Systems pharmacology and molecular docking strategies prioritize natural molecules as cardioprotective agents

Abstract

Natural compounds can interact with multiple cellular target proteins and may be prioritized as drug leads. There is a need for prioritization of compounds that protect cardiovascular systems from pathological conditions. Here we prioritize morin, veratric acid, piperine, syringic acid, vanillic acid, diosgenin, diosmetin and sinapic acid that were already identified as cardioprotective molecules in our previous studies through multi-level data integration. In this study, initially we predict targets of the above-mentioned compounds by reverse pharmacophore (PharmMapper) and structural similarity based target-screening methods. We also explored the compound–target pathways (Biocarta and KEGG) and disease relationships. Further, we chose public microarray transcriptomic data from GEO to prioritize important pathogenic targets (heart failure, cardiac hypertrophy, vascular dysfunction and atherosclerosis), and we explored the interaction potential of the above compounds on the targets via blind docking (AutoDock Vina). Moreover, the multi target action of compounds was revealed by target information retrieved from large-scale text mining and organized databases (HIT and TCMID). The drug likeness profile and toxicity prediction was achieved based on Lipinski's rule and structural similarity search (ProTox). The observed results have demonstrated that the multi target potential and less toxic nature mean these molecules can be prioritized as lead compounds for cardiovascular diseases.

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1. Introduction

Cardiovascular diseases (CVD) are the leading causes of death and disability worldwide.¹ CVD including hypertension, atherosclerosis and cardiac hypertrophy are complicated and interlinked in pathogenic progression.² As a complex disease, CVD is the consequence of multiple pathogenic factors and reflects the altered interactions of many interconnected genes.³ Consequently, most drugs for CVD were designed for a specific target and cannot be very effective.⁴ Lu et al. also indicates that, as CVD is complicated, multi-targeted therapies is a better pathway to achieve the desired treatment.⁵ Recently, Wang et al. suggested that some commonly prescribed cardiovascular drugs might exert unintended effects.⁶

Systems pharmacology provides powerful new tools and approaches for natural product lead discovery. Recently, with this approach Li et al. revealed a multiple drug–target prediction and validation, and network pharmacology techniques, to shed new lights on the effectiveness and mechanism of Compound Danshen Formula.⁷ Formerly, Yu et al. explored the mifepristone target pathway, which is a good example to identify chemotherapeutic potential seamlessly from systems pharmacology.⁸ Zhang et al. indicates the potential applications of multi-level and multi-targeting therapies in cancer treatment.⁹

Natural products have been considered as an important source of lead compounds for drug discovery and more than 50 percent of FDA-approved drugs were natural products or natural product derivatives.¹⁰ Shukla et al. suggests that supplementation of cardiovascular responsive natural products needs to be considered in all populations who have high prevalence of CVD.¹¹ With the development of large-scale network analysis, researchers have recently begun to explore the action mechanism of bioactive compounds in the context of biological networks, e.g. drug–target network.¹⁰ Zheng et al. put forth novel strategies of systems pharmacology for multi-target drug discovery from natural products for CVD based on network pharmacology methods.¹²

In this study, we aimed to prioritize natural molecules, morin, veratric acid, piperine, syringic acid, vanillic acid, diosgenin, diosmetin and sinapic acid, which was explored as cardiovascular protective and antihypertensive based on our previous laboratory studies. In our laboratory, we previously explored the following results; morin attenuates hypertension and oxidative stress in deoxycorticosterone acetate-salt (DOCA-salt) induced hypertensive rats. Further, it was shown to be a cardiovascular protective molecule with biochemical and histopathological evidences.^13,14 Another study with similar hypertensive rat model illustrates that diosmin treatment dose-dependently prevents hypertension and cardiovascular system from oxidative stress.¹⁵ Consequently, our experiment also proves that, diosmin pretreatment improves cardiac function after ischemia/reperfusion.¹⁶ With L-NAME model of pharmacological hypertension we explored the preventive effect of veratric acid,^17,18 piperine,¹⁹ syringic acid,²⁰ vanillic acid,²¹ and sinapic acid²² along with their antioxidant potential. Further, we indicated that sinapic acid prevents ischemia/reperfusion injury and coronary dysfunction in rat heart and attenuates oxidative stress dependent H9c2 cardiomyoblast cell injury.²³ Apart from this, we demonstrated that diosgenin, a steroidal saponin prevents cardiovascular remodeling with its antioxidant efficacy.^24,25

Based on our previous results, we hypothesize that, along with antioxidant potential these molecules may exhibit its cardioprotective effect, due to their interaction with number of pathological pathways. Consequently, we propose this study to explore the compound targets-pathway related to cardiovascular dysfunction of above mentioned compounds via multi way systems pharmacological approaches such as (1) target screening via pharmacophore mapping approach and pathway analysis; (2) transcriptomic (microarray) reanalysis based target prioritization and docking; (3) curated natural compound database mining; (4) drug likeness and toxicity prediction to prioritize them as cardiovascular protective lead molecules.

2. Materials and methods

2.1 Compounds and structures

Compound details including PubChem ID and structure along with pharmacological properties observed from previous studies were illustrated in Table 1. In this study, we have chosen diosmetin structure for in silico analysis instead of diosmin, since diosmin is hydrolyzed by enzymes of intestinal micro flora before absorption of its aglycone diosmetin.²⁶ All the structural information for this study were obtained from PubChem database. Briefly, 3D SDF file of compounds were downloaded from PubChem database²⁷ and converted into Mol2 or PDB format in Open Babel software.²⁸

Table 1 Compound structure and properties information

S. no.	PubChem ID	Name/molecular weight	Structure	Properties			Model of previous studies
				Anti hypertensive action	Cardiovascular protective and antioxidant effects	Hepatic and renal toxicity
1	5281670	Morin (C15H10O7)/302.23		Yes	Yes	No	DOCA-salt
2	638024	Piperine (C17H19NO3)/285.33		Yes	Yes	No	L-NAME
3	7121	Veratric acid (C9H10O4)/182.17		Yes	Yes	No	L-NAME
4	10742	Syringic acid (C9H10O5)/198.17		Yes	Yes	No	L-NAME
5	8468	Vanillic acid (C8H8O4)/168.14		Yes	Yes	No	L-NAME
6	99474	Diosgenin (C27H42O3)/414.62		Yes	Yes	No	Adenine-CRF
7	5281612	Diosmetin (C16H12O6)/300.26		Yes	Yes	No	DOCA-salt
8	637775	Sinapic acid (C11H12O5)/224.20		Yes	Yes	No	L-NAME

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2.2 PharmMapper target prediction and pathway enrichment analysis

The targets of study molecules were searched by PharmMapper Server (http://59.78.96.61/pharmmapper),²⁹ the above method is designed to identify potential target candidates for the given small molecules (drugs, natural products, or other newly discovered compounds with unidentified targets) via a ‘reverse pharmacophore’ mapping approach and it contains over 7000 receptor-based pharmacophore models. In this work, the number of the reserved matched targets was defined as 300 with only limited to the human targets (2214). This protocol was already successful in finding the major targets of tamoxifen among the top 100 pharmacophore candidates. Therefore, in this study we did not re-validate the compound–target associations with specific docking approaches.

Based on the previous studies,^30,31 in this study we hypothesized that, one compound may interacts with multiple components of various cellular pathways and disease related pathways as many natural compounds had such as curcumin.³² Here, we have submitted the Mol2 converted compound file to PharmMapper and chosen top 100 (considered as potential targets) among top 300. Their associated pathways were dissected by DAVID (Database for Annotation, Visualization and Integrated Discovery) Bioinformatics online tools (http://www.david.abcc.ncifcrf.gov/).³³ Cellular pathway association were obtained according to the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://www.genome.jp/kegg/) and Biocarta (http://www.biocarta.com/genes/index.asp)³⁴ and the annotations of target–disease relationship terms were obtained from annotations of Genetic Association Database (genetic_association_db_disease).³⁵ We selected the significant terms (P < 0.05) for further explanations about the targeting pathways.

Moreover, in order to explore the possible targets of compounds via structure likeness, in this study we utilizes two potential web servers that uses structural similarity scores to find the possible targets. First, we used SwissTargetPrediction (http://www.swisstargetprediction.ch)³⁶ web server to predict the targets of bioactive molecules based on a combination of 2D and 3D similarity measures with known ligands. Second, we utilized SuperPred (http://prediction.charite.de)³⁷ web server, connects chemical similarity (2D structure) of drug-like compounds with molecular targets.

2.3 Microarray data analysis for target prioritization and molecular docking

Prediction of interaction/inhibitory potential of compounds with valid or potential targets are a key step toward unraveling the pharmacological efficacy. In this study, microarray data reanalysis was performed to explore the potential target components of important pathways involved in cardiovascular remodeling events apart from conventional targets. Here, we focused on targets of cardiac hypertrophy along with hypertension and atherosclerosis. Microarray datasets were retrieved from the publically available experimental microarray datasets in GEO database (http://www.ncbi.nlm.nih.gov/geo/) and differentially expressed genes were screened with the interactive web tool GEO2R (http://www.ncbi.nlm.nih.gov/geo/geo2r). GEO2R performs comparisons on original submitter-supplied processed data tables using the GEOquery and limma R packages from the Bioconductor project (http://www.bioconductor.org). The Benjamini and Hochberg false discovery rate method was selected by default to adjust P-values for multiple testing.³⁸ In this study, we selected the top 250 differentially expressed genes and submit the upregulated genes among the above to GeneCodis3 software (http://genecodis.cnb.csic.es) for pathway enrichment analysis with default settings.³⁹ First, a microarray study of accession number GSE8051 (ref. 40) was conducted for discovering the gene expression in resistance artery of hypertension rat models and we reanalyzed the differential expression between male Wistar-Kyoto (WKY) and age-matched spontaneously hypertensive rats. Second, in order to prioritize targets from pathological hypertrophy we had chosen GEO dataset GSE19210 (ref. 41) and reanalyzed the gene expression changes of left ventricular remodeling associated with transition to systolic heart failure (HF) in the spontaneously hypertensive rat (SHR). Furthermore, we aimed to prioritize targets related to atherosclerosis, consequently, we reanalyzed a microarray data (GSE19286)⁴² from ApoE-deficient mice with atherosclerosis relative to nontransgenic control mice. Since, angiotensin II (Ang II) mediated signaling plays a key role in the development of hypertension associated cardiac remodeling we also select GSE59437 datasets⁴³ and reanalyzed the differential expression between control heart and day 7 of Ang II treated heart. Table 2 shows the important microarray prioritized pathways and targets for virtual docking.

Table 2 Microarray based prioritized pathways and targets for molecular docking^a

Microarray study and GEO accession	Focused modulated biological processes and pathways	Important known targets of the pathway
a JNK, c-Jun N-terminal kinases; ACE, angiotensin converting enzyme; REN, renin; ROCK, Rho-associated kinase; ECE1, endothelin converting enzyme 1; GSK-3β, glycogen synthase kinase-3β; TGF-β, transforming growth factor-β; NF-κB, nuclear factor-κB; ERK, extracellular signal-regulated kinase; JAK2, Janus kinase 2; COX2, cyclooxygenase-2.
Vascular dysfunction in SHR (GSE8051)	Vasoconstriction (BP); JNK cascade (BP); vasodilation (BP)	ACE (PDB 1O8A), REN (PDB 2V0Z), ROCK (PDB 2ESM), ECE1 (PDB 3DWB),
Atherogenesis in ApoE deficient mice (GSE19286)	Canonical Wnt receptor signaling pathway (BP); Wnt receptor signaling pathway (BP)	GSK-3β (PDB 1UV5)
Heart failure in SHR (GSE19210)	TGF-β signaling pathway; positive regulation of I-κB kinase/NF-κB cascade (BP)	TGF-βR1 (PDB 1PY5), NF-κB (PDB 1SVC)
Ang II treated heart (GSE59437)	Inflammatory process (BP); positive regulation of ERK1 and ERK2 cascade (BP)	JAK2 (PDB 3RVG), ERK2 (PDB 1TVO), COX2 (PDB 6COX)

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To explore the putative binding site of compounds on prioritized target structure, we utilized a blind docking approach via AutoDock Vina in PyRx0.8 which is an easy-to-use user interface that significantly improves the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading.⁴⁴ Recently, a molecular docking study utilizing the above tool explores the potential target of Tanshinone IIA for acute promyelocytic leukemia.³⁰ In this study, the grid box was set large enough to cover the whole protein (targets) for blind docking similar to a previous study that explore HIV protease inhibitors.^45–47

Additionally, Ligplot plus version 1.4 (http://www.ebi.ac.uk/thornton-srv/software/LigPlus)⁴⁸ was used for the 2D visualization of ligand–receptor interaction. The Catalytic Site Atlas (CSA) (http://www.ebi.ac.uk/thornton-srv/databases/CSA)⁴⁹ was used to retrieve the information of enzyme catalytic site residues in order to explore the possible orthosteric (binding at the active site) and allosteric (binding elsewhere) binding mode of ligands with receptor.

2.4 Target fishing by database analysis (evidence based text mining) and disease relationships

To identify the molecular targets of compounds, we utilized database information retrieval method. To be specific, first, information of target proteins was retrieved from Herbal Ingredients' Targets Database (HIT, http://lifecenter.sgst.cn/hit/).⁵⁰ A previous study indicates that HIT database mining potentiates systems pharmacology dissection of mechanisms of action for herbal medicines in stroke treatment and prevention.⁵¹ In addition, we utilized Traditional Chinese Medicine Integrated Database (TCMID, http://www.megabionet.org/tcmid/) to explore the potential targets and mechanism of compounds. TCMID is an integrative database that contains data of herbal ingredients, herbal targets, disease-related gene or proteins, drugs and their targets, many of which were collected through text mining, which is the largest data set for related field.⁵² In the current study, target information of diosmetin, syringic acid and veratric acid was not available in HIT but it is available in TCMID (not veratric acid). It was already indicated that, data obtained from TCMID could be effectively applied to complement the results of high throughput experiments.⁵³ In continuation of the above, we further intend to dissect the compound disease relationships via utilizing the Traditional Chinese Medicine Systems Pharmacology database and analysis platform (TCMSP) (http://sm.nwsuaf.edu.cn/lsp/tcmsp.php), built based on the framework of systems pharmacology for herbal medicines.⁵⁴ In this study, we searched all the above databases by the names of the compounds. All compound–target interactions from these databases were previously recognized and supported by published literatures.

2.5 Drug likeness and toxicity prediction

In this section, we predict the drug likeness of compounds via Lipinski rule of 5 using Molinspiration server (http://www.molinspiration.com/cgi-bin/properties).⁵⁵ The following criteria of molecular weight less than 500, compound's lipophilicity known as log P is less than 5, molecule that can donate hydrogen atoms to hydrogen bonds is less than 5 and groups that can accept hydrogen atoms to form hydrogen bonds is less than 10 was considered.⁵⁶ Along with above, the maximum number of rotatable bonds was set as 7. Furthermore, for in silico determination of the possible toxic effects of compounds we applied ProTox (http://tox.charite.de/tox),⁵⁷ a web server for the prediction of rodent oral toxicity. The prediction method is based on the analysis of the chemical similarities between compounds with known toxic effects (with known median lethal doses – LD50) and incorporates the identification of toxic fragments.

3. Results

3.1 Target prediction and pathways

We suggest that potential high-scored targets (top 100) may contribute or enriched in specific cellular pathways. In this study, we selected the top 100 protein targets (including repeats) from the top 300 high-ranking proteins for each compound. All the selected targets were further subjected to DAVID database for GO analysis with Biocarta and KEGG, which illustrates that, targets of all the study compounds have significantly (P < 0.05) enriched with some cellular pathways. Among that, cardiac hypertrophy, renin angiotensin, cell cycle, VEGF, nitric oxide (NO) signalling, focal adhesion and PPAR pathways were pronounced. Table 3 illustrates the important compound targeted pathways and the details of target components were provided in ESI File 1.† While focusing on targets disease relationship, we found significantly enriched (P < 0.05) terms related to cardiovascular diseases (ESI Table 1†).

Table 3 Compound targeted cardiovascular pathways^a

S. no.	Compound	Biocarta	KEGG pathway
a NFAT, nuclear factor of activated T cells; VEGF, vascular endothelial growth factor; IL-2, interleukin-2; FXR, farnesoid X receptor; LXR, liver X receptor; PPAR, peroxisome proliferator-activated receptor; NOD, nucleotide-binding oligomerization domain.
1	Morin	Cell cycle; NFAT and hypertrophy of the heart (transcription in the broken heart)	Cell cycle; VEGF signaling pathway
2	Piperine	NFAT and hypertrophy of the heart (transcription in the broken heart); insulin signaling pathway; IL-2 signaling pathway	Focal adhesion; VEGF signaling pathway; aldosterone-regulated sodium reabsorption
3	Veratric acid	NFAT and hypertrophy of the heart (transcription in the broken heart); apoptotic signaling in response to DNA damage	VEGF signaling pathway; renin–angiotensin system
4	Syringic acid	—	VEGF signaling pathway
5	Vanillic acid	Ras signaling pathway; Ang II mediated activation of JNK pathway via Pyk2 dependent signaling	VEGF signaling pathway
6	Diosgenin	FXR and LXR regulation of cholesterol metabolism; NFAT and hypertrophy of the heart (transcription in the broken heart)	PPAR signaling pathway; aldosterone-regulated sodium reabsorption
7	Diosmetin	Cell cycle; NFAT and hypertrophy of the heart (transcription in the broken heart)	NOD-like receptor signaling pathway; cell cycle
8	Sinapic acid	Ang II mediated activation of JNK pathway via Pyk2 dependent signaling; actions of nitric oxide in the heart; ras signaling pathway	Focal adhesion; type II diabetes mellitus

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3.2 Structural similarity based target prediction

The SwissTargetPrediction and SuperPred based structural similarity analysis revealed the possible targets based on structural similarity of known ligands–target integrations. In this study, the important cardiovascular targets were prioritized from the prediction. Both web servers had predicted overlapping targets. Table 4 indicates the major cardiovascular targets predicted by SwissTargetPrediction method.

Table 4 Target prediction based on structural similarity (SwissTargetPrediction)

Compound	Target	Similar compounds (2D/3D)	Target class
Morin	Estrogen receptor (by homology)	65/32	Transcription
	Estrogen receptor β	65/32	Transcription
	Solute carrier family 22 member 12	1/1	Unclassified
	Aldose reductase (by homology)	25/68	Enzyme
	Arachidonate 5-lipoxygenase	13/52	Enzyme
Piperine	Amine oxidase [flavin-containing] A (by homology)	74/8	Enzyme
	Amine oxidase [flavin-containing] B	76/9	Enzyme
	Histone deacetylase 1	47/4	Enzyme
	Histone deacetylase 2 (by homology)	47/4	Enzyme
	Histone deacetylase 3	45/4	Enzyme
	D(2) dopamine receptor	41/21	Membrane receptor
	D(3) dopamine receptor	26/19	Membrane receptor
Veratric acid	Carbonic anhydrase-1,2,4,9,12,(3,5A,5B,13-by homology)	17/20; 17/20; 3/4; 10/9; 10/10; 17/20; 17/20; 17/20; 17/20	Enzyme
	Tyrosine-protein kinase Lck	3/1	Tyr kinase
	Tyrosine-protein kinase Fyn	2/2	Tyr kinase
	Proto-oncogene tyrosine-protein kinase Src (by homology)	2/2	Tyr kinase
Syringic acid	Carbonic anhydrase-1,2,3,4,5A,5B,6,7,9,12,(13-by homology)	5/20; 5/20; 5/20; 1/4; 5/20; 5/20; 2/6; 5/20; 3/9; 3/10; 5/20	Enzyme
	Thiopurine S-methyltransferase	1/4	Enzyme
Vanillic acid	Carbonic anhydrase-1,2,3,4,5A,5B,6,9,12,14,(3,7,13-by homology)	17/23; 17/23; 6/4; 17/23; 17/23; 6/6; 9/9; 8/10; 8/10; 17/23; 17/23; 17/23	Enzyme
	Thiopurine S-methyltransferase	4/4	Enzyme
Diosgenin	Oxysterols receptor LXR-β	12/18	Transcription factor
	Oxysterols receptor LXR-α	12/18	Transcription factor
	3-Hydroxy-3-methylglutarylcoenzyme A reductase	100/74	Enzyme
Diosmetin	Aldose reductase (by homology)	24/70	Enzyme
	NADPH oxidase 4	8/8	Enzyme
	Xanthine dehydrogenase/oxidase	2/14	Enzyme
Sinapic acid	Carbonic anhydrase-1,2,5A,6,7,9,12,(3,5B,13-by homology)	29/11; 29/11; 29/11; 2/7; 29/11; 7/10; 5/10; 29/11; 29/11; 29/11	Enzyme
	Epidermal growth factor receptor	5/16	Tyr kinase
	Receptor tyrosine-protein kinase erbB-2	5/16	Tyr kinase
	Receptor tyrosine-protein kinase erbB-3 (by homology)	5/16	Tyr kinase

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3.3 Prioritized targets and virtual docking

The current results have shown that, compounds have strong interaction potential with targets. In this study, public microarray data (from GEO) reanalysis of major cardiovascular events (not an in-depth analysis as the source study) with GEO2R permit us to prioritize few major pathways that may lead to pathological situation. Pathway enrichment analysis of up regulated genes was done. Based on the previous literatures we had chosen few important significantly (P < 0.05) enriched pathways and its central components (proteins/receptors) (details in discussion). Among that, notably, pathways involved in vasoconstriction [angiotensin converting enzyme (ACE), renin, Rho associated kinase], and inflammation (cyclooxygenase-2), Wnt signalling (GSK-3β), transforming growth factor-β (TGF-β) and NF-κB signaling pathways were considered as major targets of the current study. In this study, the observed results from blind docking has reported multiple conformations and associated binding energies. From the results, the lowest energy conformation was selected. The current study has shown that diosgenin binds with lowest energy value (highest affinity) with target when compared with other chosen compounds. Table 5 demonstrates the docking energy and Fig. 1 illustrates binding conformation of selective compounds.

Table 5 Compounds and target interaction (docking energy)^a

PDB ID	Morin	Piperine	Veratric acid	Syringic acid	Vanillic acid	Diosgenin	Diosmetin	Sinapic acid
a Docking score/interaction potential of compounds with targets (kcal mol^−1).
1O8A	−8.6	−9.0	−6.3	−6.0	−6.3	−11.8	−8.4	−6.3
1PY5	−9.8	−8.7	−6.2	−6.1	−6.1	−11.0	−10.1	−7.0
1SVC	−6.4	−6.5	−5.0	−4.8	−4.7	−8.1	−6.5	−5.1
1TVO	−8.4	−7.2	−5.4	−5.5	−5.6	−10.1	−8.1	−5.7
1UV5	−8.9	−8.6	−6.2	−5.8	−5.9	−9.3	−9.1	−6.7
2ESM	−7.8	−7.7	−5.8	−5.4	−5.7	−8.9	−7.7	−5.9
2V0Z	−8.0	−7.8	−5.1	−5.2	−5.8	−10.6	−7.7	−6.1
3DWB	−8.1	−8.5	−6.2	−6.5	−6.3	−9.9	−8.4	−6.9
3RVG	−8.3	−7.9	−5.8	−5.8	−6.0	−9.3	−8.6	−6.4
6COX	−8.8	−8.6	−6.2	−6.0	−6.3	−10.2	−9.1	−6.7

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Fig. 1 Binding conformation of selective compounds.

The surrounding amino acid residues of conformations that shown highest interaction potential (ligand–receptor complex) were illustrated along with the catalytic residues of the enzymes in ESI Table 2.† While focusing on the inhibitory relationship at the enzyme level, we found that majority of the ligand–receptor interactions were allosteric and only few of them were shown to be directly contacted (possible hydrogen bonds) with receptor catalytic residues. From the catalytic site atlas we could directly derived the information of enzyme catalytic residues only for ACE and others were predicted by homology. From the results we observed that, diosmetin and morin have shown possible hydrogen bonding contact with His353 and His513 residues of ACE. Vanillic acid and veratric acid were shown to be interacted near to the cyclooxygenase active site residues (Tyr385, Gln203 and His207). Further morin, piperine and sinapic acid were shown hydrogen bond interaction with Arg738, His732, Glu608 and Asp671 residues of ECE1. Furthermore, piperine conformation was shown to be associated with the renin active site environment (Asp215). Diosmetin shown hydrogen bond contact with Asp202 residue of ROCK. Diosgenin and piperine shown to be interacted near the catalytic residue Gln185 of Gsk-3β. In the case of TGFβR1, diosmetin showing possible hydrogen bonding contact with Lys337. Diosgenin binds near to the environment of catalytic residues Lys151 and Ser153 of ERK2.

3.4 Database mediated compound target/activity prioritization

To prioritize a compound's pharmacological target/activity the current study has substituted a massive time consuming literature based review process by direct mining of the database. In this case, the compound targets and relevant pharmacological information were obtained via database mining of three important databases HIT, TCMID and TCMSP and the results have shown that many of our study compounds can be prioritized as cardiovascular protective candidates. Along with the in-depth text mining from experimental, interaction database (STRING), also strongly support us to prioritize the above concept. Current results from TCMID and HIT database notably indicates that, morin may target components involved in apoptosis, oxidative stress, vasoconstriction and inflammatory events, and diosgenin targets oxidative stress and lipid metabolism events. Table 6 illustrates the important targets of compounds obtained based on text mining and STITCH relationships from TCMID database. Finally, the compound–target–disease relationship obtained from TCMSP database also prioritizes these compounds as cardiovascular protective agents (ESI Table 3†).

Table 6 Target interaction and pharmacological information (TCMID database)

Compound	Target	Identification method
Morin	Apoptosis regulator BAX	STITCH
	Endothelin-1	Text mining
	Xanthine dehydrogenase/oxidase	Text mining
	Arachidonate 5-lipoxygenase	Text mining
Piperine	Tumor necrosis factor	STITCH, text mining
	Inhibitor of NF-κB kinase subunit α	Text mining
	Inhibitor of NF-κB kinase subunit α	Text mining
	Intercellular adhesion molecule 1	Text mining
	Interleukin-6	Text mining
Veratric acid	Not-available
Syringic acid	Prostaglandin G/H synthase 2	STITCH
	C–C motif chemokine 16	STITCH
Vanillic acid	Nitric oxide synthase, endothelial	Text mining
	Prostaglandin G/H synthase 2	STITCH
Diosgenin	Superoxide dismutase [Cu–Zn]	Text mining
	Catalase	Text mining
	Vascular endothelial growth factor A	Text mining
	Fatty acid synthase	Text mining
	Lipoprotein lipase	STITCH
Diosmetin	Prostaglandin G/H synthase 2	STITCH
	Tumor necrosis factor	STITCH
Sinapic acid	Choline O-acetyltransferase	Text mining

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3.5 Drug likeness and predicted toxicity of compounds

The current results indicate that all the study compounds roughly pass the Lipinski's rule of 5. The parameters obtained were illustrated in Table 7. The toxicity prediction (class 1 to 6) method indicates our compounds have less oral toxicity even at higher doses (Table 8).

Table 7 Drug likeness of compounds

Compound	Morin	Piperine	Veratric acid	Syringic acid	Vanillic acid	Diosgenin	Diosmetin	Sinapic acid
milog P	1.881	3.332	1.495	1.204	1.187	5.932	2.282	1.265
TPSA	131.351	38.777	55.767	75.995	66.761	38.696	100.129	75.995
n atoms	22	21	13	14	12	30	22	16
MW	302.238	285.343	182.175	198.174	168.148	414.63	300.266	224.212
nON	7	4	4	5	4	3	6	5
nOHNH	5	0	1	2	2	1	3	2
n violations	0	0	0	0	0	1	0	0
n rotb	1	3	3	3	2	0	2	4
MV	240.084	267.741	162.136	170.154	144.608	419.383	249.594	197.571

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Table 8 Predicted oral toxicity of compounds

S. no.	Compound	LD50 (mg kg^−1)	Toxicity class
1	Morin	3919	5 – may be harmful if swallowed (2000 < LD50 ≤ 5000 mg kg^−1)
2	Piperine	330	4 – harmful if swallowed (300 < LD50 ≤ 2000 mg kg^−1)
3	Veratric acid	2000	4 – harmful if swallowed (300 < LD50 ≤ 2000 mg kg^−1)
4	Syringic acid	1700	4 – harmful if swallowed (300 < LD50 ≤ 2000 mg kg^−1)
5	Vanillic acid	2000	4 – harmful if swallowed (300 < LD50 ≤ 2000 mg kg^−1)
6	Diosgenin	8000	6 – non-toxic (LD50 > 5000 mg kg^−1)
7	Diosmetin	3919	5 – may be harmful if swallowed (2000 < LD50 ≤ 5000 mg kg^−1)
8	Sinapic acid	1772	4 – harmful if swallowed (300 < LD50 ≤ 2000 mg kg^−1)

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4. Discussion

In general, the action mechanism of natural medicine is described as ‘multiple ingredients and multiple targets’ which may differ from western drugs to a large extent.⁵⁰ The network-based computational approach for drug target identification is not restricted to the single target protein structure. A previous study illustrates that in silico prediction of comprehensive target profiles of TCM ingredients is the first step in TCM network pharmacology.⁵⁸ Another study, similar to our approach provides comprehensive and useful data for in-depth studies of mechanism of action of green tea and systematically illustrated the mechanism of the pleiotropic activity of green tea by analyzing the corresponding “drug–target-pathway-disease” interaction network.³¹ In the current study, the enriched pathway components predicted as targets of morin, piperine, veratric acid, diosgenin and diosmetin are involved in NFAT and hypertrophy pathway of the heart. The calcineurin–NFAT and mitogen-activated protein kinases (MAPK) signaling pathways are inter-dependent and together orchestrate the cardiac hypertrophic response.⁵⁹ Furthermore, recent results also suggest a direct interaction between NFAT and NF-κB that effectively integrates two disparate signaling pathways in promoting cardiac hypertrophy and ventricular remodeling.⁶⁰ Vanillic acid and sinapic acid targets components of At1rPathway (Ang II mediated activation of JNK pathway via Pyk2 dependent signaling). A pioneering study indicates the involvement of Pyk2 in Ang II-induced activation of JNK and c-Jun in cardiac fibroblasts and the relevancy of Ang II on cardiac fibrosis and hypertrophy.⁶¹ Other than the above, morin, piperine, veratric acid, syringic acid and vanillic acid target components of VEGF signaling pathway. Recent report have demonstrated that angiogenesis is an important factor in the progression of atherosclerosis and VEGF, a potent angiogenic growth factor, increases the rate of atherosclerosis in animal models and also suggested that the anti-atherosclerosis action of polyphenols might be through inhibition of VEGF signaling.⁶² Along with these, notably, sinapic acid and piperine targets focal adhesion pathway. Focal adhesion kinase pathway plays a critical role at the cellular level in response of cardiomyocytes and cardiac fibroblasts to biomechanical stress and to hypertrophic agonists and also in endothelin induced hypertrophy.^63,64

By means of target prediction via structural similarity (SwissTargetPrediction), we found that carbonic anhydrase (CA) isozymes might be the main target of our study compounds. Here veratric acid, vanillic acid, sinapic acid and syringic acid have shown more similarity with 2D/3D structure of ligands that targets CA. CA and its inhibitors are relevant to many physiological processes and diseases. An earlier study suggest that the vasodilator effect of thiazide diuretics results primarily from inhibition of vascular smooth muscle cell CA and inhibition of CA isoenzymes (I, II, III, IV, V, IX and XII), expressed in the cells of cardiovascular system may involved in vasodilation and other cardiovascular regulations.^65,66 Veratric acid may targets the Src family members Lck and Fyn, which are known to be involved in ox-LDL and H₂O₂-mediated activation of intracellular signaling events in vascular smooth muscle cells⁶⁷ and also in CD36 scavenger receptors mediated foam cell formation in atherosclerosis.⁶⁸ In the case of diosgenin, because of its structural relevancy it may be the inhibitor of 3-hydroxy-3-methyl-glutaryl-CoA (HMG-CoA) reductase and LXRs. Statins (inhibitor of HMG-CoA reductase) treatment is regarded as one of the most effective methods for the stabilization of vulnerable atherosclerotic plaques, and beneficial in patients with coronary heart disease.⁶⁹ In addition, LXRs are target of oxysterols and LXRs lie at the intersection of lipid metabolism, inflammation and pathways involved in progression of atherosclerotic lesions and CVD.⁷⁰ The next compound piperine may be the potential inhibitor of histone deacetylases (HDACs) 1, 2 and 3. A previous study indicates that class I HDACs promote pathological cardiac hypertrophy and among the class I HDACs, HDAC2 is activated by hypertrophic stresses.⁷¹ Along with the above, it also targets the monoamine oxidase A (MAOA) that regulates the metabolism of key neurotransmitters that has been associated with cardiovascular risk factors.⁷² While specifically focusing on morin, one of its important predicted targets is estrogen receptor beta (ERbeta). It was previously indicated that, 17-beta-estradiol (E2) acts mainly through ERbeta and alleviates the important signaling in Ang II induced cardiac hypertrophy and fibrosis in female mice.⁷³ Another important target of morin was arachidonate 5-lipoxygenase, which is related with the biosynthesis of lipid inflammatory mediators leukotrienes within the atherosclerotic lesion and inhibitors of 5-lipoxygenase pathway was now evaluated in clinical trials of patients with cardiovascular disease.⁷⁴ While focusing on diosmetin, prediction results have shown that NADPH oxidases and xanthine dehydrogenase/oxidase may be its targets. Current knowledge on redox signaling pathways indicates that above two enzymes plays vital role in the pathogenesis of cardiac hypertrophy.^75,76 In case of sinapic acid, the target may be the class of tyrosine kinases such as epidermal growth factor receptor (EGFR) and receptor tyrosine-protein kinase (erbB-2) and their signaling modulation contributes to the development of atherosclerosis, cardiac hypertrophy and hypertension.⁷⁷ Along with the above, SuperPred based structural similarity search identify some new targets and many were overlapped with the targets predicted by SwissTargetPrediction method. It indicates vanillic acid may target arachidonate 15-lipoxygenase and veratric acid may target phosphodiesterase-4. Previous literatures have shown that, macrophage 12/15-lipoxygenase plays a dominant role in the development of atherosclerosis by promoting endothelial inflammation and foam cell formation⁷⁸ and suggest that inhibition of phosphodiesterase-4 ameliorates hypertension-induced impairment of learning and memory functions.⁷⁹

In drug–target discovery, virtual screening method is essential and have been applied as a complement to experimental techniques to rapid screening and predicting the location of functional binding pockets of ligands on targets and the blind docking calculations mediates the prediction of the above on the entire protein surface.^80,81 In this study, docking receptors were prioritized components of important pathways deregulated (up regulated) during cardiovascular pathogenic progression obtained from reanalysis of microarray experiments. Similarly, an earlier study identified S100A8 as a prospective biomarker for kidney cancer from microarray-based transcriptomics experiments and docking analysis have showed that aspirin, celecoxib, dexamethasone and diclofenac binds to S100A8 and may inhibit downstream signaling in kidney cancer.⁸² Additionally, an earlier study proposed a possible mechanism of activation of acetylcholine binding protein via the “blind docking” of allosteric modulators.⁸³ In the identical way, in this study, we performed blind docking of ligands via AutoDock Vina on prioritized pathway targets. All the compounds have shown good affinity on microarray prioritized pathway targets, which indicates the plausible cardioprotective nature of the compounds. When we reanalyzed the microarray data of vascular dysfunction in SHR, among important/prioritized pathways, we chosen biological process terms (GO) vasoconstriction and vasodilation. It is well known that, vascular dysfunction is one of the major targets of hypertension and its associated complications. We plausibly chosen components of vascular contraction pathway such as ACE, renin, ROCK and ECE1 as prioritized targets based on previous literature supports.^84–86 ACE plays an important physiological role in regulation of blood pressure by converting Ang I to Ang II, a potent vasoconstrictor.^84,85 Consequently, the inhibition of ACE activity is a major target in the prevention of hypertension and marine-derived natural ACE inhibitors are proposed as novel therapeutic drug candidates to treat hypertension.⁸⁷ RhoA/ROCK, known regulators of vascular tone which belong to serine/threonine (Ser/Thr) protein kinase family, is reported to involve in the organization of the actin cytoskeleton, consequently in vascular regulation.⁸⁸ Supportively, a previous study indicates that, virtual screening approach can able to predict ROCK1 interacting compound that also prove its inhibitory activity in vivo.⁸⁹ In the view of endothelin converting enzyme, hyperactivation of the endothelin system has been implicated in the pathogenesis of various cardiovascular disorders including myocardial infarction, restenosis, hypertension, heart failure and chagas cardiopathy; and there is growing interest in blockade of endothelin formation.⁹⁰ From the microarray reanalysis of atherogenesis process, we prioritize an important central regulator (target) from Wnt signaling pathway (GSK-3β). Existence of a common mechanism of accelerated atherosclerosis involving endoplasmic reticulum stress signaling through activation of GSK-3β and valproate supplementation blocked GSK-3β activation and attenuated the development of atherosclerosis.^91,92 From the enriched pathways of heart failure microarray data, we mainly focused on TGF-β signaling pathway, consequently its component TGF-β type 1 receptor (TGF-βR1). In cellular systems, TGF-β signals via a classical pathway, binding to TGF-β type 2 receptor (TGF-βR2) to activate TGF-βR1 and subsequent signaling. In myocyte, TGF-β stimulation induces hypertrophy, whereas TGF-β1 deficiency restrict Ang II-mediated cardiac hypertrophy.^93–95 Next, we targeted the NF-κB signaling pathway that enriched for heart failure, because prolonged activation of NF-κB appears to be promotes heart failure by triggering chronic inflammation.⁹⁶ Additionally, from pathways significantly deregulated during Ang II induced hypertrophy, we prioritize components of inflammatory and mitogen activated kinase signaling. Here we had chosen JAK2, COX2 and ERK2 as targets, since their functional importance in the above pathways.^97,98 Previous literatures have shown that, JAK2 kinase plays an important role in left ventricular remodeling during pressure overload hypertrophy and suggests that HDAC2 might be a downstream effector of JAK2 to mediate cardiac hypertrophic response by pressure overload or Ang-II.^99,100 COX2 is an important mediator of inflammation, cardiac hypertrophy and failure, further in cardiomyocytes, it involves in Ang II-induced oxidative stress and also contributes inflammatory cardiovascular changes.^101–103 Since the above mentioned targets are important mediators of major cardiovascular pathogenic events, we postulate the study compounds as cardiovascular protective agents because of their predicted interaction with the targets (docking affinity).

Along with the above, we did computational prioritization of compounds via database mining, which is a recently developed attractive approach for in silico drug discovery. The advantage of use of already curated database over traditional literature mining is reduced time consuming and we can obtain molecular target information including direct or indirect activation and up/down regulated genes under the treatment of individual ingredients.^50–52 Similar to the current study, a previous bioinformatics analysis explored the antirheumatic effects of Huang-Lian-Jie-Du-Tang via ingredients target mining in HIT database with a network perspective.¹⁰⁴ In this study, we have focused on HIT and TCMID database search and it explored the possible cardiovascular effects of study compounds in a detailed way. In case of morin, notably, it was shown to modulate endothelin-1 and xanthine oxidase. The source study has shown that, morin has an anti-hypertensive effect in high fat-induced hypertensive rats and it suppressed mRNA expression of endothelin-1 in the thoracic aorta.¹⁰⁵ Another study demonstrated it as an inhibitor of xanthine oxidase¹⁰⁶ and lipoxygenase (LOX).¹⁰⁷ The LOX metabolites from arachidonic acid and linoleic acid have been implicated in atherosclerosis, inflammation and its inhibition decreases neointimal formation following vascular injury.¹⁰⁸ Targets of diosgenin shows that it is associated with superoxide dismutase (SOD), catalase (CAT) and the results of the source study suggests that diosgenin could be a very useful compound to control hypercholesterolemia by both improving the lipid profile and modulating oxidative stress.¹⁰⁹ Consistently, a previous analysis also supports that it increases the activity of antioxidant enzymes network and prevent oxidative stress in diabetic animals.¹¹⁰ While focusing on vanillic acid, it was shown to be directly activating the vascular master regulator eNOS. From the database search, the source study postulates that vanillic acid enhances eNOS expression moderately.¹¹¹ Furthermore, vanillic acid also have shown to be an effective inhibitor of the activity of catalase oxidase, which is involved in oxidative stress mediated carcinogenic initiation.¹¹² When focusing on diosmetin, the STITCH compound–target interaction has shown that, it may targets COX2 and tumor necrosis factor-α (TNF-α). While focusing on syringic acid in the similar way it shows that, it may also targets COX2, thus both of it may acts as anti-inflammatory molecules.¹¹³ When focusing on piperine, the results have shown that it interacts with TNF-α and NF-κB, the source study have shown that, treatment with piperine reduced the level of nitrite in the lipopolysaccharides (LPS) stimulated BALB/c mice via inhibition of TNF-α production.¹¹⁴ Also it blocks the phosphorylation and degradation of IκBα by attenuating TNF-α induced IκB kinase activity and expression of intercellular adhesion molecule (ICAM),^115,116 which in turn suggests piperine or its structural analogues could be used for the development of new anti-inflammatory molecules. Along with the above, the cardiovascular protective role of piperine extends since it target on interleukin 6 (IL-6), in which piperine treatment significantly reduced the IL-6 production in B16F-10 cells.¹¹⁷ Collectively, hence, the targets such as COX2, TNF-α, eNOS, SOD, CAT, LOX and endothelin-1 are major regulators of cardiovascular pathogenesis and these compounds may be utilized for cardioprotective measures.

Because of toxicity, drug candidates entering clinical trials have only 8% chance of becoming marketed drugs and about 20% of failures in the late drug development.^118,119 Animal trials are currently the major method for determining the possible toxic effects of drug candidates. In silico toxicity prediction methods represent an alternative approach to experimental and nowadays, the interest has shifted to the above.^57,120 The oral toxicity prediction results obtained in this study illustrates that, the study compounds may have very less or no toxicity. The LD50 value of them was very high when compared with the already indicated pharmacological dose in animal models. Along with this, drug-like properties assessed by preliminary screening of compounds with the Lipinski's rule of 5 demonstrated drug likeness of study compounds and a recent study also indicated via the above rule to use natural products as a drug-like molecule resource for drug development.⁴ As a result, we can propose that, the current study compounds can be used as drug leads for further preclinical manipulations.

The target disease relationship study unveiled that, the target predicted from genetic association of disease, revealed an over-representation of cardiovascular diseases and ontology terms related to coronary artery diseases and myocardial infarction. Furthermore, the target disease relationship from text mining database TCMSP also supported the above.

5. Conclusion

Altogether, this pioneering study merged the conventional docking and structure based approaches to massive target prediction via database searching and database information retrieval methods to prioritize the study compounds along with drug toxicity assessment criterions. Over all, one limitation of the present study is, this study does not specifically focus a particular target of compounds and the docking approach not directed in depth towards receptor active site. In conclusion, the current study prioritizes the study compounds as cardiovascular protective molecules in multiple ways. This in silico prediction study of drug–target interactions from heterogeneous biological data may advance our system-level search for new cardiovascular drug molecules.

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Footnote

† Electronic supplementary information (ESI) available. See DOI: 10.1039/c5ra10761j

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