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Modeling compound-target interaction network of Traditional Chinese Medicines for type II diabetes mellitus: insight for polypharmacology and drug design Sheng Tian, Youyong Li, Dan Li, Xiaojie Xu, Junmei Wang, Qian Zhang, and Tingjun Hou J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/ci400146u • Publication Date (Web): 16 Jun 2013 Downloaded from http://pubs.acs.org on June 18, 2013

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Journal of Chemical Information and Modeling

1 2

Modeling compound-target interaction network of

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Traditional Chinese Medicines for type II diabetes mellitus:

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insight for polypharmacology and drug design

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Sheng Tiana, Youyong Lia, Dan Li,b Xiaojie Xuc, Junmei Wangd, Qian Zhanga,

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Tingjun Houa,b*

8 a

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Institute of Functional Nano & Soft Materials (FUNSOM) and Jiangsu Key Laboratory for

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Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123,

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China b

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College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China

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c

College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China d

Department of Biochemistry, The University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX 75390

16 17 18 19

Corresponding author:

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Tingjun Hou

21

E-mail: [email protected]

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Phone: +86-512-65882039

or

[email protected]

23 24

Keywords: Type II diabetes mellitus; Traditional Chinese Medicines; Drug-target

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interaction network; Molecular docking; Pharmacophore mapping; Naïve Bayesian

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classifier; Polypharmacology

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ACS Paragon Plus Environment


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For Table of Contents Use Only

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Abstract

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In this study, in order to elucidate the action mechanism of Traditional Chinese

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Medicines (TCM) that exhibit clinical efficacy for type II diabetes mellitus (T2DM),

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an integrated protocol that combines molecular docking and pharmacophore mapping

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was employed to find the potential inhibitors from TCM for the T2DM-related targets

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and establish the compound-target interaction network. First, the prediction

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capabilities of molecular docking and pharmacophore mapping to distinguish

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inhibitors from non-inhibitors for the selected T2DM-related targets were evaluated.

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The results show that molecular docking or pharmacophore mapping can give

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satisfactory predictions for most targets but the validations are still quite necessary

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because the prediction accuracies of these two methods are variable across different

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targets. Then, the Bayesian classifiers by integrating the predictions from molecular

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docking and pharmacophore mapping were developed, and the well-validated

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Bayesian classifiers for 15 targets were utilized to find the potential inhibitors from

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TCM and establish the compound-target interaction network. The analysis of the

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compound-target network demonstrates that a small portion (18.6%) of the predicted

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inhibitors can interact with multi-targets. The pharmacological activities for some

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potential inhibitors have been experimentally confirmed, highlighting the reliability of

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the Bayesian classifiers. Besides, it is interesting to find that a considerable number of

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the predicted multi-target inhibitors have free radical scavenging/antioxidant activities,

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which are closely related to T2DM. It appears that the pharmacological effect of the

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TCM formulae is determined not only by the compounds that interact directly with

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one or more T2DM-related targets, but also by the compounds with other

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supplementary bioactivities important for relieving T2DM, such as free radical

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scavenging/antioxidant effects. The mechanism uncovered by this study may offer a

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deep insight for understanding the theory of the classical TCM formulae for

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combating T2DM. Moreover, the predicted inhibitors for the T2DM-related targets

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may provide a good source to find new lead compounds against T2DM.

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Introduction

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Diabetes mellitus, or simply diabetes, is a group of metabolic disorder diseases.

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The diabetes patients have high blood glucose, because either the body system cannot

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produce enough insulin (absolute insulin deficiency) or the related cells are not

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sensitive to or respond to insulin (insulin resistance or relative insulin deficiency).

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Diabetes mellitus has become a serious health problem around the world. Diabetes

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mellitus is classified into three categories: type 1, type 2 and gestational diabetes.

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Type 1 diabetes mellitus (T1DM) is characterized by the loss of the insulin-producing

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beta cells of the islets of langerhans in the pancreas, leading to absolute insulin

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deficiency. Type 2 diabetes mellitus (T2DM) is characterized by insulin resistance,

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which may be combined with relatively reduced insulin secretion. The last one,

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gestational diabetes mellitus (GDM), which occurs in about 2~5% of all pregnancies,

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resembles T2DM in several respects, involving a combination of relatively inadequate

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secretion and responsiveness. Globally, it is estimated that 285 million people have

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diabetes mellitus, with T2DM making up about 90% of the cases in 2010.1 Its

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incidence is increasing rapidly, and by 2030, this number is estimated to be almost

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double.2

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T2DM is a chronic disease, associated with a ten-year shorter life expectancy,

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caused by a number of complications including cardiovascular, eyes, kidney and

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nervous system diseases.3-6 Most T2DM patients need the medication to reduce their

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blood glucose to near-normal level.7 To control the high-level blood glucose and

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prevent the diabetic complications, many drugs, such as alpha-glucosidase inhibitors,

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sulfonylureas, metformin, thiazolidinediones (TZDs) and insulin injections, have been

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used to treat T2DM patients.7 To our disappointment, all of these therapeutic agents

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have limited efficacy and are associated with undesired side effects, including

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hypoglycemia, weight gain, gastrointestinal disturbances, edema, anemia, lactic

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acidosis, etc.8, 9 Due to the deficiencies of marketed drugs for T2DM, the philosophy

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of rational drug design, or more specifically, the “one gene, one drug, one disease”

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paradigm for treating T2DM was met with controversy. 4


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Over the past decade, the number of new molecular entities (NME) approved by

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US Food and Drug Administration (FDA) did not change significantly. The reasons

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that have been proposed for this discouraging situation may not be technological,

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environmental or even scientific but philosophical.10 Also, it has been proven that

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many effective drugs act on multiple rather than one specific disease-related target.11

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It seems that the “magic bullets” cannot cure some specific diseases as expected.

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The new drug candidates with low efficacy and undesired ADME/T properties are

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not the only factor to debate the traditional paradigm for drug design/discovery.12, 13

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The phenotype usually cannot be disturbed by single-gene knockouts14 due to

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redundant functions and alternative compensatory signaling pathways.10,

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intrinsic robustness of living systems against various perturbations is an essential

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factor that hinders such magic bullet drugs to be successful in the drug market.16

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The

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Compared with the traditional single-target-drug paradigm, the new philosophy

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for drug design known as polypharmacology or network pharmacology has attracted

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more attentions.10, 17-19 Therefore, it has been realized that designing drug candidates

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to target a disease-associated network rather than a single target may be a better

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strategy to find better drug candidates for some complicated diseases, such as T2DM.

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It is estimated that approximated 34% of new drugs were directly or indirectly

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from natural products from 1981 to 2010.20 As an important source of natural products

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in clinical use, the Traditional Chinese Medicines (TCM) has played an important role

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in healthcare system of Chinese people for more than several thousand years. Because

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of satisfactory in vivo efficacy and safety of TCM in targeting complicated chronic

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diseases,21 finding potential drug candidates from TCM may be a good practice.22, 23

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The main philosophical theories of TCM are based on Yin-Yang, Five Elements,

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human body meridian systems and Zang Fu theory.24 It is well known that the basic

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form of TCM for combating diseases is TCM formula (or prescription), which is the

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mixture of special herbs with suitable dosages. A TCM formula consists of a large

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number of chemical compounds, which may interact with multi-targets. Therefore, at

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the molecular level, the mechanisms of TCM formulae may be explained by 5



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polypharmacology or network pharmacology.

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Compared with the time-consuming and costly experimental methods to uncover

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the action mechanism of TCM for combating diseases, the use of computational

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approaches may provide an easier and more practical way to understand the

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mysterious power of TCM.25-27 Previous studies suggest that molecular modeling

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techniques, especially molecular docking and pharmacophore mapping, not only can

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find potentially active compounds from TCM for targeting disease-related proteins,

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but also can identify potential targets for compounds from TCM.28-34 However, it

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seems that most previous studies just simply used computational approaches to find

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potential inhibitors from TCM or even establish the compound-target networks to

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explore the polypharmacology of TCM. It should be noted that the validations of the

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computational approaches is quite critical to guarantee the reliability of the

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predictions for TCM given by the computational approaches.

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Through thousands of years of medical use for various diseases in the Chinese

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medical system, a large amount of knowledge has also been accumulated for diabetes

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therapy that is termed as “XiaoKeZheng”.24, 35 Similar to the combination therapy of

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multi-component drugs, the multi-components from TCM can interact with

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multi-targets through an integrated way to lowing blood glucose or relieving

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diabetes.21, 36 However, we still know little about how the active compounds in the

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TCM formulae modulate the network for combating diabetes. Therefore, in this study,

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we tried to establish the compound-target interaction network by theoretical

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approaches and uncover the underlying action mechanisms of TCM for T2DM. To

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make our predictions more reliable, the capabilities of molecular docking and

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pharmacophore mapping to discriminate the known inhibitors from the non-inhibitors

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for each T2DM-related target were assessed. To integrate the advantages of molecular

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docking and pharmacophore modeling, the naïve Bayesian classifiers were developed

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by combining the predictions from molecular docking and pharmacophore mapping

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together. The combined models show better performance than those only based on the

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predictions from molecular docking or pharmacophore mapping. Then, the 6


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well-validated Bayesian classifiers were utilized to find potentially active compounds

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in TCM that can interact with the important T2DM-related targets. Finally, the

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compound-target interaction network was established to uncover the action

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mechanisms of the classical TCM formulae for relieving T2DM.

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Methods and Materials

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Collection of the T2DM-related targets

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By searching the public data sources, including Therapeutic Targets Database

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(TTD)37, Drugbank38, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway

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database39 and previous reported literatures31,

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T2DM were identified and the available crystal structures of the protein-ligand

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complexes for these targets were retrieved from the RCSB protein data bank41. For

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each target, the crystal structure with the highest resolution was reserved if multiple

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structures were available, and finally a non-redundant set of 48 crystal complexes for

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the T2DM-related targets was obtained. It is necessary to point out that almost all of

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the T2DM-related targets may have several synonyms because different standards

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might be used in different TCM-related databases.37 For example, the corticosteroid

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11-beta-dehydrogenase isozyme 1 also can be named as 11-Beta hydroxysteroid

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dehydrogenase 1, 11-beta-HSD1, 11HSD1, and so on.37 The specific target with

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different names should be check carefully. The names, PDB entries and the

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corresponding crystal resolutions for the selected protein-ligand complexes are

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summarized in Table S1 in the Supporting Materials.

40

, the important targets related to

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Collection of the compounds in the T2DM-related TCM formulae

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By extensive data profiling, 52 classical TCM formulae with clinical efficacy for

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T2DM were compiled. There are 95 Chinese herbs in those 52 TCM formulae. The

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frequency of each herb was counted, and only the 32 Chinese herbs with the

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frequency to 5 or higher were considered in our study (Table S2 in the Supporting

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Materials). The Latin names for the 32 studied Chinese herbs were collected from the 7



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latest version of TCMCD developed in our group42-45 and [email protected]

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It should be noted that multiple Latin names might be available for the same Chinese

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herb. The reason is that different standards may be used in different TCM databases.

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For example, the herb “Cang Zhu” in Chinese name is named as Latin name

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Atractylodes lancea in TCMCD and Atractylodes lancea (Thunb.) DC. in

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TCM-Database@Taiwan (Table S2 in the Supporting Materials). A total of 2,479

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non-duplicated compounds isolated from these 32 Chinese herbs were found from the

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TCMCD

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standardized and optimized by using Discovery Studio 3.1 (DS3.1) molecular

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simulation package.47

and

TCM-Database@Taiwan

databases.

These

compounds

were

198 199

Preparation of the validation datasets

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The validation dataset for each T2DM-related target was prepared to evaluate the

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prediction accuracies of molecular docking or pharmacophore mapping to distinguish

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known inhibitors from non-inhibitors. The known inhibitors were obtained from the

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BindingDB database.48 The number of the inhibitors for each target is listed in Table

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S3 in the Supporting Materials. To ensure the statistical significance of the validation

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results, the targets with the number of the known inhibitors less than 10 are excluded.

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Besides, in order to achieve acceptable computational efficiency, when the number of

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the known inhibitors for a target is higher than 500, only 500 randomly-selected

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inhibitors

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experimentally-validated non-inhibitors are quite limited, the compounds were

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randomly chosen from the Chembridge database to serve as non-inhibitors for each

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target.49 The inhibitors and non-inhibitors were compared and the duplicates were

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removed from the non-inhibitor subset. To mimic the unbalanced nature of inhibitors

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versus non-inhibitors, we chose to set the ratio of non-inhibitors versus inhibitors to

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20. By applying the processing protocol mentioned above, the validation datasets for

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the 33 T2DM-related targets were generated (Table S3 in the Supporting Materials).

were

included

in

the

validation

dataset.

216 8


Then,

because

the

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Validation of molecular docking-based virtual screening

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The crystal structures of the protein-ligand complexes for the 33 T2DM-related

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targets were used for the docking calculations performed with Glide50 in Schrodinger

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9.0.51 For each crystal structure, the crystallographic water molecules were removed,

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the missing hydrogen atoms were added, and the protonated states and partial charges

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were assigned with the OPLS-2005 force field52. Minimizations were performed until

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the average root mean square deviation of non-hydrogen atoms reached 0.3 Å.

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Preparation and refinement were accomplished with the protein preparation wizard in

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Schrodinger 9.051.

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The validation datasets were processed using the LigPrep module in

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Schrodinger51. For each small molecule, the possible ionization states were generated

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by using Epik at pH=7.0. Because the non-inhibitors randomly chosen from

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Chembridge do not have 3D structural information, all possible stereoisomers for

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each molecule were generated. All of the other parameters were kept as the default

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settings in LigPrep.

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Then, the bounding box of size 10 Å × 10 Å × 10 Å was defined for each target and

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centered on the mass center of the co-crystal ligand to confine the mass center of the

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docked ligand by using the Receptor Grid Generation function of Glide.51 According

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to our docking results, the size of the bounding box is big enough for almost all of the

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ligands to be docked into the binding site successfully for each target. The default

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Glide settings were used for grid generation. All of the compounds were docked into

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the binding site of the studied targets and scored by the Standard Precision (SP) and

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Extra Precision (XP) scoring modes of Glide.51 Five thousand poses per ligand were

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generated during the initial phase of the docking calculation, and out of which the best

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400 poses were chosen for energy minimization. Dielectric constant of 2.0 and 100

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steps of conjugate gradient minimizations were used in energy minimization stage.

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The performances of the SP and XP scoring modes of Glide on the 33 T2DM-related

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targets were evaluated and compared.

245 9



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Validation of pharmacophore-based virtual screening

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The low-energy conformations of the molecules in the validation datasets were

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generated by using the Conformation Generation protocol in DS3.147 with the FAST

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setting and the number of the maximum conformations per compound was set to 100.

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Then, the receptor-ligand interaction patterns (or pharmacophore features) for each

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target was generated by using the Receptor-Ligand Pharmacophore Generation

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(RLPG) protocol in DS3.1.47 A set of predefined pharmacophore features that include

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hydrogen bond acceptor/donor (HBA/HBD), hydrophobic (HYD), negative/positive

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ionizable (PI/NI) and ring aromatic (RA) of the ligand are identified for each

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receptor-ligand complex firstly and then pruned when not satisfying the protein-ligand

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interactions using adjustable topological rules.53

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The RLPG protocol in DS3.1 was applied to the receptor-ligand complexes for the

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33 T2DM-related targets. For each pharmacophore model, the minimum number of

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the pharmacophore features was set to 3, and the maximum number of the

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pharmacophore features was same to the number of the total features that can match

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the receptor-ligand interactions. Up to ten pharmacophore models per receptor-ligand

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complex were built and ranked by the selectivity as the fault settings in DS3.1. The

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selectivity of a pharmacophore model was evaluated by a rule-based score function

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based on the genetic function approximation (GFA) technique54. The details of the

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GFA model for pharmacophore modeling were described in the previous study.55

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Besides, the discrimination power of a pharmacophore model was determined by the

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capability to distinguish known inhibitors from non-inhibitors. The discrimination

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capability of a pharmacophore model is more important than the selectivity for virtual

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screening. An ideal pharmacophore model used in virtual screening must have the

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ability not only to find the most promising active compounds, but also to abandon the

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inactive compounds. The discrimination capability of each pharmacophore model for

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the corresponding validation set was evaluated.

273 274

Generation of the molecule-target interaction network 10


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As mentioned above, the performance of molecular docking or pharmacophore

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modeling for each target was validated. The validation process aiming to distinguish

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the known inhibitors from non-inhibitors for each target is a binary classification

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problem. Recently, the naïve Bayesian classification technique was successfully

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applied to different classification problems by our group.45,

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classification can process large amounts of data, learn fast, and is tolerant of random

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noise. It only requires a small amount of training data to estimate the parameters

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(means and variances of the variables) necessary for classification. Moreover, in the

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scheme of naïve Bayesian classification, the belonging of a compound in which class

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can be qualitatively evaluated by the posterior probability. The mathematical

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procedure to train a naive Bayesian classifier was described previously.45, 56, 57 To

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integrate the advantages of molecular docking and pharmacophore mapping, each

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compound in the validation datasets was docked into the binding site firstly and then

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mapped onto the pharmacophore model for each target. The docking scores given by

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molecular docking and the fit values given by pharmacophore mapping were used as

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the independent variables to train the naïve Bayesian classifiers.

56, 57

Naïve Bayesian

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After rigorous validation, the naïve Bayesian classifiers with reliable predictions

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for 15 targets were utilized to virtually screen the TCM compound dataset. First, The

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2,479 non-duplicate compounds from 32 Chinese herbs of the TCM formulae for

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T2DM (Table S2 in the Supporting Materials) were processed using the LigPrep

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module in Schrodinger.51 Then, those compounds were docked into the binding sites

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of the T2DM-related targets by using Glide in Schrodinger 9.0 and scored by the SP

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and XP scoring modes. Next, each compound was mapped onto the pharmacophore

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model with highest discrimination capability for each target. Based on the calculations

299

from molecular docking and pharmacophore mapping, each compound was predicted

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by the reliable naïve Bayesian classifiers for 15 T2DM-related targets.

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By applying the Bayesian classifiers, the potential inhibitors for each target were

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determined. In order to balance the prediction accuracy and the complexity of

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interaction network, only the top 10% of the predicted inhibitors ranked by decreasing 11



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304

the Bayesian scores were extracted to generate the compound-target interaction

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networks by using the Osprey network visualization program (version 1.2.0).58

306 307

Results and Discussion

308

The performance of molecular docking-based ligand profiling

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Before applying molecular docking to screen the TCM compound dataset (Table

310

S1 in the Supporting Materials), the performance of molecular docking to distinguish

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inhibitors from non-inhibitors for 33 T2DM-related targets (Table S2 in the

312

Supporting Materials) was evaluated.

313

First, as a representative measurement for the accuracy and reliability of

314

molecular docking, the “docking power” was examined.59 The co-crystallized ligands

315

were extracted and then re-docked into the binding sites. The root-mean-squared

316

deviation (RMSD) of the docked pose from the original position for each complex

317

was computed to evaluate whether the Glide docking is reliable for virtual screening.

318

The RMSD was used as the criterion for the success of the prediction. If RMSD of the

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docked pose was less than or equal to 2.0 Å from the experimentally observed

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conformation, we considered it to be a successful prediction. As shown in Table S3 in

321

the Supporting Materials, the SP and XP scoring modes can successfully recognize

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the near-native conformations for 25 and 26 of the entire 33 complexes, i.e. the

323

successful rate are 75.8% and 78.8%, respectively. In addition, only four complexes

324

that do not meet the criterion of RMSD ≤ 2.0 Å by using either SP or XP scoring

325

mode (Table S3 in the Supporting Materials). From this data, it is clear that for most

326

complexes the Glide docking can successfully recognize the correct binding

327

conformations.

328

Then, we evaluated the “discrimination power” of molecular docking to

329

distinguish known inhibitors from non-inhibitors for each target. The student’s t-test

330

was employed to evaluate the significance of the difference between the means of the

331

two distributions of the Glide SP or XP scores for the known inhibitor and

332

non-inhibitor classes (Table S3 in the Supporting Materials). On the whole, the 12


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molecular docking calculations for most targets give satisfactory results, indicated by

334

the quite low P-values associated with the difference in the mean SP or XP scores of

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the inhibitors versus those of the non-inhibitors. The P-values for 25 of 33 targets

336

(76%) are lower than 10-20, suggesting that molecular docking can successfully

337

discriminate the known inhibitors from non-inhibitors for most targets. In other words,

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the Glide docking can be used as a reliable tool to identify known inhibitors from

339

diverse chemical space for each target. The distributions of the Glide SP or XP scores

340

for four targets with good predictions and four targets with poor predictions are

341

illustrated in Figures 1a and 1b as examples.

342

Generally speaking, the XP scoring (extra precision) is believed to be better than

343

the SP scoring (standard precision). However, it is interesting to observe that the XP

344

scoring of Glide does not always show better predictions than the SP scoring. As

345

shown in Table S3 in the Supporting Materials, the XP scoring only shows better or

346

equal discrimination power for 15 (45.5%) targets according to the measurement of

347

the P-values. The results suggest that the XP scoring does not always yield better

348

performance than SP. Therefore, the comparison study of the performance between

349

the SP and XP scoring modes of Glide is quite necessary for a specific target. For each

350

target, based on the comparison results, more reliable scoring mode was chosen for

351

the following virtual screening of the TCM compound dataset.

352

In summary, the Glide docking based on the SP or XP scoring mode can

353

successfully recognize the near-native conformations for 25 or 26 of the entire 33

354

complexes. It is indicated that the Glide docking is suitable for our studies. Moreover,

355

it can successfully discriminate the known inhibitors from non-inhibitors for 25 of 33

356

targets, proving the reliability of molecular docking for most targets. Ideally, if the

357

results of docking can meet both of the two requirements: RMSD ≤ 2.0 Å and P-value

358

≤ 10-20, the docking-based virtual screening is reliable.

359

The reason of the failure of the docking calculations for some targets lies in the

360

fact that the performance of scoring function used by Glide is often varied across

361

different protein families.59,

60

For example, for HMG-CoA reductase, six crystal 13



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

362

complexes can be retrieved from PDB database (Table S4 in the Supporting

363

Materials). According to our analysis, there is only one complex (1DQA) can meet the

364

demand of RMSD (≤ 2.0 Å) by using the SP mode of Glide. Meanwhile, the

365

capability of distinguishing the known inhibitors from non-inhibitors for 1DQA

366

complex is not good, indicated by the high P-value (1.13×10-6) associated with the

367

difference in the mean SP scores of the known inhibitors versus that of the

368

non-inhibitors (Figure S1 in the Supporting Materials). That is to say, all of the

369

docking calculations by using the SP and XP scoring modes for HMG-CoA reductase

370

are questionable either duo to unacceptable RMSD or poor discrimination capacity. In

371

most previous application of molecular docking on the TCM studies, the capabilities

372

of molecular docking on the studied systems were not evaluated, and therefore, the

373

reported results may be questionable. According to our calculations, it is strongly

374

recommended that the validation process is necessary before any docking study for

375

TCM.

376 377

The performance of pharmacophore-based ligand profiling

378

Based on the 33 receptor-ligand complexes of the T2DM-related targets (Table S5

379

in the Supporting Materials), 28 complex-based pharmacophore models were

380

generated (Table S5 in the Supporting Materials), and no pharmacophore model can

381

be generated for 5 complexes. There are two possible reasons for explaining the

382

failures for these 5 complexes：(1) the number of extracted pharmacophore features is

383

quite limited (less than 3), and (2) some pharmacophore features extracted from the

384

receptor-ligand complex do not comply with the minimum feature distance criterion

385

(< 1.0 Å).

386

For each complex, the generated pharmacophore models were ranked by the

387

selectivity scores (Table S5 in the Supporting Materials). Then, the prediction power

388

of each pharamcophore model to distinguish the known inhibitors from non-inhibitor

389

in the validate dataset was evaluated. The discrimination power of a pharmacophore

390

model was quantitatively characterized by the AUC area under a receiver operating 14


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391

characteristics (ROC) curve. All pharmacophore features derived from the

392

receptor-ligand interactions (Total features), the pharmacophore features for the most

393

selective pharmacophore model (Feature set A) and those for the pharmacophore

394

model with the highest AUC value (Feature set B) are listed in Table S5 in the

395

Supporting Materials. The comparison of the numbers of the pharmacophore features

396

for 25 targets among Total features, Feature set A and Feature set B are shown in the

397

Figure 2a and listed in Table S6 in the Supporting Materials. We can observe that the

398

numbers of Total features are generally more than those of Feature set A, and the

399

numbers of Feature set A are also more than those of Feature set B for the same

400

complex (Table S5 in the Supporting Materials) for most cases. Besides, the

401

percentage of the number of the pharmacophore features that are more than five for

402

Total features, Feature set A and Feature set B are 71.4% (20/28), 64.3% (18/28) and

403

32.1% (9/28), respectively.

404

It is expected that a pharmacophore model with more pharmacophore features

405

should be more selective than that with less pharmacophore features. According to our

406

analysis, the numbers of Feature set A for most selective pharmacophore models are

407

not always equal to those of Total features. If the number of Total features derived

408

from a complex is n, the numbers of Feature set A for most selective pharmacophore

409

models determined by GFA are usually equal to n (14/28) or n-1 (11/28), except n-2

410

for PPAR-γ, PTP1B and estrogen receptor. The results demonstrate that some

411

pharmacophore features derived directly from the protein-ligand interactions have no

412

effect on the identification of the known inhibitors from chemical space. Meanwhile,

413

the numbers of Feature set B of the pharmacophore models that have the highest

414

discrimination capacity determined by the AUC values are usually n-1 (11/28) or n-2

415

(11/28), except n for angiotensin-converting enzyme, insulin-like growth factor 1

416

receptor and mitogen-activated protein kinase 1, n-3 for PPAR-γ, PTP1B and estrogen

417

receptor. By comparing the pharmacophore features between Feature set A and

418

Feature set B, it was found that the numbers of the pharmacophore models with

419

Feature set B equaling to, one less than, and two less than those in Feature set A are 15



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

420

21.4% (6/28), 64.3% (18/28) and 14.3% (4/28), respectively.

421

In summary, the pharmacophore models with Feature set B determined by the

422

validation datasets are more reasonable to be used than those with Feature set A

423

determined by GFA (Table S5 in the Supporting Materials and Figure 2b). If AUC

424

higher than 0.70 was used as the criterion to judge a successful prediction, 5 (18%) of

425

the most selective pharmacophore models yield acceptable predictions while 15 (55%)

426

of the pharmacophore models with the best discrimination capabilities yield

427

acceptable predictions. The most selective pharmacophore model was optimized by

428

GFA, but it should be cautiously used in virtual screening when there are no enough

429

known inhibitors for the validation. The aim of virtual screening is to find more

430

promising compounds (true positives) and avoid false positives from the huge

431

chemical space. The most distinguishing pharmacophore models, which are validated

432

by the validation dataset, are more suitable to be used in structure-based virtual

433

screening. Obviously, the validation process before any pharmacophore modeling

434

study for TCM is quite necessary.

435 436

The Bayesian classifier by combining the predictions from molecular docking

437

and pharmacophore mapping

438

As mentioned above, molecular docking gives good predictions for 21 targets, and

439

pharmacophore mapping gives acceptable predictions for 15 targets. Both of

440

molecular docking (RMSD ≤ 2.0 Å and P-value ≤ 10-20) and pharmacophore mapping

441

(AUC ≥ 0.7) give good predictions for 8 targets, and five targets cannot be well

442

predicted by either molecular docking or pharmacophore mapping. However,

443

molecular docking or pharmacophore mapping can give relatively reliable predictions

444

for 28 T2DM-related targets (28/33=84.8%). It must keep in mind that the virtual

445

screening based on molecular docking or pharmacophore mapping is a binary

446

classification problem. Then, we may raise the following question: could the

447

combination of these two structure-based methods enhance the prediction? For each

448

molecule, the docking score and fit value are the quantitative measurements for 16


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449

molecular docking and pharmacophore mapping, respectively. Therefore, the naïve

450

Bayesian classifiers were built by using the docking scores and fit values as the

451

independent variables for 24 targets with reliable predictions of molecular docking

452

and/or phamacophore mapping. Three criteria should be satisfied for the chosen

453

targets: (1). the number of known inhibitors should be equal to or higher than 10; (2).

454

the native-like conformation of the ligand should be reproduced by molecular docking

455

(RMSD ≤ 2.0 Å); (3). reasonable pharmacophore model should be generated. In order

456

to compare the prediction capacity among molecular docking, pharmacophore

457

modeling and the combination of the predictions from both, the AUC values with

458

5-fold cross validation were employed to measure the performance of the naïve

459

Bayesian classifiers (Table 1 and Figure 3). According to statistical results shown in

460

Table 1, we observe that the AUC values of the Bayesian classifiers based on the

461

docking scores and fit values are higher than those only based on the docking scores

462

or fit values for almost all targets (except PTP1B). For example, for insulin-like

463

growth factor 1 receptor (IGF-1R), the AUC values for the Bayesian classifier based

464

on the fit values and that based on the docking scores are 0.729 and 0.788,

465

respectively, while the AUC value for the Bayesian classifier based on both the fit

466

values and docking scores is 0.904 (Figure 4). The reason why the naïve Bayesian

467

classifier based on both fit values and docking scores for PTP1B cannot get the

468

improvement was also investigated. We found that only six known inhibitors could be

469

docked into the binding site of PTP1B and mapped onto the pharmacophore model of

470

the PTP1B complex, and the limited number of the PTP1B inhibitors could not

471

guarantee the statistical reliability of the validation results.

472

Here, the AUC values in the range of 0.5-0.6, 0.6-0.7, 0.7-0.8, 0.8-0.9 and 0.9-1.0

473

represent fail, poor, fair, good and excellent predictions for the Bayesian classifiers

474

(Table S7 in the Supporting Materials and Figure 3). The definition of AUC for

475

indicating the performance of prediction models is consistent with the definition used

476

by the Discovery Studio simulation package. The Bayesian classifiers built based on

477

docking scores or fit values can only give good or excellent predictions for 4 or 9 17



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

478

targets, while the combination of these two measurements can give good or excellent

479

predictions for 15 targets. Clearly, the combination of docking scores and fit values

480

can enhance the discrimination capacity.

481 482

The deep analysis of the potential active compounds for the T2DM-related

483

targets

484

As we discussed in the above section, for 15 targets (15/24=62.5%), the Bayesian

485

classifiers based on both docking scores and fit values give reliable predictions (AUC

486

≥ 0.8), and therefore, only the Bayesian classifier for these 15 targets were employed

487

to virtually screen the TCM compound dataset.

488

The numbers of the predicted inhibitors are listed in Table S8 in the Supporting

489

Materials. For protein-tyrosine kinase erbB-2, no potential inhibitor was found by the

490

Bayesian classifier. For protein-tyrosine kinase erbB-2, there are only ten inhibitors

491

for building and validating the Bayesian classifier. As shown in Figure S2 in the

492

Supporting Materials, these ten inhibitors almost have the same molecular framework

493

(Murcko framework).61 Therefore, the conservation of the target-inhibitor interaction

494

patterns may be unfavorable to generate a general prediction model for finding other

495

dissimilar compounds from the TCM compound dataset. Then, we investigated the

496

correlation between the number of the potential inhibitors and the volume and

497

hydrophobic property of the binding pocket for each target (Table S8 in the

498

Supporting Materials), and did not find obvious relationship between the number of

499

the potential inhibitors for each target and the volume or hydrophobic property of the

500

binding pocket.

501

It is well known that two similar compounds or two compounds that share similar

502

framework usually have similar pharmacological effects.62 Therefore, the similarities

503

between the potential inhibitors from TCM predicted by the Bayesian classifiers and

504

the known inhibitors for each target (Table S8 in the Supporting Materials) were

505

evaluated by the 2-D similarities (Tanimoto Coefficient) based on the MDLPubicKeys

506

fingerprints63 supported in DS3.1. The results are summarized in Table S9 in the 18


Page 18 of 46

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507

Supporting Materials. It is encouraging to find that a number of the potential

508

inhibitors are similar to the known inhibitors for 12 of 15 targets when the similarity

509

coefficient higher than 0.6 was used as the threshold. In addition, for insulin receptor

510

and estrogen receptor, 7 and 11 potential inhibitors have the similarity coefficient of

511

1.0 to the known inhibitors. For estrogen receptor, 9 of the 11 potential inhibitors are

512

the known inhibitors (Figure 5a), and the other 2 are quite similar to the known

513

inhibitors of estrogen receptor (Figure 5b).

514

It is interesting to find that all of the 8 compounds shown in Figure 5a were

515

directly isolated from natural products. Be specific, the compounds 2 (Emodin), 6

516

(Genistein) and 7 (Daidzein) were isolated from Polygonum cuspidatum

517

(polygonaceae),64 the compound 8 (Broussonin A) from Broussonetia Kazinoki,65 the

518

compound 4 (8-PN) from Humulus lupulus L.,66 the compound 3 (Coumestrol) from

519

plant-based natural molecules or derivatives,67 and the compounds 1 (Resveratrol)68

520

and 5 (Apigenin)69 also from natural plants. It should be noted that all of the 8

521

compounds are not only found in the natural plants mentioned above, but also found

522

in the Chinese herbs in the classical TCM formulae for treating T2DM. Furthermore,

523

for insulin receptor, we can also observe that 21 predicted inhibitors have the

524

similarity coefficients higher than 0.9 to the known inhibitors of insulin receptor

525

(Table S9 in the Supporting Materials). The structural analysis shows that 18 of the 21

526

molecules are quite similar to the known inhibitor, myricetin, of insulin receptor. The

527

myricetin and the 18 potential inhibitors similar to myricetin are shown in Figure 6.

528

Obviously, these 18 molecules share the same molecular framework (Murcko

529

framework)61 with myricetin and can be seen as the derivatives of myricetin.

530

In summary, our results imply that the Bayesian classifiers by combining the

531

predictions from molecular docking and pharmacophore mapping have exciting

532

capability to find promising inhibitors from TCM for the T2DM-related targets. Many

533

potential inhibitors predicted by the Bayesian classifiers are quite similar or even

534

identical to the known inhibitors. Moreover, by the 2D similarity analysis, we can

535

observe that some potential inhibitors are dissimilar (low similarity) to the known 19



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 20 of 46

536

inhibitors for most targets and they may be novel lead compounds for the studied

537

targets. Our observation can provide a deep insight for understanding the action

538

mechanism of TCM formulae for T2DM. The main mechanism of the TCM formulae

539

for treating T2DM may be similar to western drugs: the active compounds or

540

inhibitors in TCM can interact with the important disease-related targets, forming the

541

leading force for combating diabetes.

542 543

Analysis of the compound-target interaction network

544

A TCM formula usually consists of various herbs with numerous compounds to

545

exert potency towards diseases. The action mechanism of TCM is also termed as

546

“cocktails

547

multi-targets. Two possible mechanisms may be proposed for the pharmacological

548

effect of the TCM formulae for treating T2DM: (1). multi-active-components interact

549

with different T2DM-related targets to achieve synergic effect, (2). limited individual

550

components interact with multi-targets and most of them have activities against single

551

specific T2DM-related targets.

of

drugs”,

which

represents multi-components

interacting

with

552

After employing the Bayesian classifiers to virtually screen the TCM compound

553

dataset, 1,996 non-duplicated compounds were determined to be potential inhibitors

554

for 14 targets. Among these 1,996 compounds, 1,590 (74.38%) of them were

555

predicted to be drug-like by using the drug-likeness model developed in our group.45

556

The distributions of eight important molecular physicochemical properties, including

557

MW, PSA, AlogP, logD, logS, the number of hydrogen bond acceptors/donors and the

558

number of rotatable bonds, for these compounds are displayed in Figure S3 in the

559

Supporting Materials. In order to achieve more reliable predictions, only the top 10%

560

of the compounds (693) ranked by decreasing the Bayesian scores for each target

561

were chosen for the following analysis (Table S8 in the Supporting Materials). The

562

numbers of the predicted potential inhibitors for each target are listed in Figure 7.

563

It is interesting to find that there are a number of potential inhibitors that interact

564

with multiple targets (Figure 8 and Table S10 in the Supporting Materials). There are 20


Page 21 of 46

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565

519 non-duplicated predicted inhibitors against a single target, 94 against two targets,

566

28 against three targets, 8 against four targets, and only one against five targets. The

567

compound-target interaction networks are shown in Figure 9. As can be seen from

568

Figure 8, the number of the potential inhibitors decreases rapidly with the increase of

569

the number of targets. Apparently, most compounds in Chinese herbs have potential

570

activities against limited targets. As can be seen in Figure 9a, the whole

571

compound-target network has 519 nodes (potential inhibitors) and 696 edges

572

(interactions) with an average of 1.34 interactions per node. About 81.39% (564/693)

573

of the potential inhibitors only interact with one target. According to our analysis, we

574

can conclude that the mechanism of the TCM formulae may belong to the second

575

hypothesis: limited individual components interact with multi-targets and the others

576

only interact with the single specific T2DM-related targets.

577

We then investigated the potential multi-target inhibitors predicted by the

578

Bayesian classifiers. The similarities between the potential multi-target inhibitors and

579

the known inhibitors for each target were calculated. When the minimum similarity

580

coefficient based on the MDLPubicKeys fingerprint was set to 0.6, no similar known

581

inhibitor was found for these potential inhibitors for all of four and five targets, but

582

similar known inhibitors were successfully found for some potential inhibitors for all

583

of two and three targets.

584

For the only potential inhibitor (mirificin) against five targets (carbonic anhydrase

585

1, carbonic anhydrase 2, FBPase, glycogen synthase kinase-3 beta and insulin

586

receptor), similar known inhibitors for three of five targets (carbonic anhydrase 1,

587

carbonic anhydrase 2 and insulin receptor) could be found. For the 8 potential

588

inhibitors against four targets, similar known inhibitors for two or three of four targets

589

could be found. Besides, for the predicted inhibitors against two and three targets,

590

similar known inhibitors for all of two or three targets could be found. For examples,

591

the compound (Pureoside A) that was predicted to be a potential inhibitor of carbonic

592

anhydrase 1, PPAR delta and PPAR gamma, and similar known inhibitors for each

593

target were found and shown in Figure 10; two compounds (Epicatechin and 21



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

594

(2RS)-2-(4-O-β-D-glucopyranosylphenyl) propionyl β-D-glucopyranoside) that were

595

predicted to be potential inhibitors for two targets and similar known inhibitors were

596

found and shown in Figure 11.

597

In summary, our predictions suggest that most of the compounds extracted from

598

the Chinese herbs in TCM formulae can only interact with a single specific target.

599

Although the theory of TCM formulae is multi-compounds for multi-targets,

600

according to our analysis, it is obvious that only limited compounds from the TCM

601

formulae for T2DM show activities against multi-targets. The 2D-similarity analysis

602

shows that some known inhibitors are similar to the predicted potential inhibitors

603

against multi-targets. Furthermore, the low similarities between some of the predicted

604

potential inhibitors and the known inhibitors for each target suggest that some novel

605

potential inhibitors might be found from the predictions given by the Bayesian

606

classifiers. Those novel potential inhibitors may provide good source for

607

pharmaceutical chemists to find promising leads for treating T2DM.

608 609

The possible mechanisms of the classical TCM formulae for treating T2DM

610

As discussed in the above section, it is demonstrated that the 519 non-duplicated

611

potential inhibitors can interact with 14 T2DM-related targets. More than 80% of the

612

potential inhibitors only interact with one target, and the other potential inhibitors

613

interact with two or more targets. The multiple compounds from one herb or different

614

herbs of TCM formulae may form synergistic interactions for combating T2DM. Then,

615

the pharmacological activities of those predicted inhibitors that can interact with two

616

or more targets were examined by extensive literature searching. First, we analyzed

617

the nine compounds that interact with at least four targets (Table S10 in the

618

Supporting Materials). The only compound, mirificin, which was predicted to be an

619

inhibitor of five targets (carbonic anhydrase 1, carbonic anhydrase 2, FBPase,

620

glycogen synthase kinase-3 beta and insulin receptor), has free radical scavenging

621

activity.70 Moreover, the eight compounds that are predicted to be the inhibitors of

622

four targets have reported activities. Interestingly, the reported activities for three 22


Page 22 of 46

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623

compounds (Apiopaeonoside, Kakkalide and tectorigenin 7-O-xylosylglucoside) are

624

not related to the studied targets while related to aldose reductase.71,

625

studies have shown the close relationship between aldose reductase and free radical

626

scavenging/antioxidant.73 Aldose reductase might induce the oxidative damage

627

resulted from the competition between aldose reductase and glutathione reductase for

628

NADPH. Furthermore, the inhibitors of aldose reductase not only show activities for

629

aldose reductase, but also have antioxidant activity.74 By checking the top 10% of the

630

predicted inhibitors of aldose reductase (Table S8 in the Supporting Materials), we

631

also found that 6 of the 15 compounds have free radical scavenging/antioxidant

632

activities. According to those observations, the linkage between the three potential

633

inhibitors for four targets and the one potential inhibitor for five targets can be

634

established. Although the predicted potential inhibitors for more than four targets do

635

not have reported activities as predicted by us, all of those four compounds have the

636

reported activities of free radical scavenging or antioxidant. Besides, the previous

637

studies have shown that there were direct relationship between free radical

638

scavenging/antioxidant and T2DM.75 The level of free radical increases with the

639

T2DM disease progressing76, and in addition, some complications of T2DM,

640

including obesity, retinopathy and nephropathy, and some T2DM-related diseases,

641

such as coronary arteriosclerotic heart disease and arteriosclerosis, are also related to

642

the out of control of free radical.77-81 At last, it is necessary to emphasize that some

643

potential inhibitors that can interact only one, two or three targets also possess the

644

reported activities of free radical scavenging/antioxidant, for example, the compound,

645

apigenin-7-glucoside, a potential inhibitor of two targets (insulin receptor and

646

pancreatic alpha-amylase). Actually, the activity of apigenin-7-glucoside against

647

pancreatic alpha-amylase has been reported;82 in addition, this compound also has

648

aldose reductase and free radical scavenging/antioxidant activities.83-85 The compound,

649

4'-O-β-glucopyranosyl-3',5,7-trihydroxyflavone with predicted activities for carbonic

650

anhydrase 1 and pancreatic alpha-amylase targets have aldose reductase and free

651

radical scavenging activities.84, 86 For the compound, taxifolin, a predicted inhibitor 23


72

Previous


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

652

against three targets (carbonic anhydrase 2, estrogen receptor and pancreatic

653

alpha-amylase), the experimental activity against estrogen receptor has been

654

reported,87 and this compound also shows free radical scavenging/antioxidant,88, 89

655

PI3K inhibition,90 and PPAR gamma inhibition activities.91 Moreover, we also found

656

that some potential multi-target inhibitors did not have any documented activities for

657

the T2DM-related targets. For example, the compound, lobetyolin, was predicted to be

658

a potential inhibitor of carbonic anhydrase 2, insulin receptor and pancreatic

659

alpha-amylase targets, but the reported activity of lobetyolin is antibacterial.92 It was

660

interesting to find that the known drug of T2DM, glibenclamide, also has antibacterial

661

activity. The antibacterial activity is also found to be valuable in relieving diabetes

662

and the associated secondary disorders.93 According to our observations, we can find

663

that some of the predicted potential inhibitors have reported pharmacological

664

activities related to T2DM for more than one target.

665

In summary, we have examined the pharmacological activities of the multi-targets

666

compounds extensively. It was found that some pharmacological effects of the

667

predicted inhibitors of multi-targets have been reported previously, highlighting the

668

reliable predictions of the Bayesian classifiers. Furthermore, some potential inhibitors

669

predicted by our study need experimental verifications. Then, during the analysis of

670

the pharmacological activities of potential multi-target inhibitors, it was found that

671

most of these potential compounds have free radical scavenging/antioxidant activities

672

that have been proved to be effective to relieve T2DM and the related diseases of

673

T2DM.

674

The theory of TCM formulae is multi-compounds versus multi-targets. The

675

hundreds or even thousands of compounds extracted from the herbs of TCM formulae

676

interact with multi-targets to achieve synergistic effects. How the compounds from the

677

herbs of TCM formulae affect the T2DM-related targets is also unclear and needs to

678

be uncovered. According to our analysis, it was found that most of the predicted

679

potential inhibitors can only interact with a single target. However, a few of them still

680

can interact with more than one target. Some pharmacological effects, such as radical 24


Page 24 of 46

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681

scavenging/antioxidant and antibacterial activities, which are not directly related to

682

the studied targets, were found. Most compounds from the herbs in the TCM formulae

683

only interact with an individual target, forming the leading fighting force to combat

684

T2DM. Then, those potential multi-target compounds may influence the

685

T2DM-related targets, forming additional forces to enhance the therapeutic effects. At

686

last, a portion of the compounds are responsible for remedying the other related

687

symptoms that are proved to be related to T2DM, such as abnormal status of free

688

radical and oxidation in the human body. All of these observation found in this study

689

can be seen as a proper way to reveal the classical theory “Monarch, Minster,

690

Assistant and Guide” in TCM prescriptions at the molecular level.

691 692

Conclusion

693

In this study, we employed structure-based and complex-based virtual screening

694

approaches to find potential inhibitors from the Chinese herbs of the classical TCM

695

formulae for T2DM against the important T2DM-related targets. In order to achieve

696

more reliable predictions, the performance of molecular docking and pharmacophore

697

mapping for the 33 T2DM-related targets was evaluated. By combining the

698

advantages of molecular docking and pharmacophore mapping together, the Bayesian

699

classifiers were developed by integrating the predictions from molecular docking and

700

pharmacophore mapping. The validation results show that the integrated models are

701

superior to that based on the prediction from molecular docking or pharmacophore

702

mapping only.

703

The Bayesian classifiers with satisfactory discrimination capabilities for 15

704

targets were employed to screen the 2,479 compounds from the Chinese herbs of the

705

TCM formulae for T2DM. The pharmaceutical activities of some compounds

706

predicted by the Bayesian classifiers are confirmed by the experimental studies. In

707

order to uncover the underlying mechanism of the TCM formulae for combating

708

T2DM, the top 10% of the potential compounds ranked by the Bayesian scores for

709

each target were extracted to build the compound–target interaction network. The 25



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

710

analysis of the compound-target network shows that ~19% of the potential inhibitors

711

interact with more than one target. The examination of the pharmacological activities

712

demonstrates that the biological activities for a small portion of these potential

713

multi-target inhibitors have been reported, but the underlying activities for most

714

potential multi-target inhibitors need experimental verification. Besides, it is

715

important for us to find that a variety of the potential multi-target inhibitors have free

716

radical scavenging/antioxidant activities, which are closely related to T2DM. We can

717

conclude that the pharmacological effects of the TCM formulae for T2DM are not

718

only determined by the compounds that interact directly with one or more

719

T2DM-related targets, but also by the compounds with other supplementary

720

bioactivities important for treating T2DM, such as free radical scavenging/antioxidant

721

activities. The holistic way to express join forces for treating T2DM may be the

722

essence of the TCM formulae to break the robustness of the phenotype of T2DM and

723

restore the human body to become normal physiological condition.

724 725

Acknowledgements

726

This study was supported by the National Science Foundation of China (21173156),

727

the National Basic Research Program of China (973 program, 2012CB932600), and

728

the Priority Academic Program Development of Jiangsu Higher Education Institutions

729

(PAPD)

730 731

Supporting Information

732

Table S1. The names, PDB entries and crystal resolutions for the 48 T2DM-related

733

targets; Table S2. The 32 Chinese herbs in the TCM classical formulae for T2DM

734

with the highest frequency; Table S3. The docking power and discrimination power

735

of the Glide docking for 33 T2DM-related targets; Table S4. The docking power of

736

the Glide docking based on the six crystal structures of the complexes of HMG-CoA

737

reductase; Table S5. The numbers of Total features, Feature set A and Feature set B,

738

and the selectivity and discrimination capabilities of the pharmacophore models based 26


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Page 27 of 46

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739

on Feature set A and Feature set B; Table S6. The comparisons of the number of

740

pharmacophore features in Total features, Feature set A and Feature set B; Table S7.

741

The comparison of the performance of the naïve Bayesian classifiers built by using

742

docking scores, fit values and the combination of docking scores and fit values; Table

743

S8. The volume and hydrophobic properties of the binding pockets and the numbers

744

of the potential inhibitors predicted by the naïve Bayesian classifiers for 15 targets;

745

Table S9. The number of the potential inhibitors from the TCM formulae that are

746

similar to the known inhibitors for 15 targets by using different thresholds of the

747

similarity based on the MDLPublicKeys fingerprints; Table S10. The number of the

748

predicted inhibitors that can interact with two, three, four and five targets; Figure S1.

749

The distributions of the docking scores of the known inhibitors and non-inhibitors by

750

using the (a) SP and (b) XP scoring modes of Glide for 1DQA of HMG-CoA

751

reductase; Figure S2. Ten known inhibitors of receptor protein-tyrosine kinase erbB-2

752

target with similar Murcko framework (colored in red); Figure S3. The distributions

753

of the eight important physicochemical properties for the 1,996 non-duplicated

754

potential inhibitors for 15 targets. This information is available free of charge via the

755

Internet at http://pubs.acs.org

756 757

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980

Legend of figures

981

Figure 1. The distributions of the docking scores of the validation datasets for (a) four

982

targets (PPAR-gamma, PPAR-alpha, DPP-4 and insulin receptor) with low p-values,

983

and (b) four targets (carbonic anhydrase 2, pyruvate dehydrogenase [lipoamide]

984

kinase isozyme 1, transthyretin and corticosteroid 11-beta-dehydrogenase, isozyme 1)

985

with high p-values.

986

Figure 2. (a) The comparison of the numbers of the pharmacophore features in Total

987

features, Feature set A and Feature set B, and (b) The comparison of the 32


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988

discrimination capacity indicated by the AUC values of the pharmacophore models

989

based on Feature set A and Feature set B.

990

Figure 3. The comparison of the discrimination capability indicated by the AUC

991

values for the naïve Bayesian classifiers based on fit values, docking scores and the

992

combination of fit values and docking scores.

993

Figure 4. The distributions of Bayesian scores of the inhibitors and non-inhibitors and

994

the AUC value of the naïve Bayesian classifiers based on (a) docking scores, (b) fit

995

values and (c) the combination of docking scores and fit values for insulin-like growth

996

factor 1 receptor (IGF-1R).

997

Figure 5. (a) Eight potential inhibitors predicted by the Bayesian classifier are

998

identical to the known inhibitors of estrogen receptor, and (b) three potential

999

inhibitors predicted by the Bayesian classifier are similar to the known inhibitors of

1000

estrogen receptor.

1001

Figure 6. Eighteen potential inhibitors from the TCM formulae predicted by the

1002

Bayesian classifier are similar to the known inhibitor, myricetin, of insulin receptor.

1003

Figure 7. The number of the potential inhibitors ranked by the Bayesian scores for

1004

establishing the compounds-targets network.

1005

Figure 8. The distribution of the number of the potential multi-target inhibitors.

1006

Figure 9. (a) The compound-target network for all potential inhibitors, (b) for those

1007

against no less than targets, (c) for those against no less than three targets, and (d) for

1008

those against no less than four targets.

1009

Figure 10. The molecule, Pueroside A, was predicted to an inhibitor of three targets

1010

(carbonic anhydrase, PPAR delta and PPAR gamma). (a) schematic representation of

1011

the interaction between Pueroside A and three targets, and (b) the known inhibitors

1012

that have the maximum similarity to Pueroside A based on the MDLPulicKeys

1013

fingerprints for three targets, respectively.

1014

Figure 11. The molecules, Epicatechin and (2RS)-2-(4-O-β-D-glucopyranosylphenyl)

1015

propionyl β-D-glucopyranoside were predicted to be the inhibitors of two targets. (a)

1016

schematic representation of the interactions between Epicatechin and carbonic 33



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

2

and

estrogen

Page 34 of 46

1017

anhydrase

receptor,

and

those

between

1018

(2RS)-2-(4-O-β-D-glucopyranosylphenyl)

propionyl

β-D-glucopyranoside

1019

carbonic anhydrase 1 and carbonic anhydrase 2 targets, and (b) the known inhibitors

1020

of carbonic anhydrase 2 and estrogen receptor that have the maximum similarity to

1021

Epicatechin; the known inhibitors of carbonic anhydrase 1 and carbonic anhydrase

1022

that have the maximum similarity to (2RS)-2-(4-O-β-D-glucopyranosylphenyl)

1023

propionyl β-D-glucopyranoside based on the MDLPublicKeys fingerprints.

and

1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038

Table 1. The performances of the naïve Bayesian classifiers by using fit values,

1039

docking scores and the combination of fit values and docking scores for 24 targets No

Target name

Ninh

Nnon

AUC Fit value

Score

Combined

1

Carbonic anhydrase 1

52

1040

0.908

0.841

0.942

2

Carbonic anhydrase 2

91

1820

0.803

0.500

0.867

3

Growth factor receptor-bound protein 2

16

320

0.687

0.597

0.741

4

Peroxisome proliferator-activated receptor gamma

500

10000

0.532

0.795

0.837

5

Glycogen synthase kinase-3 β

69

1380

0.833

0.916

0.971

6

Aldose reductase

145

2900

0.717

0.836

0.892

7

Pancreatic α-amylase

22

440

0.795

0.743

0.865

34


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8

Peroxisome proliferator-activated receptor delta

224

4480

0.624

0.781

0.830

59

1180

0.641

0.573

0.672

0.614

0.741

0.814

(PPAR-δ) 9

Pyruvate dehydrogenase [lipoamide] kinase isozyme 1

10

Insulin receptor

370

7400

11

Insulin-like growth factor 1 receptor

177

2540

0.788

0.729

0.904

12

mRNA of Protein-tyrosine phosphatase,

34

680

0.604

0.759

0.596

263

5260

0.533

0.809

0.847

0.856

0.896

non-receptor type 1 (PTP1B) 13

Peroxisome proliferator-activated receptor α (PPAR-α)

14

Glucokinase (Hexokinase D)

314

6280

0.624

15

Protein Kinase C, alpha type

500

10000

0.572

0.600

0.683

16

Mitogen-activated protein kinase 10

500

10000

0.573

0.655

0.720

17

Corticosteroid 11-beta-dehydrogenase, isozyme 1

500

10000

0.526

0.578

0.604

(11-Beta hydroxysteroid dehydrogenase 1) 18


500

10000

0.551

0.601

0.661

19


130

2600

0.562

0.668

0.722

20

Receptor protein-tyrosine kinase erbB-2

13

260

0.669

0.937

0.957

21

Estrogen receptor

500

10000

0.676

0.897

0.940

22

Glucocorticoid receptor

115

2300

0.628

0.736

0.788

23

Fructose-1,6-bisphosphatase 1 (FBPase)

89

1780

0.740

0.821

0.856

24

Phosphatidylinositol-4,5-bisphosphate 3-kinase

16

320

0.840

0.898

0.934

catalytic subunit, delta isoform (Phosphoinositide 3-kinase delta)

1040 1041 1042

35



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1043 1044

Figure 1

1045

36


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1046 1047

Figure 2

1048

37



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1049 1050

Figure 3

1051

38


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1052 1053

Figure 4

1054

39



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1055 1056

Figure 5

40


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1057 1058

Figure 6

1059

41



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1060 1061

Figure 7

1062 1063 1064

42


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1065 1066

Figure 8

1067 1068 1069 1070 1071

43



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1072 1073

Figure 9

1074 1075 1076 1077 1078 1079

44


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1080 1081

Figure 10

45



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1082 1083

Figure 11

46


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Modeling CompoundâTarget Interaction Network ... - ACS Publications

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