TCMAnalyzer: a Chemo- and Bioinformatics Web Service for

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TCMAnalyzer: a Chemo- and Bioinformatics Web Service for Analyzing Traditional Chinese Medicine Zhihong Liu, Jiewen Du, Xin Yan, Jiali Zhong, Lu Cui, Jinyuan Lin, Lizhu Zeng, Peng Ding, pin chen, Xinxin Zhou, Huihao Zhou, Qiong Gu, and Jun Xu J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.7b00549 • Publication Date (Web): 08 Feb 2018 Downloaded from http://pubs.acs.org on February 10, 2018

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Journal of Chemical Information and Modeling

TCMAnalyzer: a Chemo- and Bioinformatics Web Service for Analyzing Traditional Chinese Medicine Zhihong Liu1,#, Jiewen Du1,#, Xin Yan1, Jiali Zhong2, Lu Cui1, Jinyuan Lin1，Lizhu Zeng1，Peng Ding1, Pin Chen3, Xinxin Zhou2, Huihao Zhou1, Qiong Gu1,*, and Jun Xu1,* 1

Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun

Yat-sen University, 132 East Circle at University City, Guangzhou 510006, China 2

School of Chinese Materia Medica, Guangzhou University of Chinese Medicine,

Guangzhou 510006, China 3

National Supercomputer Center in Guangzhou, Sun Yat-sen University, Guangzhou

510006, China

# Equal contributors. *To whom correspondence should be addressed. Jun Xu：[email protected] or [email protected] Qiong Gu：[email protected]

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Abstract Traditional Chinese Medicine (TCM) has been widely used and proven effective in the long-term clinical practice. However, the molecular mechanism of action for many TCMs remains unclear due to the complexity of many ingredients and their interactions with biological receptors. This is one of the major roadblocks in TCM modernization. In order to solve this problem, we have developed TCMAnalyzer, which is a free web-based toolkit allowing a user to: 1) identify the potential compounds that are responsible for the bioactivities for a TCM herb through scaffold-activity relation searches using structural search techniques, 2) investigate the molecular mechanism of actions for a TCM herb at systemic level, 3) explore the potential targeted bioactive herbs. The toolkit can result in TCM networks that demonstrate the relations among natural product molecules (small molecular ligands), putative protein targets, pathways, and diseases. These networks are graphically depicted to reveal the mechanism of actions for a TCM herb, or to identify new molecular scaffolds for new chemotherapies. TCMAnalyzer is freely available at http://www.rcdd.org.cn/tcmanalyzer. Keywords Traditional Chinese Medicine, TCMAnalyzer, Chemoinformatics, Bioinformatics, Natural Products, Mechanism of Action, Targets, Diseases, Pathways


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Introduction TCM has been widely used in China and proven effective in clinical practice. Some natural products from TCM are well characterized at molecular level and have well understood mechanism of action. However, for the majority of TCM there is no clear evidence of clinical efficacy. TCM inspired the discovery of anti-malaria agents won the Nobel Prize in physiology or medicine in 2015. Recently, more TCMs are indexed in the European and US Pharmacopoeia. Dantonic (T89) capsule has demonstrated positive results in phase III clinical trial to prevent and treat stable angina and has great chance to be approved by FDA. Ban Lan Gen, a well-known Chinese cold and flu remedy, has been approved by UK Medicines and Healthcare Products Regulatory Agency (MHRA). Along with the increasing number of successful development and modernization of TCM products, TCM has gain more attention both from academia and industries. TCM are effective in the prevention and treatment of various diseases. However, the molecular mechanism of action for many TCMs remains unclear due to the complexity of many ingredients and their interactions with biological receptors. This make difficulties to identifying the essential active ingredients and quality control, and finally hinders the development of TCM. According to TCM theory, TCM exert the therapeutic action through multiple synergistic functions.1 This consistent well with the theory of network pharmacology, which is proposed as the next paradigm of drug discovery.2 Network pharmacology is based on the theory of systems biology, which aims to understand biological systems through network analysis at system level.2 Network pharmacology provides a good approach for better understanding the mechanism of action (MOA) of TCM.1, 3, 4 Due to the significance, chemoinformatics and bioinformatics approaches were applied in TCM study. Hylands analyzed the distribution patterns of 8411 compounds from 240 Chinese herbs using Random Forest (RF) and self-organizing maps (SOM).5 Bender introduced the in silico target prediction approaches to rationalizing the MOA of TCM.6 Recently, they further investigated the cold, hot, and neutral nature of TCM based on a large scale analysis of compound−nature pairs from TCM, and elucidated ACS Paragon Plus Environment


the difference between different nature classes in TCM on the molecular level.7 For elucidating the MOA of TCM for type II diabetes mellitus (T2DM), Hou proposed an integrated protocol that combines docking and pharmacophore approaches to find the potential inhibitors from TCM for the T2DM-related targets and establish the compound−target interaction network.8 Many chemo- and bioinformatics analysis on TCM are reported.9-12 Presently, several TCM based knowledge database were reported focus on different aspects of TCM study. Chen developed TCM Database@Taiwan, a small molecular database on traditional Chinese medicine for virtual screening.13 Shi reported the Traditional Chinese Medicine Integrated Database (TCMID), which records TCM-related information collected from different resources and through text-mining method.14 Wang reported traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) with a particular strength on the composition of the large number of herbal entries with ADME properties.15 He reported BATMAN-TCM, focus on bioinformatics analysis tool such as, gene function enrichment analysis for putative targets of TCM.16 Other databases like CVDHD17 and NANPDB18, focus on the natural products and their specific diseases or areas. Based on the comprehensive resource of TCM, we develop TCMAnalyzer, a chemo- and bioinformatics web service for analyzing TCM to assist scientists in answering questions about TCM regarding active ingredients, protein targets, therapeutic mechanisms, and key structural fragments responsible for the therapeutic activities. The algorithms embedded in the toolkit, such as sub-structure search, similarity search, and scaffold search are developed in our group. All those results are rendered with interactive network. This web service would help in structure mining the complex ingredients, MOA of TCM illustration, and the bioactive herb explorations. Methods Data and analytic tools. The data used in TCMAnalyzer have the following


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components: herbal recipe, the molecular structures of active ingredients, protein target, signal transduction pathways and, medical indications (Figure 1). The recipe and related information are derived from the latest release of Chinese pharmacopoeia 2015 edition. The molecular structures of active ingredients come from the public database TCMSP15 and TCMID14. Targets and disease data are derived from ChEMBL19 and TTD20. For the interaction data of ingredients against targets, we downloaded

the

MySQL

version

of

ChEMBL

database

(ChEMBL_22)

(https://www.ebi.ac.uk/chembl/downloads), and extract the targets and the bioactivity data for the structures of ingredients using an in-house script. The targets for active ingredients was defined using 100µM as threshold in the bioassay. Compounds against targets with activity less than 100µM (a standard value 100,000nM in ChEMBL) was left. It's worth noting that information on the type of action of molecules on targets (activation or inhibition) is absent. In ChEMBL, the information on the type of action was only annotated for drugs. For non-drug molecules, there is no such annotations in activity data. Precision description for compound target interaction would be the further directions in the public chemical biology database constructions. The signal transduction pathways come from KEGG database21.



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Figure 1. TCMAnalyzer data components and data mining toolkit. To analyze TCM data, TCMAnalyzer employs algorithms, such as, substructure search, markush search, two and three dimensional

structure similarity

calculations,22 and molecular scaffold search.232D similarity calculations is based on the atom center fragment. 3D similarity adopts the weighted gaussian algorithm (WEGA) for molecular shape similarity calculation, an enhanced Gaussian-sphere model based molecular shape comparison methods. Both methods used Tanimoto coefficient as similarity function to quantify the similarity between two molecules. More details of algorithms can be seen in our previous publications.22,

24

Gene

ontology enrichments analysis for TCM-targets relation studies can be conducted ACS Paragon Plus Environment

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through David web service, which is an online bioinformatics tool for protein or gene functional annotation.25,

26

The relations among natural product molecules (small

molecular ligands), putative protein targets, pathways, and diseases can be rendered in a form of an interactive network. Webserver implementation. TCMAnalyzer is a publicly accessible platform, which can be accessed through a web browser using the browser/server framework. Marvin JS is used as molecular structure editor. ChemDoodle web component is used as a chemical structure viewer facility.27 TCMAnalyzer accepts complete structure query, substructure query, scaffold query, and two- or three-dimensional structure similarity queries for a user to identify desired natural product molecules from TCM herbs. TCMAnalyzer results can be rendered in a form of an interactive network map. The detailed information can be found in Table 1. Table 1. The toolkits for creating TCMAnalyzer. Tools

Purpose

Chinese Pharmacopoeia prescriptions and TCM data source

Link wp.chp.org.cn/front/chpint/en

ChEMBL 22

targets and bioactive natural products www.ebi.ac.uk/chembl

KEGG

pathways data source

TCMSP

natural products data source of TCM lsp.nwsuaf.edu.cn/tcmsp.php

TCMID

natural products data source of TCM www.megabionet.org/tcmid

DAVID

go enrichment analysis

ChemDoodle Web

structure display of natural products web.chemdoodle.com

Vis.js

network display

visjs.org

MySQL

storage database

www.mysql.com

Golang

web server language

golang.org

www.kegg.jp

david.ncifcrf.gov

Results and discussion Data statistics. The number of prescriptions, TCM, structures of ingredients, targets, and diseases are listed in Table 2. TCMAnalyzer contains 1,493 prescriptions and 618 TCMs manually curated from Chinese Pharmacopoeia (2015). TCMAnalyzer also concludes 16,437 Structures of ingredients, 2,298 targets, and 1,529 diseases derived from public databases. All those data entries are the foundations for further data



mining and analysis. Table 2. Data statistics of TCMAnalyzer. Content

Entries

Data Source

Prescriptions

1,493

Chinese Pharmacopoeia(2015)

TCM

618

Chinese Pharmacopoeia(2015)

Structures of ingredients

16,437

TCMSP, TCMID

Targets

2,298

ChEMBL, TTD, UniProt

Diseases

1,529

TTD

Web interface. TCMAnalyzer can be accessed through web browsers. The “analysis” page allows a user to input a query, which includes query structure, single or multiple herbs, prescription, one or more protein targets, and disease. The analysis can be divided into three modules, structure mining module, TCM MOA illustration module, and targeted bioactive herb exploration module. In structure mining module, user can draw a molecule or a fragment of interest in Marvin JS. As shown in Figure 2A, five commonly used structure query methods are supported, namely full-structure search, substructure search, scaffold search, 2D similarity search, and 3D similarity search. In TCM MOA illustration module, as shown in Figure 2B, user can select single herb, multiple herbs, or a prescription. Chinese name, Pinyin name and English name are supported for herbs. Chinese name and Pinyin name are supported for prescription. In targeted bioactive herb exploration module, as shown in Figure 2C, user can input a target, multiple targets, or a disease. The target name, UniProt ID, and gene name are supported for target. After a query is composed, the user can submit the task, the results will be depicted in the result page for inspection. The result page consists of prescription, TCM, molecule, target, disease, and pathway fields. Each result table can be sorted based upon the values of a non-chemical-structure field. A result table can be exported in a format such as JSON, XML, CSV, TXT, SQL, or EXCEL. For molecular data in the result page, compound list can be inspected in a grid view and, exported in SDF


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format. The result page can be rendered in a form of a network. The user can do this by clicking a button at the upright of the page to inspect the interactive network. The user can also set various parameters in the right panel to operate the network to gain the best output. The network picture can be exported in an image format. “My data” page allows a user to upload user’s data, and a unique data ID will be generated for being accessed in the future. “News page” reports the important progress on TCM or natural products. The data and detailed information can be found in the help page.



Figure 2. Analysis modules in TCMAnalyzer. (A) Structure mining module. (B) TCM MOA illustration module. (C) Targeted bioactive herb exploration module. ACS Paragon Plus Environment

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Case study 1: MOA illustration of TCM. Radix Sophorae Flavescentis and Fructus Cnidii form a classic antipruritic herbal pair in traditional Chinese medicine (TCM). Inputing Sophorae Flavescentis Radix and Cnidii Fructus in module 2 resulted in a new

page

(http://www.rcdd.org.cn/tcmanalyzer/result.html?JobId=L03BFB5979G5G344R7IS), as shown in Figure 3. The new page consists of six fields: prescription, TCM, molecule, target, disease, and pathway. We can go through the basic information of the submitted herbs and the pharmacological network after clicking the “view the network” button. The generated TCM-Chemicals-Targets network suggested RSF and FC share natural products and interact various targets, which show potential synergistic anti-inflammatory effect. This has been experimental validated through an inhibition effect of crude extract (20mg/ml) in LPS-induced NO production in RAW264.7 (Figure 4).

Figure 3. TCM information and the brief network.



Figure 4. Synergistic effect of Radix Sophorae Flavescentis (RSF) and Fructus Cnidii (FC) herb pair. A) Radix Sophorae Flavescentis, B) Fructus Cnidii, C) Synergistic effect of RSF and FC herbal pair inhibition of LPS-induced NO production in RAW264.7. For a better view, a detailed and interactive network was created after click at the upright button. As shown in Figure 5, a user can change the various parameters by setting parameters at the control panel on the right side of the page to modify the network. The user can also click the node to get detailed information of the molecule, TCM, target, and disease. Finally, the network can be exported as an image with different formats.


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Figure 5. The interactive TCM-molecule-target-disease network. From the output results, there are 34 prescriptions (Figure S1) containing Sophorae Flavescentis or Fructus Cnidii. 337 molecules (Figure S2) were identified from the herb pair, and 108 targets (Figure S3) are related with those molecules. 204 diseases (Figure S4) are related with those targets, and those targets can be functional enriched to 86 pathways (Figure S5).

Sorting with count, among the 86 pathways,

we can see that most pathways are related with cancer. These results are consisted with the experimental results, in which RSF shows an anti-tumor activities.28,

29

Meanwhile, NF-kappa B signaling pathway (hsa04064) and TNF signaling pathway (hsa04668) were identified, which are closely relative with inflammation. Those results illustrate a potential mechanism of action for RSF and FC herbal pair. Case study 2: Structure mining quinoline scaffold. TCMAnalyzer employs various structure mining tools and can be easily find the molecules with specific scaffold and their TCMs. Draw quinoline scaffold in MarvinJS of analyze page, and as shown in Figure 6, TCMAnalyzer find 8 molecules and existed in 7 herbals. This illustrate the distribution of natural products with specific functional scaffold. Those herbs could be important resources of the natural products.



Figure 6. Structure mining quinoline scaffold.


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Conclusions TCM modernization requires understanding the relations among natural product molecules (small molecular ligands), putative protein targets, pathways, and diseases. TCMAnalyzer is developed to meet this need. With TCMAnalyzer, a user can explore the molecular mechanism of actions for a TCM herb at systemic level, and elucidate potential ingredients for a TCM herb. The result can be depicted in an interactive network, which demonstrates the relations among natural product molecules (small molecular ligands), putative protein targets, pathways, and diseases.

Supporting Information The Supporting Information is available free of charge on the ACS Publications website. Figure S1. Prescription information. Figure S2. Molecule information. Figure S3. Target information. Figure S4. Disease information. Figure S5. Pathway information. Acknowledgement This work was supported by the National Science Foundation of China (81473138, 81573310, 81703416), Guangdong Province Science and Technology Planning Project (2016A020217002), Guangdong Province Frontier and Key Technology Innovation Program (2015B010109004), Guangdong National Science Foundation (2016A030310228). Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (the second phase) under Grant No.U1501501. Conflict of Interest: none declared.

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