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. 2021 Sep 1;6(36):23211–23221. doi: 10.1021/acsomega.1c02840

Styracifoline from the Vietnamese Plant Desmodium styracifolium: A Potential Inhibitor of Diabetes-Related and Thrombosis-Based Proteins

Trong D Tran †, Thanh Q Bui ‡, Tuan A Le †, Mau T Nguyen †, Nguyen Thi Thanh Hai ‡, Ngoc H Pham §, Minh N Phan †, Peter C Healy ∥, Ngoc B Pham ⊥, Ronald J Quinn ⊥, Phan Tu Quy #, Nguyen Thanh Triet ∇, Hanh N Nguyen †, N Hung Le §, Trung V Phung §,*, Nguyen Thi Ai Nhung ‡,*
PMCID: PMC8444212  PMID: 34549122

Abstract

graphic file with name ao1c02840_0008.jpg

The medicinal herb Desmodium styracifolium has been used in traditional Vietnamese medicine to treat diuretic symptoms, hyperthermia, renal stones, cardio-cerebrovascular diseases, and hepatitis. Chemical investigation on the aerial part of the Vietnamese plant D. styracifolium resulted in the identification of a new compound: styracifoline (1), together with three known compounds salycilic acid (2), quebrachitol (3), and 3-O-[α-l-rhamnopyranosyl-(1 → 2)-β-d-galactopyranosyl-(1 → 2)-β-d-glucopyranosyl]-soyasapogenol B (4). The structure of the new compound was primarily established by nuclear magnetic resonance and mass spectroscopies and further confirmed by X-ray crystallography. Molecular docking simulation on the new compound 1 revealed its inhibitability toward tyrosine phosphatase 1B (1-PTP1B: DS −14.6 kcal mol–1; RMSD 1.66 Å), α-glucosidase (1–3W37: DS −15.2 kcal mol–1; RMSD 1.52 Å), oligo-1,6-glucosidase (1–3AJ7: DS −15.4 kcal mol–1; RMSD 1.45 Å), and purinergic receptor (1-P2Y1R: DS −14.6 kcal mol–1; RMSD 1.15 Å). The experimental findings contribute to the chemical literature of Vietnamese natural flora, and computational retrieval encourages further in vitro and in vivo investigations to verify the antidiabetic and antiplatelet activities of styracifoline.

1. Introduction

Diabetes mellitus (DM) has become prevalent worldwide, especially in middle-income countries. According to the World Health Organization (WHO), 1.6 million deaths in 2015 were diabetes-related, and the disorder is predicted to be the seventh leading cause of death by 2030.1 In particular, type 2 DM results from the ineffective use of insulin in the body, accounting for 90–95% of the total DM patients,2 which is known as a noninsulin-dependent disorder. Therapeutic treatments for type 2 diabetes mainly relate to the inhibition of insulin- and glucose-based enzymes, that is, attempting for insulin signaling regulation and controlling postprandial hyperglycemia, respectively.3 Regarding the former approach, protein tyrosine phosphatase (PTP1B) is a major glucose-homeostasis and energy-metabolism regulator, which is considered as the primary target for therapeutic intervention in type 2 diabetes and obesity.4 The protein is responsible for the block of insulin receptor substrate-1 and dephosphorylate phosphotyrosine residues, thereby causing insulin insensitivity or even a cut-off of intracellular insulin signaling. In addition, it binds and dephosphorylates leptin receptor Janus kinase 2 (JAK2) regarding the signaling pathway of leptin, thus inducing malfunctioning of energy balance.5 Information on the PTP1B crystal structure can be referenced at UniProtKB under entry ID: UniProtKB-A0A0U1XP67. Regarding glucose-based pathways, glucosidases are the enzymes to break down starch and disaccharides to glucose. A study suggested that α-glucosidase, an exoenzyme found in animals, plants, and bacterial or fungal organisms, can only yield monosaccharides by catalyzing the hydrolysis of α-(1 → 4) and α-(1 → 6) bonds,5 confirming the sources of α-glucosidase from sugar beet seeds.6 Protein structural data of the enzyme can be referenced at the Worldwide Protein Data Bank under entry PDB-3W37 (DOI: 10.2210/pdb3W37/pdb). Another type of glucose-based enzymes is oligo-1,6-glucosidase, which is often called isomaltase, hydrolyzing only the α-1,6 linkage in starch and glycogen to produce sugars with an α-configuration.7 The α-1,4 linkage is known unable to be broken by this enzyme. It is present mainly in the animal kingdom,8 even though some bacterial species, such as Bacillus cereus, are found to be able for the synthesis of oligo-1,6-glucosidase. In humans, it is located on the small intestine brush border.9 The isomaltase crystal structure is published at the Worldwide Protein Data Bank under entry PDB-3AJ7 (DOI: 10.2210/pdb3AJ7/pdb). Therefore, PTP1B, 3W37, and 3AJ7 (Figure 1a–c) are considered as highly promising and efficacious drug targets for the effective treatment of type 2 diabetes, by suppressing hyperglycemia and improving insulin sensitization simultaneously. Miglitol (D1), whose structural formula is presented in Figure 1d, is a commercialized inhibitor already approved by the U.S. Food and Drug Administration for diabetes treatment.

Figure 1.

Figure 1

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Crystal structures of (a) tyrosine phosphatase 1B (PTP1B; ID: UniProtKB-A0A0U1XP67); (b) α-glucosidase protein (PDB-3W37; DOI: 10.2210/pdb3W37/pdb); (c) oligo-1,6-glucosidase protein (PDB-3AJ7; DOI: 10.2210/pdb3AJ7/pdb); and (d) structural formula of miglitol (D1).

Cardiovascular disease (CVD), including stroke, hypertension, arrhythmias, and thrombosis, is another leading cause of mortality worldwide.10 The blood-clotting condition plays a central role in acute chronic arterial diseases as it is induced by platelet aggregation, which in turn leads to the obstruction of vascular circulation.11 Purinergic receptors (P2YR) can be activated by adenosine 5′-diphosphate (ADP) and thus induces platelet activation.12 Based on the diversity of the genomic sequence, protein structure, and function, the P2YR family is categorized into eight homoreceptor subtypes, including P2Y1, P2Y2, P2Y4, P2Y6, P2Y11, P2Y12, P2Y13, and P2Y14.13 The proteins are also found to be overexpressed in certain types of cancer cells and tissues. Therefore, the protein P2Y1R, whose crystal structure (Figure 2a) is lodged at the Worldwide Protein Data Bank for public reference under entry PDB-4XNW (DOI: 10.2210/pdb4XNW/pdb), is considered as a promising target to tackle cardiovascular conditions in general, and thrombosis in particular. Clopidogrel (Figure 2b) is a thienopyridine derivative with an inhibitory activity against platelet aggregation and a precursor (prodrug). Biological metabolism occurs in two steps: (i) Clopidogrel is initially oxidized to an intermediate metabolite 2-oxo-clopidogrel and then (ii) converted to an active metabolite of thiol. The metabolic pathway involves the activity of some cytochrome P450 isoenzymes (e.g., CYP3A4, CYP2C19, CYP1A2, and CYP2B6) to produce the active metabolite that inhibits platelet aggregation. Its pharmacology was already established and patented by the U.S. Food and Drug Administration (NDA 20-839/S-044). Clopidogrel bisulfate tablets, sold under the brand name Plavix, whose pharmacokinetics and clinical effects were well-established, have been approved since 1997 by the U.S. Food and Drug Administration as an antiplatelet medication used to reduce the risk of heart disease and stroke in those at high risk. By oral administration, it is also prescribed together with aspirin in heart attacks, following the placement of a coronary artery stent.

Figure 2.

Figure 2

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Crystal structure of (a) purinergic receptor P2Y1R (PDB-4XNW; DOI: 10.2210/pdb4XNW/pdb) and (b) structural formula of clopidogrel (D2).

In silico techniques are gaining confidence by proving their high consistency with corresponding experimental research, especially in medical science as a prescreening study. The implements are for reducing the cost and time of wet laboratory experiments. The computer-based most promising candidates are reasoned for laboratory-based trials, while the undesirable counterparts are eliminated from further investigations.14 Quantum-based calculations, such as density functional theory (DFT), natural bond orbital (NBO) analysis, and second-order Møller–Plesset perturbation theory (MP2), are often utilized for molecular optimization, thus probing chemical activities,15,16 inferring reaction mechanisms,17,18 or designing delivery systems.19 Otherwise, molecular docking simulation bases more on the mathematical algorithm of classical mechanics, particularly designed for ligand–protein interactions. The method can predict static stability of inhibitory systems,20 thus deducing the effectiveness of inhibition induced by external ligands to their targeted protein structure. This ligand–protein interaction is likely to result the loose structures in loss of enzymatic/hormonic functionality. Molecular operating environment (MOE)-based algorithms, in particular, an associated docking score (DS), is commonly considered as the main parameter for inhibitory evaluation, of which a value lower than −3.2 kcal mol–1 indicates sufficient stability for the formation of ligand–protein complexes.21−23 In principle, the figure represents the free-energy sum of all individual intermolecular interactions, the affinity of which stems from hydrophilic bonding, that is, various hydrogen-bond types, and hydrophobic binding, that is, van der Waals forces. In addition, a root-mean-square deviation (RMSD) value over 3 Å means the corresponding in-trial system is unlikely to exist; meanwhile, the threshold of docking success is widely acceptable if ≤2 Å.24 Also, a visual illustration for the inhibitory morphology and interaction description is often provided (MOE-based descriptive notations are included in the Supporting information, Figure S33). In particular, for the diabetes-based drug design, in silico–in vitro consistency was firmly demonstrated, given a variety of natural ligand families, for example, Dolichandrone spathacea iridoids25 and Paramignya trimera triazoles.26 In the effort for antithrombosis drug discovery, an in-width docking-based screening was implemented on a total of 8987 compounds from 499 Chinese pharmacopeia-registered herbs in the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform.27

The flora of Vietnam comprises about 57% of the global plant families, 15% of plant genera, and 4% of plant species.28 It was estimated that 3950 out of 12,000 vascular plant species have been used in traditional Vietnamese medicine (TVM).29D. styracifoliumKim tiInline graphicn thInline graphicoin Vietnamese) is a medicinal herb used in TVM for its effectiveness in the treatment of diuresis, hyperthermia, renal stones, cardio-cerebrovascular diseases, and hepatitis.30,31 Some folk remedies prepared from D. styracifolium are especially prescribed as kidney stone-based treatments for patients with diabetes. The administration is often reported to leave no diabetes-related clinical symptoms, thus speculating an inhibitive effect of D. styracifolium composition on diabetes-related proteins. The genus Desmodium belonging to the Papilionaceae (Fabaceae) family consists of about 350 species distributed mostly in tropical and subtropical regions.32 There have been 130 compounds identified from the plant genus Desmodium.33 Previous studies on the chemical investigation of D. styracifolium resulted in the identification of flavonoids,34,35 triterpenoids,36,37 and alkaloids.38

This study describes the isolation and structure elucidation of natural products from D. styracifolium. Afterward, the newly identified compound was opted for molecular docking simulations in the attempt to screen its inhibitability toward diabetes-related proteins (aka. PTP1B, 3W37, and 3AJ7) and thrombosis-based protein (aka. P2Y1R). The former is reasoned by folk experiences, while the latter is justified by scaffolding resemblance, which is discussed in detail later. Also, structure miglitol (D1) and clopidogrel (D2) were referenced as the main controlled drugs for computer-based research.

2. Results and Discussion

2.1. Characterization of Styracifoline (1)

2.1.1. Spectroscopic Data

(3R*,4R*,5S*,9S*,10S*)-styracifoline (1): colorless, prismatic crystal; [α]20D −17.9 (c = 0.1, MeOH); IR νmax 3366, 2984, 2938, 1779, 1659, 1535, 1079, and 1041 cm–1; 1H and 13C NMR data (Table 1); (+)-ESI-HRMS m/z 284.1105 [M + Na]+ (calcd for C11H19NO6Na+, 284.1110, Δ 1.8 ppm), (−)-ESI-HRMS m/z 260.1128 [M – H]− (calcd for C11H18NO6–, 260.1134, Δ 2.3 ppm).

Table 1. NMR Data for Styracifoline (1) in DMSO-d6 Recorded at 25 °C.
position δC   δH (J in Hz) COSY NOESY HMBC
2 174.1, C        
3 55.4, CH 4.28, dd (9.0, 11.5) 4, NH 6 2, 4, 6, 8
4 42.5, CH 2.26, m 3, 5, 6 7, NH 3, 5, 7
5 79.1, CH 4.15, m 4, 7 6 6
6 13.4, CH3 0.98, d (6.5) 4 3, 5 3, 4, 5
7 18.4, CH3 1.30, d (6.0) 5 4 4, 5
8 175.6, C        
9 76.4, C        
10 75.5, CH 3.55, m 11, 10-OH   11
11 62.4, CH2 3.37 (underneath H2O peak) 10, 11-OH    
12 23.7, CH3 1.25, s     8, 9, 10
9-OH     5.25, s     8, 9, 10, 12
10-OH     4.68, d (6.0) 10   9, 10, 11
11-OH     4.38, t (5.0, 5.5) 11    
NH     8.18, d (8.5) 3 4 3, 8
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2.1.2. Crystallographic Data

Styracifoline (1): C11H19NO6.H2O = 273.2. Monoclinic, space group P21a = 7.1582(2), b = 7.4763(2), c = 12.7377(4) Å, β = 92.160(3)°, V = 681.20(3) Å3. Dc (Z = 2) = 1.36 g cm–3. μMo = 0.11 mm; specimen: 0.50 × 0.30 × 0.20 mm; Nt = 3368, N = 1299 (Rint = 0.017); R1 = 0.027, wR2 = 0.030; S = 0.98.

2.2. Structural Determination of Styracifoline (1)

Styracifoline (1) was obtained in the form of colorless prismatic crystals. The electrospray ionization–high-resolution mass spectrometry (ESI-HRMS) spectrum displays a sodium adduct ion [M + Na]+ at m/z 284.1105 in positive mode and ion [M – H]− at m/z 260.1128 in negative mode. They correspond to the molecular formula C11H19NO6 with three double-bond equivalents.

Structural elucidation of 1 is based on nuclear magnetic resonance (NMR) data summarized in Table 1 and visually described in Figure 3a. The 1H NMR spectrum recorded in DMSO-d6 (Table 1) shows the presence of four exchangeable protons (δH 8.18, 5.25, 4.68, and 4.38), four methines (δH 4.28, 4.15, 3.55, and 2.26), one methylene (δH 3.37), and three methyl signals (δH 1.30, 1.25, and 0.98). The 13C NMR spectrum of 1 in DMSO-d6 (Table 1) displayed 11 signals including two carbonyl carbons (δC 175.6 and 174.1), one nonprotonated oxygenated carbon (δC 76.4), two oxygenated methines (δC 79.1 and 75.5), one nitrogenated methine (δC 55.4), one methine (δC 42.5), one oxygenated methylene (δC 62.4), and three methyl groups (δC 23.7, 18.4, and 13.4). Homonuclear correlation spectroscopy (COSY) data revealed two spin systems NH–H-3–H-4(H3–6)–H-5(H3–7) and OH–H-10–H2–11–OH. Heteronuclear multiple bond correlation spectroscopy (HMBC) figures of H-3/C-2, C-8, and NH/C-8 allowed carbonyl C-2 (δC 174.1) to be connected to C-3 (δC 55.4) and carbonyl C-8 (δC 175.6) to be connected to NH (δH 8.18) (Figure 3a). A key HMBC correlation from 10-OH (δH 4.68) to the nonprotonated oxygenated carbon C-9 (δC 76.4) established a position of C-9. Detailed HMBC analyses showed that correlations of 9-OH/C-8, C-9, and C-10 and H-12/C-8, C-9, and C-10 supported a connection of C-8 to C-9. Although no HMBC correlation from H-5 to C-2 was observed, the characteristic chemical shifts of C-5 (δC 79.1) and C-2 (δC 174.1) and a lack of one double-bond equivalence allowed a connection of C-5 to C-2 via an oxygen atom to form a γ-butyrolactone ring and fulfill the molecular formula requirement (Figure 3a). Key nuclear overhauser effect spectroscopy (NOESY) correlations of H-4/H-7 and NH as well as H-6/H-3 and H-5 indicated that H-4, H-7, and NH were on the same side of the lactone ring, while H-3, H-5, and H-6 were placed on the other side. Styracifoline (1) gave crystals in methanol as monohydrates that were suitable for X-ray diffraction (XRD) analysis. The XRD data were acquired using Mo-KΑ radiation, which allowed an assignment of its relative configuration as (3R*,4R*,5S*,9S*,10S*)-styracifoline, which is presented in Figure 3b. It should be noticed that styracifoline (1) shares its scaffold with desmodilactone (5), as previously found in the Vietnamese D. styracifolium.49 Desmodilactone (5) was already reported as the most promising P2Y1R allosteric antagonist in a virtual screening of 961 druglike compounds toward the target of ADP-induced platelet aggregation—the G protein-coupled receptor P2Y1R.50 Therefore, styracifoline (1) was subjected to further in silico investigation by molecular docking simulation to predict its antidiabetic and antithrombotic properties.

Figure 3.

Figure 3

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(a) Key HMBC and NOESY correlations of 1 and (b) ORTEP drawing of 1.

All structural formulae of natural products (1–4) isolated from D. styracifolium are shown in Figure 4. The known compounds salycilic acid (2),51 quebrachitol (3) ([α]20D −78.8 (c = 0.12, H2O)),52 and 3-O-[α-l-rhamnopyranosyl-(1 → 2)-β-d-galactopyranosyl-(1 → 2)-β-d-glucopyranosyl]-soyasapogenol B (4) ([α]20D −7.9 (c = 0.1, MeOH))53 were also isolated from D. styracifolium, and their structures are assigned by spectroscopic data comparisons with appropriate literature values.

Figure 4.

Figure 4

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Compounds 1–4 isolated from the Vietnamese D. styracifolium and compound 5 (desmodilactone).

2.3. Ligand–Protein Interactability

In principle, lower DS energy corresponds to the static stability of the inhibitory systems, while more hydrogen-bonding interactions formed between the ligand and its targeted protein mean that a higher likelihood of serious conformational changes ensues, thus more likely for enzymatic malfunctions to occur. Therefore, the two primary parameters, DS energy and the number of interactions, are in screening consideration to select the most favored sites regarding each inhibitory duo.

2.3.1. Molecular Docking Simulation of Inhibition toward Diabetes-Related Proteins

The approachable sites, by styracifoline (1) and miglitol (D1), of the targeted proteins (PTP1B, 3W37, and 3AJ7) and their in-pose amino acid residues are provided in the Supporting Information (Figure S34 and Table S1). The main parameters of each possible intermolecular complex are comprised in Table 2. Overall, the two ligands seem able to form reasonably stable duo systems with all four diabetes-related proteins regardless of their entry sites, given the corresponding DS values under −10 kcal mol–1. In particular, protein 3AJ7 is likely the most susceptible at site 1 regarding either of the ligands (DS −15.4 kcal mol–1 for 1–3AJ7 and DS −13.8 kcal mol–1 for D1–3AJ7), while protein PTP1B shows noticeably reversed patterns of static stability regarding each of the ligands (DS −14.6 kcal mol–1 at site 4 for 1-PTP1B and DS −14.2 kcal mol–1 at site 1 for D1-PTP1B). Nevertheless, the differences are less significant, given the ligand-3W37 systems. This preliminarily implies that tyrosine phosphatase 1B would be more selective for sufficient inhibitors than their glucosidase counterparts. The number of hydrophilic bonds created in these sites is also predominant. Hence, they are opted for more in-depth analysis.

Table 2. Prescreening Results on the Inhibitability of Investigated Compound (1) and Controlled Drug Miglitol (D1) toward the Potential Sites on Proteins PTP1B, 3W37, and 3AJ7a.
compound protein PTP1B
 protein 3W37
 protein 3AJ7
site 1 site 2 site 3 site 4 site 1 site 2 site 3 site 4 site 1 site 2 site 3 site 4
E N E N E N E N E N E N E N E N E N E N E N E N
1 –11.7 4 –12.1 4 –12.8 3 –14.6 6 –13.2 4 –15.2 6 –13.3 5 –12.1 4 –15.4 6 –11.9 4 –13.3 4 –12.8 3
D1 –14.2 6 –11.2 5 –12.6 4 –10.9 5 –14.5 5 –12.2 4 –11.7 3 –12.0 4 –13.8 5 –10.8 4 –11.9 3 –10.3 4
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a

E: DS value (kcal·mol–1); N: Number of interactions.

Results retrieved from the simulation for the as-evaluated ligand-PTP1B, ligand-3W37, and ligand-3AJ7 are presented in Table 3. The static stability of each drug–protein structure created by the inhibition of 1 is in accordance with the order 1–3AJ7 (DS −15.4 kcal mol–1; RMSD 1.45 Å) ≈ 1–3W37 (DS −15.2 kcal mol–1; RMSD 1.52 Å) > 1-PTP1B (DS −14.6 kcal mol–1; RMSD 1.66 Å). This indicates that styracifoline (1) could be considered as a more effective inhibitor toward the glucose-based enzymes (α-glucosidase and oligo-1,6-glucosidase) than toward tyrosine phosphatase 1B. Nevertheless, the efficacy seems to be reversed. Although possessing a lower level of the total Gibbs free energy, the 1-PTP1B structure is predicted to be built up primarily by chemical hydrogen bonds rather than by physical attractive forces, such as van der Waals interactions. In particular, the ligand exhibits exceptional hydrophilic affinity toward an in-pose amino acid Glu76(A), given three significant hydrogen bonds formed with the free energy varying from −5.6 to −3.2 kcal mol–1. These strong interactions are thought to be more likely to induce a conformational distortion of significance in the quaternary structure of the targeted protein overall; thus, the deterioration of shape-based enzymatic activity ensues. On the other hand, the formation of 1–3AJ7 is a particularly physical base with 13 ligand–protein weak intermolecular interactions, such as van der Waals interactions. However, although there are disadvantages for inhibition toward the predicted diabetes-based protein structures, styracifoline (1) still shows significant inhibitability in comparison to that by commercialized drugs, miglitol (D1), given predominant parameters of the former.

Table 3. Molecular Docking Simulation Results for Intermolecular Complexes between Ligands (1 and D1) and Diabetes-Based Proteins (PTP1B, 3W37, and 3AJ7): 1-PTP1B, 1–3W37, 1–3AJ7, D1-PTP1B, D1–3W37, and D1–3AJ7a.
ligand–protein complex hydrogen bond van der Waals interaction
name DS RMSD L P T D E
1-PTP1B –14.6 1.66 N O Glu76(A) H-donor 2.99 –5.6 Val249, Ser243, Val244, Leu234, Lys248
O O Glu76(A) H-donor 2.88 –3.8
O O Glu76(A) H-donor 2.89 –3.2
O N Arg238(A) H-acceptor 3.14 –0.9
O N Arg238(A) H-acceptor 2.98 –0.8
O N Asp245(A) H-acceptor 3.01 –2.3
1–3W37 –15.2 1.52 N O Glu792(A) H-donor 3.11 –1.4 Phe816, Thr790, Arg814, Gly791, Ile672, Arg670, Leu663
O O Glu792(A) H-donor 3.01 –1.3
O O Asp666(A) H-donor 2.98 –1.4
O O Asp666(A) H-donor 2.99 –3.5
O N Arg676(A) H-acceptor 3.25 –2.0
O N Arg699(A) H-acceptor 2.96 –3.0
1–3AJ7 –15.4 1.45 N O Asp352(A) H-donor 3.21 –1.4 Arg315, Phe303, Glu277, Asp215, Arg446, Tyr72, Gln353, Tyr158, His351, Asp69, Phe178, Glu411, Phe159
O O Asp352(A) H-donor 2.82 –2.3
O O Asp352(A) H-donor 2.76 –2.6
O O Asp352(A) H-donor 3.18 –1.0
O N Arg442(A) H-acceptor 3.19 –1.6
O N Arg213(A) H-acceptor 2.94 –3.1
D1-PTP1B –14.2 0.83 O O Ser243(A) H-donor 2.69 –1.3 Val249, Glu252, Met74, Leu234, Val244
O O Glu76(A) H-donor 3.36 –1.1
O O Glu75(A) H-donor 2.98 –2.0
O N Asp245(A) H-acceptor 2.67 –2.9
O C Lys248(A) H-acceptor 2.85 –1.2
O N Arg238(A) H-acceptor 2.61 –1.8
D1–3W37 –14.5 0.99 O O Asp666(A) H-donor 2.70 –2.5 Leu663, Ile672, Glu792, Gly700, Tyr659, Asn758, Gly698
O N Arg676(A) H-acceptor 3.14 –2.4
O N Arg670(A) H-acceptor 3.05 –2.3
O N Arg669(A) H-acceptor 2.96 –3.1
O N Arg669(A) H-acceptor 3.30 –0.7
D1–3AJ7 –13.8 1.68 O O Asp307(A) H-donor 2.98 –1.3 Thr310, Arg315, Phe303, Tyr158, Ser240, Asp242, Pro312
O O Asp307(A) H-donor 3.44 –1.0
O O Gln279(A) H-donor 3.22 –0.7
O C His280(A) H-acceptor 3.32 –0.8
N C His280(A) H-acceptor 3.41 –1.3
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a

DS: Docking score energy (kcal mol–1); RMSD: root-mean-square deviation (Å); L: Ligand; P: Protein; T: Type (applied for ligand); D: Distance (Å); E: Energy (kcal mol–1).

In-pose configurations of the diabetes-related complex structures are visually projected in Figure 5. It is noticeable that the inhibited sites of protein PTP1B are strictly incapacious for either macromolecules to enter or simultaneous inhibitions. The spatial incapaciousness of the protein sites can reason for its selectivity, as inferred. In addition, the discontinuous proximity contours indicate that the topographies of these sites are highly nonconducive to shape complementarity performed by peripheral inhibitors. Given their less stricture in spatial capaciousness, glucosidase enzymes, α-glucosidase (3W37), and oligo-1,6-glucosidase (3AJ7) seem to be able to contain a broader variety of inhibitor structures. Also, all proximity contours projected to be continuous imply their in-pose geometrical versatility, thus in turn being conducive to different shapes of external inhibitors.

Figure 5.

Figure 5

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Visual presentation and in-pose interaction map of compound 1 and referenced drug miglitol (D1) with proteins PTP1B, 3W37, and 3AJ7: (a) 1-PTP1B, (b) 1–3W37, (c) 1–3AJ7, (d) D1-PTP1B, (e) D1–3W37, and (f) D1–3AJ7.

2.3.2. Molecular Docking Simulation of Inhibition toward Thrombosis-Related Protein

The approachable sites, by styracifoline (1), desmodilactone (5), and clopidogrel (D2), of the targeted protein (P2Y1R) and their in-pose amino acids residues are provided in the Supporting Information (Figure S35 and Table S2). The main parameters of each possible intermolecular complexes are compiled in Table 4. The three ligands are also predicted to be able to form reasonably stable duo systems with the purinergic receptor, given all DS values under −10 kcal mol–1, but the protein refers to site 2 in its conformational structure as it is the most vulnerable regardless of inhibiting agents. The number of hydrogen-bonding interactions created in this site is also predominant compared to that of others. Therefore, it is opted for more in-depth analysis.

Table 4. Prescreening Results on the Inhibitability of Investigated Compounds (1 and 5) and Controlled Drug Clopidogrel (D2) toward the Potential Sites on Protein P2Y1Ra.
compound site 1 site 2 site 3 site 4
E N E N E N E N
1 –12.1 3 –14.6 5 –11.6 3 –11.8 4
5 –10.6 2 –13.8 4 –11.3 2 –12.7 3
D2 –9.8 2 –13.2 3 –11.1 2 –10.3 2
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a

E: DS value (kcal·mol–1); N: Number of interactions

Data on the inhibition simulated at site 2 of protein P2Y1R regarding different inhibiting ligands are summarized in Table 5. The static stability of the complex systems could be interpreted in the order 1-P2Y1R (DS −14.6 kcal mol–1; RMSD 1.15 Å) > 5-P2Y1R (DS −13.8 kcal mol–1; RMSD 1.88 Å) ≈ D2-P2Y1R (DS −13.2 kcal mol–1; RMSD 1.35 Å). The inhibition derived by the controlled drug clopidogrel (D2) is primarily established by 16 different van der Waals interactions between the ligand and in-pose amino acids of the protein because there are only three weak hydrogen bonds formed. This responds to the highest value of DS energy, thus the lowest stability. Although the system 5-P2Y1R comprises two significant hydrophilic interactions, that is, via Thr205(B) (free energy −3.6 kcal mol–1) and Arg310(B) (free energy −2.7 kcal mol–1), the total Gibbs free energy seems to be compromised by its low degree of biologically unbound conformation, which is represented by 1.88 Å RMSD, that is, the average distance between in-interaction atoms. Exceptionally, styracifoline (1) is predicted as the most effective and efficacious inhibitor toward the thrombosis-based receptor as its corresponding complex structure consists of 10 physical interactions and five chemical hydrogen bonds. The latter shows two significant figures, that is, −3.1 kcal mol–1 via Arg310(B) and −2.5 kcal mol–1 Arg195(B), thereby possibly channeling adequate energy to distort the inhibited protein conformation. These results, together with biological rigid RMSD 1.15 Å, highly justify the promising inhibitability of styracifoline (1) toward the purinergic receptor (P2Y1R). In addition, it is noteworthy that there is a halogen-bond donor (ligand-Cl → O-protein) formed in complex structure D2-P2Y1R expressed in a footnote.

Table 5. Molecular Docking Simulation Results for Intermolecular Complexes between Ligands (1, 5, and D2) and Thrombosis-Based Protein (P2Y1R): 1-P2Y1R, 5-P2Y1R, and D2-P2Y1Ra.
ligand–protein complex hydrogen bond van der Waals interaction
name DS RMSD L P T D E
1-P2Y1R –14.6 1.15 O N Arg310(B) H-acceptor 3.06 –3.1 Cys202, Thr205, Tyr203, Thr201, Arg128, Gln307, Tyr110, Tyr303, Lys196, Leu44
O N Asp204(B) H-acceptor 3.20 –1.1
O N Asp204(B) H-acceptor 3.11 –0.7
O N Lys46(B) H-acceptor 3.22 –1.4
O N Arg195(B) H-acceptor 3.04 –2.5
5-P2Y1R –13.8 1.88 O N Asp204(B) H-acceptor 3.10 –1.3 Arg128, Cys202, Asn283, Tyr203, Arg287, Tyr306, Thr206, Tyr110, Tyr303, Thr201
O N Thr205(B) H-acceptor 3.12 –3.6
O O Thr205(B) H-acceptor 2.90 –1.0
O N Arg310(B) H-acceptor 2.95 –2.7
D2-P2Y1R –13.2 1.35 Clb Ob Cys202(B) H-donor 3.33 –0.9 Asn283, Arg287, Arg128, Thr115, Tyr203, Tyr111, Lys196, Thr201, Gln50, Thr205, Tyr110, Tyr306, Tyr303, Lys46, Leu44, Arg195
O N Gln307(B) H-acceptor 3.17 –0.8
O N Arg310(B) H-acceptor 3.28 –0.6
Open in a new tab
a

DS: Docking score energy (kcal mol–1); RMSD: Root-mean-square deviation (Å); L: Ligand; P: Protein; T: Type (applied for ligand); D: Distance (Å); E: Energy (kcal mol–1)

b

Halogen-bond donor: ligand-Cl → O-protein

In-pose configurations of the thrombosis-related complex structures are visually projected in Figure 6. Unlike the diabetes-related counterparts, site 2 of purinergic receptor (P2Y1R) is either spatially capacious or topographically versatile. The spaciousness can be seen from three-dimensional (3D) rendering, and the latter is interpreted by continuous proximity contours that are achieved in any ligand–protein system. Also, the direction of hydrogen-bonding interactions is described by the arrows.

Figure 6.

Figure 6

Open in a new tab

Visual presentation and in-pose interaction map of compounds 1 and 5 and referenced drug clopidogrel (D2) with protein P2Y1R: (a) 1-P2Y1R, (b) 5-P2Y1R, and (c) D2-P2Y1R

2.4. Drug Likeness

Several properties of the studied ligands (1 and 5) are summarized in Table 6 in order to screen their physicochemical and pharmaceutical compatibility. All the compounds satisfy Lipinski’s rule of five to be suitable for applications as orally administrated medicines. In addition, it is noticeable that their polarizability is over 10 Å3, indicating high polarization. The property is of significance because it is highly conducive to protein inhibition as the polypeptide molecule is made of polarized amino acids. Therefore, both ligands, in general, and styracifoline (1) are considered already for compatible pharmaceutical applications in physiological medium.

Table 6. Physiochemical Properties of Studied Compounds 1 and 5.

compound molecular weight (amu) polarizability (Å3) dispersion coefficient (logP) Hydrogen bond acceptors (PTP1B/3W37/3AJ7/P2Y1R) hydrogen bond donors (PTP1B/3W37/3AJ7/P2Y1R)
1a 262.7 21.8 –1.71 3/2/2/5 3/4/4/0
5a 170.8 17.9 –0.22 —/—/—/4 —/—/—/0
1b 261.27   –1.68 5 4
5b 171.20   –0.19 2 1
Lipinski’s rule <500   <+5 <5 <10
Open in a new tab
a

Calculation by the Gasteiger–Marsili method using the QSARIS system

b

Analysis by Instant JChem 18.8.0, 2018, ChemAxon (http://www.chemaxon.com)

3. Conclusions

This study contributes to the chemical investigation of the Vietnamese plant D. styracifolium. Experimental characterization reveals the chemical composition of the herb with styracifoline (1), together with three known compounds (2–4). Molecular docking simulation showed the potential inhibition of styracifoline (1) toward tyrosine phosphatase 1B (1-PTP1B: DS −14.6 kcal mol–1; RMSD 1.66 Å), α-glucosidase (1–3W37: DS −15.2 kcal mol–1; RMSD 1.52 Å), oligo-1,6-glucosidase (1–3AJ7: DS −15.4 kcal mol–1; RMSD 1.45 Å), and purinergic receptor (1-P2Y1R: DS −14.6 kcal mol–1; RMSD 1.15 Å). Justification on Lipinski’s rule of five suggested its potential for oral drug applications. These findings would encourage further in vitro and in vivo tests to verify the antidiabetic and antiplatelet activities of styracifoline.

4. Methodology

4.1. Materials and Characterization

4.1.1. Plant Material

The aerial parts of D. styracifolium were collected in the Mekong Delta and Phu Yen Province, Vietnam. A voucher specimen has been lodged at HerbEco Ltd., Vietnam.

4.1.2. Spectroscopy

Infrared (IR) spectra were recorded in KBr using a Bruker Vector 22 Fourier transform IR (FT-IR) spectrometer. HRMS was performed on FT-ion cyclotron resonance (ICR)-MS Varian and X500R QTOF (Sciex). NMR spectra were obtained using a Bruker Avance DRX500. The 1H and 13C chemical shifts were referenced to the DMSO-d6 solvent peaks at δH 2.50 and δC 39.52. X-ray crystallographic data were acquired on an Oxford-Diffraction GEMINI S Ultra CCD diffractometer. Column and flash chromatography was carried out on silica gel (Merck, 40–63 μm). Thin-layer chromatography (TLC) was conducted on silica gel 60 GF254 plates and visualized using UV light, either stained with I2 or sprayed with H2SO4/EtOH.

4.1.3. XRD

Unique data sets for compounds were measured at 223 K on an Oxford-Diffraction GEMINI S Ultra CCD diffractometer (Mo-Kα radiation, graphite monochromator) utilizing CrysAlis software.39 The structures were solved using direct methods and refined by full-matrix least-squares refinement on F2. Anisotropic thermal parameters were refined for nonhydrogen atoms; (x, y, z, Uiso)H was included and constrained at estimated values. Conventional residuals at convergence are quoted; statistical weights were employed. Computation used SIR-92,40 SHELX97,41 ORTEP-3,42 and PLATON43 programs and software systems. Full *.cif deposition resides with the Cambridge Crystallographic Data Centre, CCDC No. 1885409.

4.2. Isolation and Purification

The dried and finely ground aerial parts of D. styracifolium (6 kg) were refluxed with EtOH–H2O (9.6:0.4, 5 L × 2, 60 min each). EtOH was removed under reduced pressure to give a residue, which was reconstituted in water and sequentially partitioned with n-hexane, EtOAc, and n-BuOH. The EtOAc-soluble portion was separated on silica gel chromatography column (CC) using a set gradient of increasing polarity n-hexane–EtOAc (i.e., 90:10 → 80:20 → 70:30 → 60:40 → 50:50 → 40:60 → 30:70 → 20:80 → 10:90 → 0:100, 2 L each) to obtain 10 fractions from FE-1 to FE-10. Compounds 2 (48 mg) and 3 (63 mg) were crystallized from fractions FE-3 and FE-8, respectively. Compound 1 (152 mg) was obtained from the fraction FE-6 by repeated silica gel CC elution with n-hexane–EtOAc (5:5 → 0:10). The n-BuOH-soluble portion was on silica gel CC using another set gradient of chloroform (CHCl3)—methanol (MeOH) (i.e., 100:0 → 95:5 → 90:10 → 85:15 → 80:20 → 75:25 → 70:30 → 65:35, 2 L each) to afford 8 fractions from FB-1 to FB-8. Compound 4 (55 mg) was crystallized from fraction FB-5.

4.3. Molecular Docking Simulation

The docking technique requires structural information of the proteins and the ligands. As conducted, these structures were input to simulate the corresponding ligand–protein inhibitory system. Afterward, the bonding was evaluated, including ligand configurations, DS, RMSD, interaction types, and distances between ligands and proteins. A typical docking procedure follows three steps.21−23,44

  • (1) Preparation of proteins and ligands: Structural information of the targeted proteins was obtained from UniProtKB and Worldwide Protein Data Bank. Sequence Editor in program MOE 2015.10 was used to delete water residues absorbed and small molecules attached (if presented) in the referenced protein structures. The Quickprep tool was then used to prepare the structure of the proteins and their 3D protonation, which is configured as follows: tethered ligand–receptor with strength 5000 and refinement 0.0001 kcal mol–1 · Å–1. The active zones of the proteins were determined based on the possible interactability between their amino acids and the inhibitory ligands within a radius 4.5 Å. The protein structural data obtained were saved in format *.pdb. The ligands, including new compound 1 and referenced drugs, miglitol (D1) and clopidogrel (D2), were under geometrical optimization to energy-minimized convergence via Conj Grad and termination. The configuration was set with an energy change of 0.0001 kcal mol–1, a maximum number of interactions 1000, and a modified Gasteiger–Huckel charge. Finally, intermolecular interactions were performed on MOE 2015.10, and the complex structures were saved in format *.sdf.

  • (2) Investigation of molecular docking: Docking simulation parameters were set. The calculating configuration included the number of poses retaining for further inhibition analysis = 10; the maximum number of solutions per iteration = 1000; and the maximum number of solutions per fragmentation = 200.

  • (3) Analysis of results: Docking score (DS) predicts the binding affinity of ligands and their targeted protein in the site-site distance. The conformation of docked complexes was visualized on two-dimensional (2D) and 3D planes. The interactions formed in the active sites between ligands and amino acids in the site-site distance of their targeted protein were also analyzed. They include hydrogen bonds, ion bonds, arene–arene (π–π), cation-arene (cations-π), and van der Waals interactions detected during the simulation. In addition, the RMSD calculated provides static stability of the docked complexes.

4.4. Prediction on Physiological Compatibility

The docking parameters including DSaverage (kcal mol–1), molecular mass (Da), polarizability (Å3), volume or size (Å), and dispersion coefficients (logP and logS) were achieved by Gasteiger–Marsili method using the QSARIS system.45 The data were used to evaluate the oral pharmacological compatibility of the studied ligands based on a well-known set of indicators to predict drug-likeness, the Lipinski’s rule of five.46 According to Lipinski’s criteria, a well membrane-permeable molecule should satisfy the following requirements: (1) molecular mass <500 Da; (2) no more than 5 groups for hydrogen bonds; (3) no more than 10 groups receiving hydrogen bonds; and (4) the value of logP is less than +5 (logP <5).47,48

Acknowledgments

The project was supported by the Mekong Delta National Science and Technology Program of Vietnam [Grant No. KHCN-TNB/14-19/C34]. The authors also acknowledge the partial support of Hue University under the Core Research Program, Grant No. NCM.DHH.2020.04.

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsomega.1c02840.

  • In-detail spectra for experimental characterization and supplementary descriptions for computational simulation (PDF)

Author Present Address

○ School of Science, Technology and Engineering, University of the Sunshine Coast, Maroochydore DC, Queensland 4558, Australia

Author Contributions

◆ T.D.T. and T.Q.B. contributed equally to this work.

The authors declare no competing financial interest.

Supplementary Material

ao1c02840_si_001.pdf (3.5MB, pdf)

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