Three-Dimensional Pharmacological Characteristics of Taste Type Receptors and Ligand-Based Virtual Screening in Chinese Herbal Medicine | Open Access Journals

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Three-Dimensional Pharmacological Characteristics of Taste Type Receptors and Ligand-Based Virtual Screening in Chinese Herbal Medicine

Zhang YL1*, Zhang Y1, Wang X2, Wang S1 and Qiao Y1

1School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China

2School of Traditional Chinese Medicine, Capital Medical University, Beijing, China.

*Corresponding Author:
Zhang YL
School of Chinese Materia Medica
Beijing University of Chinese Medicine, Beijing, China
Tel: +86 10 84738620
E-mail: icollean_zhang@163.com

Received date: 28/07/2015; Accepted date: 10/08/2015; Published date: 13/08/2015

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Abstract

In contrast to sweet and umami taste, which evolved to recognize a limited subset of nutrients, bitter taste has the onerous task of preventing the ingestion of a large number of structurally distinct toxic compounds. 25 taste receptor type 2 members (T2Rs) have been shown to function as bitter taste receptors. While there is an important question in taste research is how 25 receptors of the human T2Rs family detect thousands of structurally diverse compounds. In silico modeling of T2Rs allowed us to visualize the putative mode of various interactions between agonists and hT2Rs. In this study, ligand-based characterization of the structure function relationship for hT2Rs have been used and the pharmacophore models of T2R1, T2R10, T2R14 and T2R46 have been generated to understand the molecular basis underlying the broad tuning and selectivity of T2Rs members. Moreover, we served T2Rs as representation of Bitter Flavor and verified the relationship between them using virtual screening methods, which also show the scientific variety of Bitter Flavor because of the structural characteristics. The results show that T2Rs agonist pharmacophore models have ability to accumulate bitter herbs and identify the effective components from bitter herbs. It also shows that the Bitter Flavor theory of TCM holding various scientific content because of the structural characteristics. The method used in this paper provides a way for exploring the scientific connotation of five flavors theory of TCM, it can be extended to other TCM theory to solve similar problems.

Keywords

Five Flavors, Bitter, T2Rs, Pharmacophore, Pharmacological Efficacy.

Abrreviation

TCM: Traditional Chinese Medicine; GPCR G: Protein Coupled Receptor; T2Rs: Taste Type?Receptors; TCMD: Traditional Chinese Medicine Database; DS4.0: Accelrys Discovery Studio 4.0; T2R1: Taste receptor type 2 member 1; T2R10: Taste receptor type 2 member 10; T2R14: Taste receptor type 2 members 14; T2R46: Taste receptor type 2 members 46.

Introduction

Bitter taste is mediated by a family of 25 highly divergent GPCRs (G Protein Coupled Receptors) [1,2]. A large number of T2Rs have been shown to function as bitter taste receptors in heterologous expression assays [3-6], and several have distinctive polymorphisms that are associated with significant variations in sensitivity to selective bitter tastants in mice, chimpanzees and humans [7,8]. An important question in taste research is how 25 receptors of the human T2R family detect thousands of structurally diverse compounds. An answer to this question may arise from the observation that T2Rs in general are broadly tuned to interact with numerous substances. In silico modeling of T2Rs allowed us to visualize the putative mode of various interaction between agonists and hT2Rs (human taste type?receptors).

Despite recent progress in structure determination of GPCRs, structural data on GPCRs are scarce and crystallized receptor proteins exhibit only low amino acid sequence similarity with T2Rs. Therefore, ligand-based characterization of the structurefunction relationship for this GPCR family is necessary to understand the molecular basis underlying the broad tuning and selectivity of its members. Moreover, the pharmacological properties of hT2Rs are characterized by two important features: (i) a rather broad tuning, exemplified by the fact that, based on current information on 20 deorphaned receptors, some hT2Rs responded to up to one-third of all bitter compounds tested [9] and (ii) although highly variable, the average affinity for bitter agonists is rather low compared with other GPCR-ligand interactions [9]. Nevertheless, hT2Rs can discriminate even among chemically very similar bitter compounds with high accuracy [10]. The combination of these two features Results in the manifestation of agonist spectra, which are unique for every single hTAS2R, although some overlaps for individual bitter compounds are evident

According to the Bitter Database built in 2012 (http://bitterdb.agri.huji.ac.il/dbbitter.php), taste type? receptors and specific ligands distribution have been summarized in Table 1, which remind us of some hints about the law of T2Rs. In this study, pharmacophore model of T2R1, T2R10, T2R14 and T2R46 which have been reported possessing more ligands have been generated to study its diversified structural T2Rs.

BitterDB Receptor ID Short Name Organism Protein Name Number of Ligands
1 T2R1 Human Taste receptor type 2 member 1 35
2 T2R3 Human Taste receptor type 2 member 3 1
3 T2R4 Human Taste receptor type 2 member 4 22
4 T2R5 Human Taste receptor type 2 member 5 1
5 T2R7 Human Taste receptor type 2 member 7 6
6 T2R8 Human Taste receptor type 2 member 8 3
7 T2R9 Human Taste receptor type 2 member 9 3
8 T2R10 Human Taste receptor type 2 member 10 31
9 T2R13 Human Taste receptor type 2 member 13 2
10 T2R14 Human Taste receptor type 2 member 14 47
11 T2R16 Human Taste receptor type 2 member 16 10
12 T2R38 Human Taste receptor type 2 member 38 21
13 T2R39 Human Taste receptor type 2 member 39 20
14 T2R40 Human Taste receptor type 2 member 40 11
15 T2R41 Human Taste receptor type 2 member 41 1
16 T2R42 Human Taste receptor type 2 member 42 0
17 T2R43 Human Taste receptor type 2 member 43 16
18 T2R44 Human Taste receptor type 2 member 44 8
19 T2R45 Human Taste receptor type 2 member 45 0
20 T2R46 Human Taste receptor type 2 member 46 27
21 T2R47 Human Taste receptor type 2 member 47 10
22 T2R48 Human Taste receptor type 2 member 48 0
23 T2R49 Human Taste receptor type 2 member 49 2
24 T2R50 Human Taste receptor type 2 member 50 2
25 T2R60 Human Taste receptor type 2 member 60 0

Table 1: Taste TypeⅡ Receptors and Specific Ligands Distribution[11].

As a guide principle, TCM property theory has played an important role in syndrome differentiation and clinical prescription for thousands of years [11-13]. The theory of Five Flavors which has been used to summarize the function of drugs is one of the native-born medicine theories in China. According to TCM property theory, the favour of bitter is one of the five basic tastes (the other four are sweetness, sourness, saltiness, and bitterness). In this study, we served Taste Type?Receptors (T2Rs) as one of representations of Bitter Flavor and try to verify the relationship between them. In this study, we have also collected some natural agonists of T2Rs as well as the TCM sources and the related properties (Table 2).

Compounds TCM Source Herbal Source Property T2Rs
Benzoin An Xi Xiang Styrax tonkinensis (Pierre) Craib ex Hart. Bitter/Pungent, Normoal T2R10, T2R14
Andrographolide Chuan Xin Lian Andrographis paniculata (Burm. f.) Nees Bitter, Cold T2R46, T2R47, T2R50
Sinigrin Ting Li Zi Lepidium apetalum Willd. Pungent/Bitter, Cold T2R16, T2R38
Xanthohumol Pi Jiu Hua Humulus lupulus L. Bitter/Pungent, Cool T2R1, T2R14, T2R40
Adhumulone Pi Jiu Hua Humulus lupulus L. Bitter/Pungent, Cool T2R1, T2R40
Adlupulone Pi Jiu Hua Humulus lupulus L. Bitte/Pungent, Cool T2R1, T2R14
Absinthin Ku Ai Artemisia absinthum L. Bitter/Pungent, Warm T2R10, T2R14, T2R46, T2R47
Quassin Ku Shu Pi Celastrus angulatus Maxim. Cold, Bitter T2R4, T2R10, T2R14, T2R46, T2R47
Amarogentin Ku Xing Ren Prunus armeniaca L. var. ansu Maxim. Bitter, Warm T2R1, T2R4, T2R16, T2R39, T2R43, T2R46, T2R47, T2R50
Quinine Jing Ji Na Cinchona ledgeriana Moens Bitter, Cold T2R4, T2R7, T2R10, T2R14, T2R39, T2R40, T2R43, T2R44, T2R46
Aloin Lu Hui Aloe barbadensis Miller Bitter, Cold T2R43, T2R44
Aristolochic acid Ma Dou Ling Aristolochia contorta Bge. Bitter/Pungent, Cold T2R14, T2R43, T2R44
Brucine Ma Qian Zi Strychnos nuxvomica L. Bitter, Cold T2R4, T2R46
Picrotoxinin Mu Fang Ji Coculus trilobus (Thunb.) DC. Bitter/Pungent, Cold T2R10, T2R14, T2R46, T2R47
Strychnine Ma Qian Zi Strychnos nuxvomica L. Bitter, Cold T2R10, T2R46

Table 2: Natural agonists of T2Rs and TCM sources [11].

Materials and Methods

Generation of Common Feature Pharmacophore Model

A pharmacophore model can be considered as the highest common denominator of a group of molecules exhibiting a similar pharmacological profile and which are recognized by the same site of the target protein. HipHop algorithm, which attempts to produce an alignment of compounds expressing certain activity against a particular target and by superposition of diverse conformations to find common three-dimensional arrangements of features shared between them [2], has been used to build pharmacophore of T2R1, T2R10, T2R14, and T2R46. The best pharmacophore model of T2Rs have been built to search Traditional Chinese Medicine Database (TCMD) (Version 2009), questing for the agonists from traditional Chinese medicine for further research of the connection between the “Bitter Flavor” and T2Rs.

Compounds and Biological Data

The studies were implemented on a series of T2R1 agonists reported by literature [11,14-16]. The structures of agonists are listed in Figure 1. Considering the distribution of structural diversity, twelve compounds were selected to generate the pharmacophore model and the other compounds were used as test set to validate the model. The agonists of T2R10, T2R14 and T2R46 have been listed in Supplementary Materials.

chemistry-Chemical-structures

Figure 1: Chemical structures of T2R1 agonists.

Pharmacophore generation and validation:

In this study, the main steps of pharmacophore generation are as follows [17-18]:

Conformational analysis

The 3D qualitative pharmacophore hypotheses have been constructed by HipHop (Common Feature Pharmacophore Generation) within Accelrys Discovery Studio 4.0 (DS4.0). Ligand conformations were created within the relative energy threshold of 20 kcal/mol by the BEST mode (Best Quality Conformer Generation) at the number of 255 maximum conformations.

Common feature mapping

According to the Feature Mapping’s initial analysis, hydrogen bond acceptor (A), hydrogen bond donor (D), hydrophobic (H), and ring aromatic (R) which well-mapped all of the training set ligands, has been selected during the pharmacophore generation. These pharmacophore features can be characterized the interaction between the ligand and receptor.

Pharmacophore generation and verification

The HipHop algorithm attempts to produce an alignment of compounds by superposition of diverse conformations to find common three-dimensional arrangements of features shared between them. The Minimum Features has been set as 3, while the Maximum Features was 10. And we set the maximum number of pharmacophores was 10.

In this study, for validating the pharmacophore hypotheses using external test set molecules, which have not been used for pharmacophore model generation, a test database of Experimentally known T2Rs agonists embedded in a database consisting of some drug-like molecules (taken from the MDL MDDR database: Version2007.2) was constructed to evaluate all of the pharmacophore models. All of the identified ligands were filtered by Lipinskis Rule and were similar in chemical structural characteristics. To evaluate the performance of the models, four parameters (i.e., A%, Y%, N, and CAI) and the relationship between them was revealed in Figure 2 with the correlativity as follows [17]:

image (1)

image (2)

image (3)

image (4)

chemistry-Schematic-diagram

Figure 2: Schematic diagram of indicators evaluating the pharmacophore models [15].

A% can represent the ability to identify active compounds from the test database and Y% represents the proportion of active compounds in hit compounds. N, the index of effective identification, is used to evaluate the ability of the models to identify active compounds from non-active compounds. CAI, a comprehensive evaluation index, is used to identify the best pharmacophore model. D is the total number of compounds in the test database and A is the number of active compounds. Ht is the total number of hit compounds from the test database and Ha is the number of active hit compounds from the test database. The model with the highest value of CAI is considered to be the best.

Virtual Screening

According to the performance in terms of the enrichment factor (CAI value) of pharmacophore models, the most excellent pharmacophore model can be useful filters for virtual screening to identify T2R1, T2R10, T2R14 and T2R46 agonists within large compound repositories in TCM. This research select the model with highest CAI value served as a query to perform 3D Flexible Searching operation in DS 4.0 to search Traditional Chinese Medicine Database (TCMD, version 2009), which contains 23033 natural compounds from 6735 medicinal plants. Moreover, all potential hit compounds in the database should be satisfied the Lipinski’s rule of five requirements [19-20].

Results

Pharmacophore Models Generation

Twelve compounds were used as the training set for a HipHop running. The top 10 pharmacophore models with the calculation Results are detailed in Table 3. According to the simulated Results, the main features contains, Hydrophobic (H) and H-Bond Acceptors (A) have been generated. Both of the Direct Hi and Partial Hit values of these 10 pharmacophore models are “111111” and “000000” which confirm that all of the eleven molecules in training set have been taken into the generation of the models. The “4” value of Max Fit proves to us that all of the features in models can match with the molecules in training set. The Rank scores indicate the matching degree between the pharmacophore feature and the molecules. In general, the higher the score, the model matched more satisfactorily. The pharmacophore model calculation Results of T2R10, T2R14 and T2R46 have been listed in Supplementary Materials.

Model Features Rank Direct Hit Partial Hit Max Fit
01 HHHA 50.372 111111 000000 4
02 HHHA 50.260 111111 000000 4
03 HHHA 49.553 111111 000000 4
04 HHHA 49.285 111111 000000 4
05 HHHA 49.096 111111 000000 4
06 HHHA 48.796 111111 000000 4
07 HHHA 48.796 111111 000000 4
08 HHHA 48.261 111111 000000 4
09 HHHA 48.105 111111 000000 4
10 HHHA 47.887 111111 000000 4

Table 3: T2R1 Pharmacophore model calculation results.

Pharmacophore Models Validation

On the basis of the diagram of indicators evaluating the pharmacophore models showed before, we have shown the parameter values for each pharmacophore model in Table 4. NO.1 MODEL with the highest CAI value has been selected to screen TCMD2009 database. The pharmacophore feature of Model 1 mapped with ligand Xanthohumol has been showed in Figure 3. The parameter values for T2R10, T2R14 and T2R46 pharmacophore models have been listed in Supplementary Materials.

Model Aa Db Htc Had A%e Nf CAIg
1 27 180 41 19 70.4 3.09 2.17
2 27 180 46 17 63.0 2.46 1.55
3 27 180 46 18 66.7 2.61 1.74
4 27 180 48 17 63.0 2.36 1.49
5 27 180 42 16 59.3 2.54 1.50
6 27 180 45 18 66.7 2.67 1.78
7 27 180 47 18 66.7 2.55 1.70
8 27 180 46 18 66.7 2.61 1.74
9 27 180 49 17 63.0 2.31 1.46
10 27 180 59 16 59.3 1.81 1.07

Table 4: T2R1 Parameter values for each pharmacophore model.

chemistry-Pharmacophore-Model

Figure 3: T2R1 Pharmacophore Model_01 features (A) and the matching map with ligand Xanthohumol (B).

Virtual screening

To quest for the potential agonists of T2R1, T2R10, T2R14 and T2R46, the pharmacophore model generated by HipHop was used as the query to perform a search of all of the known compounds from TCMD2009. According to the Results of virtual screening, for example in T2R1, 186 compounds showing notable pharmacological activities have been hit, were documented in 57 Chinese Herbs from Chinese Pharmacopoeia 2010. 53.7% hits belong to the flavor of bitter (Table 5). No doubt target T2R1 bear some relation to “bitter flavor”.

Ligands TCM Souces Flavors T2Rs
TCMD059 Bai Guo Sweet/Bitter T2R10,T2R14
TCMD060 Bai Guo Sweet/Bitter/ T2R10,T2R14
TCMD061 Bai Shao Bitter/Sour T2R10,T2R14
TCMD062 Bai Shao Bitter/Sour T2R10,T2R14
TCMD063 Chai Hu Bitter T2R10,T2R14,T2R46
TCMD064 Chai Hu Bitter T2R10,T2R14,T2R46
TCMD065 Chai Hu Bitter T2R1,T2R10,T2R14,T2R46
TCMD066 Chai Hu Bitter T2R1,T2R10,T2R14,T2R46
TCMD067 Chuan Xin Lian Bitter T2R10,T2R14,T2R46
TCMD068 Chuan Xin Lian Bitter T2R10,T2R14,T2R46
TCMD069 Da Huang Bitter T2R10,T2R46
TCMD070 Da Huang Bitter T2R10,T2R14
TCMD071 Da Huang Bitter T2R10,T2R14,T2R46
TCMD072 Da Huang Bitter T2R1,T2R10,T2R46
TCMD073 Da Huang Bitter T2R1,T2R10,T2R14,T2R46
TCMD074 Da Huang Bitter T2R1,T2R10,T2R14,T2R46
TCMD075 Dan Sheng Bitter T2R10,T2R46
TCMD076 Dan Sheng Bitter T2R10,T2R46
TCMD077 Dan Sheng Bitter T2R10,T2R14,T2R46
TCMD078 Dan Sheng Bitter T2R1,T2R10,T2R14,T2R46
TCMD079 Dan Sheng Bitter T2R1,T2R10,T2R14,T2R46
TCMD080 Huang Lian Bitter T2R46
TCMD081 Huang Qin Bitter T2R10
TCMD082 Huang Qin Bitter T2R1,T2R10,T2R14,T2R46
TCMD083 Lian Qiao Bitter T2R1,T2R10,T2R14,T2R46
TCMD084 Lian Qiao Bitter T2R1,T2R10,T2R14,T2R46
TCMD085 Long Dan Bitter T2R1,T2R10,T2R14,T2R46
TCMD086 Ma Qian Zi Bitter T2R1,T2R10,T2R46
TCMD087 Zhe Bei Mu Bitter T2R1,T2R10,T2R14,T2R46
TCMD088 Zhe Bei Mu Bitter T2R1,T2R10,T2R14,T2R46
TCMD089 Zhi Qiao Bitter/Pungent/ Sour T2R1,T2R10,T2R46

Table 5: Partial Virtual Screening Hits of T2Rs Agonists.

Discussion

Correlation Analysis between Bitter Flavor and T2Rss

According to the simulation Results, the “hit” compounds are derived from medicinal herbs sharing bitter flavor, such as Salvia miltiorrhiza Bunge, Coptis chinensis Franch and Rheum palmatum L. Moreover, the number of “Hit” compounds whose Hit Score higher than 80.0 is 68 and 51.5% compounds are from pungent TCM sources. It indicated that the pharmacophore model of T2Rs can gather the same structural characteristics of pungent TCM. The model is capable to identify the compounds drawing from bitter TCM.

Method Limitation Analysis

this paper, 4 bitter taste receptor were studied through pharmacophore model and virtual screening to explore its relationship with bitter flavor. However, in addition to these 4 targets, the T2Rs family includes 21 other GPCRs. Therefore, this study is not enough to reveal the relationship between T2Rs family and bitter property, and further study is also needed to interpret bitter flavor through T2Rs family.

Conclusions

In this paper, ligand-based characterization of the structure-function relationship for hT2Rs has been used and the pharmacophore model of T2R1, T2R10, T2R14 and T2R46 have been generated. The Results show that T2Rs agonist pharmacophore models have ability to accumulate bitter herbs and identify the effective components from bitter herbs. It also show that the Bitter Flavor of TCM holding various scientific content because of the structural characteristics The method used in this paper provides a way for exploring the scientific connotation of five flavors theory of TCM, it can be extended to other TCM theory to solve similar problems.

Author Contributions

Y-J Qiao and Y-L Zhang have been conceived and designed the experiments. Y-X Zhang and X Wang have been involved in processing data and preparing the manuscript. S-F Wang participated in the Discussion of views in the paper. All authors have read and approved the final manuscript.

Acknowledgements

This study was financially supported by the National Science Foundation of China (Project No. 81430094)

References