Fast and effective identification of the bioactive compounds and their targets from medicinal plants via computational chemical biology approach

Shoude Zhang; Weiqiang Lu; Xiaofeng Liu; Yanyan Diao; Fang Bai; Liyan Wang; Lei Shan; Jin Huang; Honglin Li; Weidong Zhang

doi:10.1039/C0MD00245C

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DOI: 10.1039/C0MD00245C (Concise Article) Med. Chem. Commun., 2011, 2, 471-477

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Fast and effective identification of the bioactive compounds and their targets from medicinal plants via computational chemical biology approach†

Shoude Zhang‡ ^a, Weiqiang Lu‡ ^b, Xiaofeng Liu‡ ^b, Yanyan Diao ^b, Fang Bai ^b, Liyan Wang ^b, Lei Shan *^c, Jin Huang *^b, Honglin Li *^b and Weidong Zhang *^ac
^aSchool of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China. E-mail: wdzhangy@hotmail.com; Fax: +86-21-64250213; Tel: +86-21-64250213
^bShanghai Key Laboratory of Chemical Biology, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China. E-mail: huangjin@ecust.edu.cn; hlli@ecust.edu.cn
^cDepartment of Phytochemistry, School of Pharmacy, Second Military Medical University, Shanghai 200433, China. E-mail: shanleish@yahoo.com.cn

Received 1st December 2010 , Accepted 30th March 2011

First published on 13th May 2011

Abstract

The potential drug target database (PDTD) was searched by the TarFisDock server, a reverse docking approach, to identify putative targets for a collection of 19 natural products derived from two medicinal plants Bacopa monnieri (L.) Wettst (BMW) and Daphne odora Thunb. var. marginata (DOT), which are both used for the treatment of diabetes and inflammation in Traditional Chinese Medicine (TCM). Out of the top 5% of target candidates, dipeptidyl peptidase IV (DPP-IV) was the most frequent potential target and the predicted results were subsequently confirmed by in vitroenzyme assay. As a result, five natural products show moderate inhibitory activities against DPP-IV with IC₅₀ values ranging from 14.13 μM to 113.76 μM. Subsequently, thirteen analogues of active compounds out of our in-house natural products database (NPD) were also identified with inhibitory activity against DPP-IV, with IC₅₀ values ranging from 26.92 μM to 87.72 μM. The results indicate that the computational chemical biology approach is a good complement to the experimental target identification strategies for elucidating the mechanism of the natural products, especially for those components without unambiguous binding targets whilst having some traditional efficacy in TCM.

Introduction

Natural products have been considered as invaluable sources of lead compounds in developing front-line drugs over the past half century.^1–4 From 1970 to 2006, about 24 unique natural products have been discovered and approved as drugs,^5–9 and those having explicit molecular mechanisms are useful to understand their related clinical therapeutic effects.^10,11 However, deficient knowledge about the biological targets of the vast number of natural products limits the scope of their further application in new drug discovery. As we know, many natural products derived from Traditional Chinese Medicine (TCM) have clinical therapeutic effects with unknown targets for their mechanism of action. As a source of leads and prospective drug candidates, the biological targets also need to be determined for newly discovered natural products from microbials, plants, marine or other sources. Therefore, there is an ever-increasing demand to identify the target(s) of known or unknown bioactive natural products.^6,12

Target identification and validation are the first key stages in the drug discovery pipeline.¹³ Numerous technologies^12,14 have been developed to identify and validate the drug targets based on mass spectrometry, and several experimental approaches such as affinity chromatography,¹⁵phage display,¹⁶mRNA display,¹⁷ yeast-three-hybrid,¹⁸ two-dimensional gel electrophoresis,¹⁹ genomic and proteomic techniques have been proved to be feasible methods to identify the targets of natural products or synthetic chemicals. However, these methods have limited application due to their laborious and time-consuming nature.²⁰ As a complement to the experimental methods, a series of computational tools, such as reverse docking, have been proven as novel approaches for target identification via high throughout virtual screening of targets in silico.^21–25 TarFisDock (http://www.dddc.ac.cn/tarfisdock/) is a web-based tool for automating the procedure of searching the potential targets, calculating the binding affinities between the small molecule probes and the potential protein targets from a large store of protein 3D structures.^26,27 In our previous studies, the potential targets for [6]-gingerol and two active natural products from Ceratostigma willmottianum were successfully identified through TarFisDock.^22,28

Bacopa monniera (L.) Wettst. (BMW), a traditional Indian medicinal plant, has been widely used in the Ayurvedic system of medicine,²⁹ and Daphne odora Thunb. var. marginata (DOT) is another medicinal plant used for the treatment of injuries from falls and bruises as a folk medicine in China. Recently, we reported that two new chemical constituents of this plant displayed significant antiproliferative activity on several cancer cell lines.³⁰ In this study, we took advantage of reverse docking in searching the potential drug target database (PDTD, http://www.dddc.ac.cn/pdtd/),²⁷ and identified that the dipeptidyl peptidase IV (DPP-IV) was one of the most possible target candidates of the 19 natural products ingredients from the above two medicinal plants (MPs), according to their application in diuretic indications. Subsequently, 13 analogues of the active compounds out of our in-house natural products database (NPD) also presented moderate inhibitory activity against DPP-IV (from 26.92 μM to 87.72 μM). The results indicate that the computational chemical biology approach is a good complement to the experimental target identification strategies in elucidating the way for the mechanism of the natural products, especially for those components without unambiguous binding targets whilst having some traditional efficacy in TCM.

Results and discussion

Nineteen compounds (Tables S1 and S2, ESI†) isolated from BMW and DOT were reverse docked using the TarFisDock server against the protein targets in PDTD, which is a comprehensive database currently containing >1100 entries with >800 known potential drug targets from the Protein Data Bank (PDB). The top 5% of candidate targets for each query (as shown in Table S1†) were reserved along with the rankings, interaction energy scores, binding poses of the query molecules, and corresponding annotation information of functions and indications. The target candidates associated with the traditional efficacy and indications of the two MPs (as shown in Table 1)^31,32 were selected and summarized in Table 2.

Table 1 Traditional therapeutic efficacy and indications of medicinal plants BMW and DOT

MPs	Traditional efficacy	Related disease
BMW	Detoxification	Inflammation
	Elimination of swelling	Inflammation/diabetes
	Diuretic	Diabetes/renal diseases
	Qingreliangxue	Inflammation/psoriasis/blood disease/infections
DOT	Anti-inflammatory	Inflammation
	Elimination of swelling	Inflammation/diabetes
	Diuretic	Diabetes/renal diseases
	Huoxuehuayu	Blood disease

Table 2 Predicted targets and related diseases for the 19 compounds from BWM and DOT identified by TarFisDock server^a

Compound	Inflammation	Diabetes	Renal diseases	Psoriasis	Blood disease	Infections
a LTA4H: Leukotriene A4 hydrolase; DPP-IV: Dipeptidyl Peptiidase IV; NOS: Nitric Oxide Synthase; DR: Dihydrofolate Reductase; CFX: Coagulation Factor Xa; AR:Aldehyde Reductase; 3AHD: 3-alpha-hydroxysteroid Dehydrogenase; NNA: Nicotinate-nucleotide Adenylyltransferase; UDS: Undecaprenyl diphosphate synthetase; UM: Unspecific Monooxygenase; SPAT: Serine Proteinase alpha-thrombin; OC: Oxidosqualene cyclase; AVPR1A: Vasopressin V1a receptor; IMPD: Inosine-5′-monophosphate Dehydrogenase; GP: Glycogen Phosphorylase; PLA2: Phospholipase A2; NA: Neuraminidase subtype N9; AG: Alpha glucosidase; IR: Insulin Receptor; FGFR2: Fibroblast Growth Factor Receptor 2; Fla: Flavohemoglobin; PN: Peptide N-myristoyltransferase; Gly: Glycosyltransferase; PPAR: Peroxisome Proliferator Activated Receptor delta; ASD: Aspartate-semialdehyde Dehydrogenase; CR: Cyclophilin Receptor; CS: Chorismate synthase; TOP: Topoisomerase IV; DDRP: DNA-directed RNA Polymerase II 19 KDa Polypeptide.
1	Phospholipase D	Glucokinase	------	------	------	HPPK
2	LTA4H /DR	DPP-IV	------	------	NOS	HPPK
3	3AHD	DPP-IV/ AR	------	------	CFX	DR/ HPPK
4	LTA4H	UM/ DPP-IV	------	------	SPAT	NNA/UDS/ HPPK
5	LTA4H	AR/ DPP-IV	AVPR1A	------	OC/SPAT	HPPK /IMPD/NNA
6	LTA4H	------	------	p38 MAP kinase	Thrombin	HPPK
7	LTA4H /PLA2	DPP-IV	------	------	------	NNA/UDS
8	DR	DPP-IV /AG	------	------	------	NA/HPPK
9	------	IR/DPP-IV	------	------	FGFR2	Caspase-1/Fla
10	------	DPP-IV	------	------	------	PN/Gly
11	Chymase	DPP-IV /PPAR	------	------	------	------
12	------	DPP-IV /IR/AR	------	------	------	HPPK /ASD/PN
13	------	DPP-IV	AVPR1A	------	------	Gly/ASD
14	CR	DPP-IV	------	------	------	CS
15	------	DPP-IV	------	------	------	DR/TOP
16	------	DPP-IV/IR/AG	------	------	------	DR/ASD
17	Matrilysin	IR/DPP-IV	------	------	------	------
18	LTA4H	------	DDRP	------	AVPR1A	IMPD/ASD
19	CR	------	------	------	AVPR1A	Fla

We finally focused on three targets (LTA₄H, HPPK, DPP-IV) because of their high frequency of occurrences in the result list (Table 2). To further analyze the relationship between the compounds and their predicted targets, a network containing 55 nodes (19 compounds and 36 targets) and 122 interactions was illustrated in Fig. 1. Inspection of the interaction network shows that DPP-IV has the largest number of connections to the compounds. According to the known therapeutic indications (diabetes) of BMW and DOT, DPP-IV, which is a well-known viable therapeutic target for type II diabetes,³³ was chosen as the target for the subsequent experimental validation.


	Fig. 1 Interaction network between the 19 compounds and their predicted targets. The connection data were obtained via TarFisDock computation, and if a protein is considered to be a predicted target of a compound, we presume they can interact with each other and a connection forms between them (presented as a line). The network contains 55 nodes (19 compounds and 36 targets) and 122 interactions, and compounds and targets are presented as diamonds and circles, respectively. The degree values of the nodes (the number of edges linked to the node) are mapped to the sizes of the nodes, and a larger node size indicates a higher degree value. The targets are clustered by their related diseases, which are also emphasized by different colors.

As a widely distributed serine protease that exhibits postproline and alanine peptidase activity, DPP-IV biologically inactivates peptidesvia cleavage at the N-terminal region after X-proline or X-alanine.³⁴ Its two hormone substrates, glucagon-like peptide 1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP), are important for glucose metabolism.^35,36 DPP-IV proves to be an attractive pharmaceutical drug target for the treatment of type II diabetes, and so far two small molecule inhibitors of DPP-IV, saxagliptin and sitagliptin have been approved as the clinical drugs for type II diabetes treatment.^37–39

To validate the prediction that DPP-IV is a potential target for these compounds from the two MPs, inhibitory activities against DPP-IV were tested in vitro. Five out of these 19 natural compounds showed moderate inhibitory activities, with IC₅₀ values ranging from 14.13 μM to 113.76 μM (Fig. 2). Among them, compound 4 exhibits good complementarity in terms of shape and pharmacophore interactions in the binding pocket of DPP-IV (Fig. 3). Afterwards, 27 analogs of these five active compounds (as shown in Table S2†) were identified and tested in the inhibitory activity assay from our in-house natural products database (NPD) which holds a collection of about 4000 natural products’ structures. Thirteen compounds out of the 27 analogues showed moderate inhibitory activities against DPP-IV. Therefore, the results of in vitro biological effects proved that DPP-IV is a potential target for these compounds. In spite of the moderate inhibitory activities presented, the combined amplification and synergic effects from the multiple components in these two MPs may contribute to the overall efficacies in diuretic indications at the molecular, cellular, and organism levels.⁴⁰ Further work needs to be performed to systematically dissect their mechanisms and explore their value and possible beneficial effects in the treatment of disease models in vitro and in vivo in the future.


	Fig. 2 Five hits identified with reverse docking.


	Fig. 3 Dose–response curve for inhibition activation by compound 4 (left). The predicted binding mode of compound 4 and DPP-IV (right). The carbon atoms are illustrated in cyan for the compound, respectively. The binding site is illustrated with a blue solid surface. The protein is illustrated with cartoon. The yellow dashed lines indicate protein–ligand hydrogen bonds.

These compounds can be divided into five categories according to the similarity of the compounds' skeletons (Table 3), which are flavonoids, daphneolons, betulinic acids, stigmasterols and others, respectively.

Table 3 Categories and sources of the active compounds, and their inhibitory effects against DPP-IV

Category	Compounds	IC₅₀/μM	Sources
a Compound 7 and its analogs. b Compound 5 and its analogs. c Compound 8 and its analogs. d Compound 10 and its analogs.
Flavonoids ^a	7	113.76	Daphne odora Thunb. var. actrocaulis Rehd.
	30	33.11	Abies georgei Orr
	32	26.92	Incarvillea sinensis LAM
	34	32.73	Hypericum japonicum Thunb. ex Murray
	35	41.69	Blumea balsamifera DC.
	36	37.15	Rhododendron spinuliferum Franch.
Daphneolons^b	5	38.43	Daphne odora Thunb. var. marginata
	20	41.40	Ainsliaea rubrifolia Franch.
	22	22.39	Daphne odora Thunb. var. marginata
	24	87.72	Incarvillea mairei var. grandiflora (Wehrhahn) Grierson
	26	83.18	Incarvillea mairei var. grandiflora (Wehrhahn) Grierson
	28	87.10	Daphne odora Thunb. var. actrocaulis Rehd.
Betulinic acids^c	8	55.82	Bacopa monnieri (L.) Wettst
	38	56.23	Zanthoxylum nitidum (Roxb.) DC.
	39	32.36	Zanthoxylum nitidum (Roxb.) DC.
Stigmasterols^d	10	16.58	Bacopa monnieri (L.) Wettst
Stigmasterols^d	43	28.84	Bacopa monnieri (L.) Wettst
Other	4	14.13	Daphne odora Thunb. var. marginata

The predicted binding poses of the five compounds (4, 5, 7, 8, and 10) in the active site of DPP-IV protein generated by reverse docking are shown in Fig. 4. The active site of DPP-IV lies in a large cavity and consists of two highly discriminative binding sites for most inhibitors.⁴¹ The carboxylates of the Glu 205/206 dyad can form strong salt bridges with the basic groups of the inhibitors such as the first and second amines, which can be regarded as an anchor to filter out the undesired binding ligands. Although the five hit compounds do not possess basic groups, they can form hydrogen bond interactions with the Glu dyad and the hydrophilic residues nearby as Arg 125 does through different chemical moieties, such as hydroxyls of compounds 5, 7, 10 and the carboxylate of compound 8. However, such ligand–protein interactions are not as strong as the ionic ones observed in the well-designed DPP-IV inhibitors, and as a result the inhibitory activities of the five natural products are relatively low. The other specific binding site of DPP-IV is called the S1 pocket which is highly rigid and composed of the side chains of hydrophobic residues like Tyr 631, Val 656, Trp 659, Tyr 662, Tyr 666, and Val 711. The rigidity of the pocket has been probed and larger rings cannot be tolerated. For compounds 4 and 5, the S1 pocket is occupied by the 6-membered rings like pyrone and benzene, respectively, while for compounds 7 and 8, the S1 pocket is not fully occupied by the small hydrophobic motifs like acetyl and propene, which in turn reduces the inhibitory potencies compared with compounds 4 and 5. Additional ligand–protein interactions contributing to binding affinities include hydrophobic interactions with aromatic rings of Phe 357 and Tyr 547, which are exposed to the binding site, as observed in the binding poses in Fig. 4. The predicted binding poses of the hit compounds highly conform to the key pharmacophores underpinned by the recognition studies of DPP-IV and other inhibitors,⁴¹ indicating that the reverse docking strategy is reliable in target identification in this study.


	Fig. 4 Predicted binding poses of the five hit compounds in the active site of DPP-IV: daphneticin (4, A), daphneolon (5, B), 4′-trihydroxy-8-ethoxycarbonyl flavan (7, C), betulinic acid, (8, D), and 3-O-stigmasterol-(6-O-palmitoyl)-β-D-glucopyranoside (10, E). The carbon atoms are illustrated in cyan and green for the compounds and DPP-IV residues, respectively. The shape of the binding site is illustrated with a gray mesh surface. Only the residues within 8 Å of the binding compounds are displayed for clarity. The yellow dashed lines indicate protein–ligand hydrogen bonds.

Conclusion

Target identification of natural products is pivotal to understand the mechanism of medicinal plants. Herein, we used the computational chemical biology strategy, a reverse docking method, to seek the potential binding proteins for a collection of natural products. This process has led to the identification of DPP-IV as a novel target of five natural products from two medicinal plants with moderate inhibitory activities. Our results indicated that the computational chemical biology method combined with biological experiment is an efficient and reliable method for finding novel targets of natural products derived from medicinal plants. Furthermore, combined with modern computational, system or network biology approaches,⁴² the combined amplification and synergic effects of the components from TCM should be probed, which provides an efficient and feasible way to enhance the therapeutic efficacy and reduce the adverse effects of the marketed drugs.⁴³ This work provides a successful paradigm for the target validation of natural products, and will boost the mechanism research for multi-component Chinese herbal medicine formulae in the future.

Experimental procedures

Chemistry

The 19 compounds from two MPs, BMW and DOT, as well as the analogs are all selected from the in-house Natural Product Database (NPD), which holds a collection of 4000 natural products isolated from 85 MPs. Detailed data of characterization are provided in the ESI.†

Reverse docking

TarFisDock, a web server for identifying drug targets with a docking approach, was used to screen major chemical constituents of BMW and DOT. TarFisDock docks small molecules into the 3D structures of potential drug targets deposited in the PDTD (Potential Drug Target Database), and output the top 5% of candidates ranked by the interaction energy scores, along with corresponding binding conformations of the query and annotation information of biological functions and therapeutic indications.¹⁰ The most frequently occurred target candidates identified by the reverse docking are considered as potential target candidates for further studies. More descriptions on the reverse docking methods have been reported previously.^10–12 The binding poses of the compounds with the DPP-IV were automatically generated by TarFisDock.

In vitro enzyme inhibition assay

The fluorimetric assay is based on the cleavage of the 7-amino-4-methylcoumarin (AMC) moiety from the C-terminus of the peptide substrate H-Gly-Pro-AMC (SIGMA), which increases its fluorescence intensity at 460 nm.⁴⁴ DPP-IV inhibitory activity assays were performed in 96-well flat-bottom plates in a total assay volume of 100 μL. The diluted recombinant soluble human DPP-IV (SIGMA) was incubated for 10 min at room temperature in 50 mM Tris-HCl (pH 7.5), with different concentrations of the compounds to be tested. Compound solutions were prepared from stock in DMSO. After 10 min incubation, the substrate H-Gly-Pro-AMC was added to a final concentration of 50 μM. The increase in fluorescence was monitored for 20 min at room temperature with a Synergy™ 4 Multi-Mode Microplate Reader (BioTek). IC₅₀ values were determined from plots of percent activity over compound concentration using the GraphPad Prism software with three independent determinations.

Abbreviations

PDTD	potential drug target database
TarFisDock	Target Fishing Dock
BMW	Bacopa monnieri(L.)Wettst
DOT	Daphne odora Thunb. var. marginata
DPP-IV	dipeptidyl peptidase IV
NPD	natural products database
TCM	Traditional Chinese Medicine
MPs	medicinal plants

Acknowledgements

This work was supported by the 863 Hi-Tech Program of China (grants 2007AA02Z304 and 2007AA02Z147), the Special Fund for Major State Basic Research Project (grant 2009CB918501 and 2011CB910200), the National Natural Science Foundation of China (grants 20803022 and 90813005), the Major National Scientific and Technological Project of China (grant 2009ZX09501-001), the Shanghai Committee of Science and Technology (grants 09dZ1975700, 10431902600 and 08JC1407800), the Innovation Program of Shanghai Municipal Education Commission (10ZZ41), and the Fund of State Key Laboratory of Phytochemistry and Plant Resources in West China. Honglin Li is also sponsored by Shanghai Rising-Star Program (grant 10QA1401800), Program for New Century Excellent Talents in University and the Fundamental Research Funds for the Central Universities.

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Footnotes

† Electronic supplementary information (ESI) available: The detailed results of TarFisDock, the structures and chemistry section of all the compounds. See DOI: 10.1039/c0md00245c

‡ These authors contributed equally to this work.

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