta

GSK-3β

Research

Glycogen biosynthesis

Cancer

Alzheimer’s

Diabetes

Hepatitis C virus (HCV) polymerase

HS5B Pol

Successful

DNA biosynthesis

Virus infection

aThe targets verified by HypoDB screening

Analysis of the interaction network

A topological analysis of the interaction network offered insights into the biologically relevant connectivity patterns, and highly influential compounds or targets. Some Chinese medicines had been investigated by interaction network analysis [3032].

The pharmacological network of M. cordata had three types of nodes (Fig. 5). The 26 alkaloid nodes formed the core of the network, and were surrounded by 65 target nodes. Each target was linked to at least one pathway. A total of 60 pathway nodes constituted the outer layer of the network. Each alkaloid was the center of a star-shaped action net except for the two bisbenzo[c]phenanthridines (BisBen), which were only linked to one target and one pathway, respectively. The alkaloids and targets were strongly interconnected in many-to-many relationships.
Fig. 5

The pharmacological network of Macleaya cordata. Hexagon, targets; Rectangle, biopathway; Ellipse, alkaloids (bright green Ben, dark green BisBen, breen Ber, orange Pro)

A general overview of the global topological properties of the network was obtained from the statistical data by the Network Analyzer of Cytoscape. The diameter of the network was 8.0, the centralization was 0.14, and the density was 0.024. The node degree indicated the number of edges linking to other nodes. The highly connected nodes were referred to as the hubs of the network. The degrees of all the alkaloids (Fig. 6a) and important targets (Fig. 6b) were investigated. The compounds with higher degree values, such as C5, C6, C9, C19, and C20, that might participate in more interactions than the other components were the hubs in the network. The target degree values mostly ranged between 2 and 7. The targets with the highest degree values included MIF (16), TTR (11), FabZ* (11), ERα* (10), and MR (10). The targets with higher degree values might be involved in the pharmacological actions of M. cordata.
Fig. 6

Degree distribution in the network. a alkaloids, b targets

Interpreting the pharmacological actions

By mining the PubMed and TTD, the targets of M. cordata in the PharmaDB profiling results were annotated with biological functions and clinical indications (Table 3). Furthermore, the targets were classified according to the reported pharmacological activities of M. cordata as follows: microorganism (including bacterial, fungal, and viral) infection (12 targets, with 3 targets verified by HypoDB screening), parasitic disease (5 targets, with 2 targets validated by HypoDB screening), pain (3 targets), cancer (31 targets, with 8 targets confirmed by HypoDB screening), inflammation (8 targets, with 1 target verified by HypoDB screening), and injury (4 targets, with 2 targets fished by HypoDB screening).

Antibacterial activity

The extracts and their purified alkaloids from M. cordata exhibited notable activities against Staphylococcus aureus, Pseudomonas aeruginosa, Escherichia coli, Bacillus subtilis, Tetracoccus spp., and methicillin-resistant Staphylococcus aureus (MRSA) [12, 33]. In this study, 12 proposed targets were closely related to microorganisms, and seven of them exhibited antibacterial activities (Fig. 7). the key types of alkaloids with antibacterial activity were dihydro-benzo[c]phenanthridine alkaloids and protoberberines.
Fig. 7

The compounds mapping of microorganism related targets

Five targets (LmrR, TEM-1*, CAT, FNR, and ActR) were related to multidrug-resistant bacterial strains. LmrR, a multidrug binding transcriptional regulator and the predicted target of C11, was a PadR-related transcriptional repressor that regulated the production of LmrCD, a major multidrug ABC transporter in Lactococcus lactis [34, 35]. TEM-1* (TEM-1 beta-lactamase) fished by C19 was one of the antibiotic-resistance determinants for penicillins, early cephalosporins, and novel drugs from their derivatives [36]. A new drug, Avibactam™, innovated by AstraZeneca is a TEM-1 inhibitor that has already entered phase III clinical development [37]. In addition, chloramphenicol acetyltransferase (CAT), an antibiotic-inactivating enzyme predicted by C11, catalyzed the acetyl-S-CoA-dependent acetylation of chloramphenicol at the 3-hydroxyl group and resulted in chloramphenicol-resistance in bacteria [38]. Ferredoxin-NADP+ reductase (FNR), targeted in silico by C4, C5, C6, and C9, participated in numerous electron transfer reactions, had no homologous enzyme in humans, and was a target for the accumulation of multidrug-resistant microbial strains [39]. The Streptomyces coelicolor TetR family protein ActR* was found by C19. ActR* may mediate timely self-resistance to an endogenously-produced antibiotic. TetR-mediated antibiotic-resistance might have been acquired from an antibiotic-producer organism [40].

Two targets indicating other pathways were involved in the antibacterial activity. The ZipA-FtsZ complex was fished by C13, C14, and C20 (Fig. 8). ZipA was a membrane-anchored protein in E. coli that interacted with FtsZ-mediated bacterial cell division, and was considered a potential target for antibacterial agents [41]. The target ENR catalyzed an essential step in fatty acid biosynthesis. ENR was a target for narrow-spectrum antibacterial drug discovery because of its essential role in metabolism and its sequence conservation across many bacterial species [42].
Fig. 8

Three alkaloids mapped to ZipA-FtsZ. Left the crystal structure and pharmacophore of target, right the alkaloids fit to the pharmacophore

Antiparasitic activity

M. cordata showed remarkable effects against Ichthyophthirius multifiliis in grass carp [43] and richadsin [44], as well as against Dactylogyrus intermedius in Carassius auratus [45]. The total alkaloids of M. cordata were able to kill gastrointestinal parasites [46].

In this study, five targets involved in parasitic diseases were predicted. Because of the lack of reported protein–ligand crystal structures for parasitosis, these five targets were not related to the above parasitosis in either humans or other animals. However, the findings suggested the potential of M. cordata to treat other parasitosis, such as malaria, Chagas disease, and Kala-azar. The enoyl-acyl carrier reductase PfENR* fished by two alkaloids (C5 and C26) and the (3R)-hydroxymyristoyl acyl carrier protein dehydratase FabZ* in silico targeted by six alkaloids (C5, C6, C9, C11, C12, and C16) were involved in the fatty acid biosynthesis of Plasmodium falciparum. The antioxidant enzyme GR fished by C13, C14, and C19 was a target for antimalarial drug development [47]. The target glycosomal glyceraldehyde-3-phosphate dehydrogenase (GAPDH) found by C11 was a target for the development of novel chemotherapeutic agents for the treatment of Chagas disease [48]. Dihydroorotate dehydrogenase (DHODH) retrieved by C5 and C6 was related to both Leishmania infection and Trypanosoma infection [49].

Analgesic activity

A mixture of the isoquinoline alkaloids from M. cordata exhibited strong analgesic activity towards the pain caused by inflammatory cytokines and direct peripheral nerve stimulation [50]. In this study, three targets related to pain were identified. nAChR7α was abundantly expressed in the central and peripheral nervous systems, and involved in subchronic pain and inflammation [51]. In the profiling results, nAChR7α was picked out by five alkaloids (C2, C11, C15, C19, and C25). MAPK p38 fished by C9, C14, and C20 was involved in the development and maintenance of inflammatory pain [52, 53]. The reductase ALR fished by C19 was a specific target of painful diabetic neuropathy [54, 55]. Inhibitors of ALR relieved pain and improved somatic and autonomic nerve function [56]. In addition, based on the action network, berberines (Ber) such as C19 and C20 may also be involved in the analgesic activity of M. cordata.

Anti-inflammatory activity

Eight targets related to inflammation were identified in this study. Phosphatidylinositol-4, 5-bisphosphate 3-kinase catalytic subunit gamma isoform (PI3 Kγ) fished by C19 recruited leukocytes [57]. The proteinase MMP12, also known as macrophage metalloelastase (MME) or macrophage elastase (ME), was identified with three fitted compounds (C3, C5, and C9) in this study. MMP12 mediated neutrophil and macrophage recruitment and T cell polarization [58], and was a potential therapeutic target for asthma [59]. PPARγ* fished by C3 was another inflammation-related target. Some early findings demonstrated the anti-inflammatory effects of PPARγ by activating human or murine monocytes/macrophages and monocyte/macrophage cell lines [60].

MAPK p38 was involved in a signaling cascade controlling cellular responses to inflammatory cytokines, and it was verified for this pathway in murine macrophage RAW264.7 cells that the M. cordata extract increased both the mRNA and protein levels of cytoprotective enzymes including heme oxygenase-1 (HO-1) and thioredoxin 1 via activation of the p38 MAPK/Nrf2 pathway [16]. The kinase calcium/calmodulin-dependent protein kinase II (CAMKII) was a regulator of intracellular Ca2+ levels, which triggered activation of the transcription factor nuclear factor-kappa B (NF-κB) after T-cell receptor stimulation. An inhibitory effect of CAMKII on NF-κB was confirmed [61]. Phospholipase A2 (PLA2s) was a key enzyme in prostaglandin (PG) biosynthesis for discharging arachidonic acid. Selective inhibitors of PLA2s were implicated in inflammation and connected to diverse diseases, such as cancer, ischemia, atherosclerosis, and schizophrenia [62].

The target mineralocorticoid receptor (MR) fished by five compounds (C3, C4, C6, C7, and C20) was activated by mineralocorticoids, such as aldosterone and deoxycorticosterone, as well as by glucocorticoids, like cortisol. Antagonists of MR had cardioprotective and anti-inflammatory effects in vivo via aldosterone-independent mechanisms [63]. Macrophage migration inhibitory factor (MIF) was involved in both innate and adaptive immune responses. Inhibitors of MIF were potential anti-inflammatory agents [64].

Seven of the eight predicted targets were also related to cancer. These dual correlative targets were PI3Kγ, MMP12, PPARγ*, MAPK p38, CAMKII, PLA2s, and MIF. Their matching compounds are shown in Fig. 9, and the benzo[c]phenanthridine (Ben) alkaloids and berberine (Ber) alkaloids were involved in the anti-inflammatory activity.
Fig. 9

Alkaloid C11 mapped to GAPDH. Left the crystal structure and pharmacophore of GAPDH, upper right the alkaloid C11 docked into the target, lower right C11 fitting into the pharmacophore and the shape of the pocket

Injury healing activity

In this study, four predicted targets (ERα*, ERβ*, MR, and COMP) were involved in injury repair. Among them, ERα*, ERβ*, and MR were linked with internal injuries, such as brain injury [65], vascular injury [66], and neuronal injury [67]. The other target, cartilage oligomeric matrix protein (COMP), found by C9 was a non-collagenous extracellular matrix protein found predominantly in cartilage, but also in tendon, ligament, and meniscus [68]. COMP was a marker for joint destruction associated with osteoarthritis, rheumatoid arthritis, trauma, and intense activity [69].

Antitumor activity

Both the mixed and single alkaloids of M. cordata strongly inhibited proliferation and induced apoptosis of cancer cells [6, 70]. The anticancer drug Ukrain™ is an isoquinoline type. The major components of Ukrain™ are chelidonine, sanguinarine, chelerythrine, protopine, and allocryptopine. Ukrain™ exerted cytotoxic effects in cancer cells without negative effects on normal cells [71], and had radiosensitization effects on cancer cells, while exerting radioprotective effects on normal cells [72].

In the pharmacological profiling results, almost half of the predicted targets (31 of 65 targets) had a close relationship with cancer, and ten of them (Table 3) successfully entered into clinical trial observations. In total, nine targets related to cancer were fished by more than five compounds. The results revealed promising prospects for M. cordata in antitumor drug research and development. Based on the action network (Fig. 5), possible antitumor molecular mechanisms of M. cordata were analyzed as follows: (1) most possible effective targets and (2) most likely contributing compounds.

The MIF column was particularly tall (Fig. 10) because it was fished by 15 compounds, including all quaternary benzo[c]phenanthridine (Ben) alkaloids (C11–C16), two other benzo[c]phenanthridine (Ben) alkaloids, five protoberberine (Ber) alkaloids, and two protopine (Pro) alkaloids. The discovered pathways of these 15 compounds mainly included NF-κB and ERK signaling pathways [73, 74], Bax/Bcl and caspase-dependent pathway [75], ROS-mediated mitochondrial pathway [76], p38 MAPK/Nrf2 pathway [77], and VEGF-induced Akt phosphorylation pathway [78]. All of these pathways were linked closely with MIF [7984]. However, there have been no experimental reports on to the interactions between MIF and these alkaloids.
Fig. 10

The alkaloids mapping of cancer related targets

Both transthyretin (TTR) and proto-oncogene serine threonine kinase* (PIM-1) were found by seven compounds. TTR was a biomarker for lung cancer [85] and pancreatic ductal adenocarcinoma [86], but has not yet been confirmed as a therapeutic target. PIM-1* fished by C5, C6, C8, C9, C14, C19, and C20, and also verified by HypoDB screening, was responsible for cell cycle regulation, antiapoptotic activity, mediation of homing, and migration of receptor tyrosine kinases via the JAK/STAT pathway. PIM-1 was upregulated in many hematological malignancies and solid tumors. Although PIM kinases were described as weak oncogenes, they were heavily targeted for anticancer drug discovery [87]. C12 was partially involved in the JAK/STAT pathway [88].

The benzo[c]phenanthridine (Ben) alkaloids of M. cordata hit cancer-related targets a total of 75 times, compared with 25 times for protoberberines (Ber), five times for protopines (Pro), and one time for bis-benzo[c]phenanthridines (BisBen) (Fig. 11). According to the quantitative determination of alkaloids from M. cordata, the quaternary benzo[c]phenanthridine alkaloids C12, C13, and C15 were the main active components [89]. However, the dihydro-benzo[c]phenanthridines such as C5, C6, and C9 rarely reached the limit of detection (LOD), and hit more targets than the main alkaloids. As the quaternary and dihydro-benzo[c]phenanthridines can be transformed into one another, the dihydro-benzo[c]phenanthridines could be active compounds in vivo. The metabolism of C15 was examined in pig liver microsomes and cytosol by electrospray ionization hybrid ion trap/time-of-flight mass spectrometry, and C7 was one of the main metabolites in liver microsomes and the only metabolite in cytosol [90]. Hence, the issue of whether the dihydro-benzo[c]phenanthridines were the main compounds combining with the targets in vivo requires further investigation.
Fig. 11

The hit number of the alkaloids to cancer related targets

Among the 31 cancer-related targets, at least seven (including MIF, PPARγ*, CAMKII, and Pi3Kγ) were involved in the immune system. These immune-associated targets might be crucial to for oncotherapy with M. cordata.

Potential pharmacological activities

According to the pharmacological profiling, some unreported pharmacological performances of M. cordata emerged. In this study, 10 targets linked with neurodegeneration were fished, among which AChE and MAO-B were crucial therapeutic targets in Alzheimer’s disease and Parkinson’s disease [9194].

In addition, antiviral activities, especially anti-HIV, anti-SARS coronavirus, and antifungal activities, were kinds of extensions of the antibacterial function of M. cordata. The possible anti-HIV activity was notable, because HIV-1 reverse transcriptase and multidrug-resistant HIV-1 protease* were particularly related to AIDS [9599]. Meanwhile, the anti-HIV activity was partly confirmed by HypoDB screening. The protein SARS-CoV M(pro) predicted by C3 and C5 was an attractive target for structure-based drug design of anti-SARS drugs owing to its indispensability for the maturation of severe acute respiratory syndrome coronavirus (SARS-CoV) [100]. Another target, HS5B Pol, fished by five alkaloids was a target for anti-HCV therapeutic advances [101]. Inhibitors of HS5B Pol would be a principal option for the treatment of HCV [102]. Meanwhile, scytalone dehydratase and negative transcriptional regulator NmrA were suggested to be physiological targets of new fungicides and the subjects of inhibitor design and optimization [103105].

In this paper, we proposed a very wide range of the promising targets for the isoquinoline alkaloids of M. cordata. Most of the hits are not yet proven by pharmacological experiment.

Conclusion

Through in silicotarget fishing, the anticancer, anti-inflammatory, and analgesic effects of M. cordata were the most significant among many possible activities. The possible anticancer effects were mainly contributed by the isoquinoline alkaloids as active components.

Abbreviations

CHARMM: 

chemistry at Harvard Macromolecular Mechanics

MOE: 

molecular operating environment

RMSD: 

root mean square deviation

MMFF: 

Merck molecular force field

PDB: 

Protein Data Bank

KEGG: 

Kyoto Encyclopedia of genes and genomes

TTD: 

Therapeutic Target Database

HTML: 

hypertext markup language

Declarations

Authors’ contributions

HBL, QFL, PGX and YP conceived and designed the study. HBL, QFL and PGX performed the experiments. HBL, QFL and YP wrote the manuscript. All authors read and approved the final manuscript.

Acknowledgements

The authors would like to thank Prof. Jun Xu (Sun Yat-sen University, China) and Prof. Yanze Liu (IMPLAD, China) for assistance in preparing the manuscript and Dr. Rong Zhao (National Yang-Ming University, Taiwan) for assistance in analyzing the pathway. This work was supported by National Natural Science Foundation of China (Grant No. 81072995), and Peking Union Medical College Youth Fund (Grant No. 3332013079).

Competing interests

The authors declare that they have no competing interests.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences

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