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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">A Drug-Target Network-Based Approach to Evaluate the Efficacy of Medicinal Plants for Type II Diabetes Mellitus</title><author><name sortKey="Gu, Jiangyong" sort="Gu, Jiangyong" uniqKey="Gu J" first="Jiangyong" last="Gu">Jiangyong Gu</name><affiliation><nlm:aff id="I1">Beijing National Laboratory for Molecular Sciences, State Key Lab of Rare Earth Material Chemistry and Applications, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China</nlm:aff></affiliation></author><author><name sortKey="Chen, Lirong" sort="Chen, Lirong" uniqKey="Chen L" first="Lirong" last="Chen">Lirong Chen</name><affiliation><nlm:aff id="I1">Beijing National Laboratory for Molecular Sciences, State Key Lab of Rare Earth Material Chemistry and Applications, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China</nlm:aff></affiliation></author><author><name sortKey="Yuan, Gu" sort="Yuan, Gu" uniqKey="Yuan G" first="Gu" last="Yuan">Gu Yuan</name><affiliation><nlm:aff id="I1">Beijing National Laboratory for Molecular Sciences, State Key Lab of Rare Earth Material Chemistry and Applications, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China</nlm:aff></affiliation></author><author><name sortKey="Xu, Xiaojie" sort="Xu, Xiaojie" uniqKey="Xu X" first="Xiaojie" last="Xu">Xiaojie Xu</name><affiliation><nlm:aff id="I1">Beijing National Laboratory for Molecular Sciences, State Key Lab of Rare Earth Material Chemistry and Applications, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">24223610</idno><idno type="pmc">3810496</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3810496</idno><idno type="RBID">PMC:3810496</idno><idno type="doi">10.1155/2013/203614</idno><date when="2013">2013</date><idno type="wicri:Area/Pmc/Corpus">000E95</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">A Drug-Target Network-Based Approach to Evaluate the Efficacy of Medicinal Plants for Type II Diabetes Mellitus</title><author><name sortKey="Gu, Jiangyong" sort="Gu, Jiangyong" uniqKey="Gu J" first="Jiangyong" last="Gu">Jiangyong Gu</name><affiliation><nlm:aff id="I1">Beijing National Laboratory for Molecular Sciences, State Key Lab of Rare Earth Material Chemistry and Applications, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China</nlm:aff></affiliation></author><author><name sortKey="Chen, Lirong" sort="Chen, Lirong" uniqKey="Chen L" first="Lirong" last="Chen">Lirong Chen</name><affiliation><nlm:aff id="I1">Beijing National Laboratory for Molecular Sciences, State Key Lab of Rare Earth Material Chemistry and Applications, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China</nlm:aff></affiliation></author><author><name sortKey="Yuan, Gu" sort="Yuan, Gu" uniqKey="Yuan G" first="Gu" last="Yuan">Gu Yuan</name><affiliation><nlm:aff id="I1">Beijing National Laboratory for Molecular Sciences, State Key Lab of Rare Earth Material Chemistry and Applications, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China</nlm:aff></affiliation></author><author><name sortKey="Xu, Xiaojie" sort="Xu, Xiaojie" uniqKey="Xu X" first="Xiaojie" last="Xu">Xiaojie Xu</name><affiliation><nlm:aff id="I1">Beijing National Laboratory for Molecular Sciences, State Key Lab of Rare Earth Material Chemistry and Applications, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China</nlm:aff></affiliation></author></analytic><series><title level="j">Evidence-based Complementary and Alternative Medicine : eCAM</title><idno type="ISSN">1741-427X</idno><idno type="eISSN">1741-4288</idno><imprint><date when="2013">2013</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>The use of plants as natural medicines in the treatment of type II diabetes mellitus (T2DM) has long been of special interest. In this work, we developed a docking score-weighted prediction model based on drug-target network to evaluate the efficacy of medicinal plants for T2DM. High throughput virtual screening from chemical library of natural products was adopted to calculate the binding affinity between natural products contained in medicinal plants and 33 T2DM-related proteins. The drug-target network was constructed according to the strength of the binding affinity if the molecular docking score satisfied the threshold. By linking the medicinal plant with T2DM through drug-target network, the model can predict the efficacy of natural products and medicinal plant for T2DM. Eighteen thousand nine hundred ninety-nine natural products and 1669 medicinal plants were predicted to be potentially bioactive.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Lin, Y" uniqKey="Lin Y">Y Lin</name></author><author><name sortKey="Sun, Z" uniqKey="Sun Z">Z Sun</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Stumvoll, M" uniqKey="Stumvoll M">M Stumvoll</name></author><author><name sortKey="Goldstein, Bj" uniqKey="Goldstein B">BJ Goldstein</name></author><author><name sortKey="Van Haeften, Tw" uniqKey="Van Haeften T">TW van Haeften</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Gu, J" uniqKey="Gu J">J Gu</name></author><author><name sortKey="Zhang, H" uniqKey="Zhang H">H Zhang</name></author><author><name sortKey="Chen, L" uniqKey="Chen L">L Chen</name></author><author><name sortKey="Xu, S" uniqKey="Xu S">S Xu</name></author><author><name 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Evid Based Complement Alternat Med</journal-id><journal-id journal-id-type="iso-abbrev">Evid Based Complement Alternat Med</journal-id><journal-id journal-id-type="publisher-id">ECAM</journal-id><journal-title-group><journal-title>Evidence-based Complementary and Alternative Medicine : eCAM</journal-title></journal-title-group><issn pub-type="ppub">1741-427X</issn><issn pub-type="epub">1741-4288</issn><publisher><publisher-name>Hindawi Publishing Corporation</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">24223610</article-id><article-id pub-id-type="pmc">3810496</article-id><article-id pub-id-type="doi">10.1155/2013/203614</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group></article-categories><title-group><article-title>A Drug-Target Network-Based Approach to Evaluate the Efficacy of Medicinal Plants for Type II Diabetes Mellitus</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Gu</surname><given-names>Jiangyong</given-names></name><xref ref-type="aff" rid="I1"></xref></contrib><contrib contrib-type="author"><name><surname>Chen</surname><given-names>Lirong</given-names></name><xref ref-type="aff" rid="I1"></xref><xref ref-type="corresp" rid="cor1">*</xref></contrib><contrib contrib-type="author"><name><surname>Yuan</surname><given-names>Gu</given-names></name><xref ref-type="aff" rid="I1"></xref></contrib><contrib contrib-type="author"><name><surname>Xu</surname><given-names>Xiaojie</given-names></name><xref ref-type="aff" rid="I1"></xref><xref ref-type="corresp" rid="cor2">*</xref></contrib></contrib-group><aff id="I1">Beijing National Laboratory for Molecular Sciences, State Key Lab of Rare Earth Material Chemistry and Applications, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China</aff><author-notes><corresp id="cor1">*Lirong Chen: <email>lirongc@pku.edu.cn</email> and </corresp><corresp id="cor2">*Xiaojie Xu: <email>xiaojxu@pku.edu.cn</email></corresp><fn fn-type="other"><p>Academic Editor: Francis B. Lewu</p></fn></author-notes><pub-date pub-type="ppub"><year>2013</year></pub-date><pub-date pub-type="epub"><day>10</day><month>10</month><year>2013</year></pub-date><pub-date pub-type="pmc-release"><day>10</day><month>10</month><year>2013</year></pub-date><pmc-comment> PMC Release delay is 0 months and 0 days and was based on the
<volume>2013</volume><elocation-id>203614</elocation-id><history><date date-type="received"><day>25</day><month>6</month><year>2013</year></date><date date-type="accepted"><day>19</day><month>9</month><year>2013</year></date></history><permissions><copyright-statement>Copyright © 2013 Jiangyong Gu et al.</copyright-statement><copyright-year>2013</copyright-year><license license-type="open-access"><license-p>This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><abstract><p>The use of plants as natural medicines in the treatment of type II diabetes mellitus (T2DM) has long been of special interest. In this work, we developed a docking score-weighted prediction model based on drug-target network to evaluate the efficacy of medicinal plants for T2DM. High throughput virtual screening from chemical library of natural products was adopted to calculate the binding affinity between natural products contained in medicinal plants and 33 T2DM-related proteins. The drug-target network was constructed according to the strength of the binding affinity if the molecular docking score satisfied the threshold. By linking the medicinal plant with T2DM through drug-target network, the model can predict the efficacy of natural products and medicinal plant for T2DM. Eighteen thousand nine hundred ninety-nine natural products and 1669 medicinal plants were predicted to be potentially bioactive.</p></abstract></article-meta></front><body><sec id="sec1"><title>1. Introduction</title><p>Type II Diabetes mellitus (T2DM) has been a major global health problem and affects a large population worldwide [<xref ref-type="bibr" rid="B1">1</xref>, <xref ref-type="bibr" rid="B2">2</xref>]. T2DM is a multifactorial and genetically heterogeneous disease caused by various risk factors such as insulin resistance, <italic>β</italic>-cell dysfunction, and obesity [<xref ref-type="bibr" rid="B2">2</xref>–<xref ref-type="bibr" rid="B5">5</xref>]. Moreover, T2DM may cause acute cardiovascular disease, retinopathy, nephropathy, neuropathy, and kidney-related complications [<xref ref-type="bibr" rid="B5">5</xref>–<xref ref-type="bibr" rid="B7">7</xref>]. Therefore, it demands effective drugs with minimal toxicity. The herbal medicines have been used for T2DM for thousands of years and accumulated a great deal of clinical experience. A herbal formula comprises several medicinal plants or animals and thus can affect the biological system through interactions between compounds and cellular targets [<xref ref-type="bibr" rid="B3">3</xref>, <xref ref-type="bibr" rid="B8">8</xref>–<xref ref-type="bibr" rid="B17">17</xref>]. The main mechanisms of herbal medicines in treating T2DM are that it increases insulin secretion and the sensitivity of insulin, inhibits glucose absorption, and reduces radicals caused by lipid peroxidation [<xref ref-type="bibr" rid="B8">8</xref>]. However, the major problem of herbal medicines is lack of scientific and clinical data to evaluate their efficacy and safety.</p><p>Network pharmacology proposed by Hopkins is a holistic approach to understand the function and behavior of a biological system at systems level in the context of biological networks and would be the next paradigm for drug discovery [<xref ref-type="bibr" rid="B18">18</xref>–<xref ref-type="bibr" rid="B20">20</xref>]. Several efforts have been made to explore the mechanism of herbal medicines such as prediction of the active ingredients and potential targets [<xref ref-type="bibr" rid="B21">21</xref>–<xref ref-type="bibr" rid="B26">26</xref>] and screening synergistic drug combinations [<xref ref-type="bibr" rid="B11">11</xref>, <xref ref-type="bibr" rid="B27">27</xref>, <xref ref-type="bibr" rid="B28">28</xref>]. The drug-target network (DTN) which connects drugs and their target proteins is an important biological network and provides an overview of polypharmacology of drugs [<xref ref-type="bibr" rid="B29">29</xref>–<xref ref-type="bibr" rid="B32">32</xref>]. Since medicinal plants have multiple compounds and a compound would have several target proteins, the DTN may bridge the gap between medicinal plants and diseases. In this work, we developed a computational approach based on DTN to evaluate the efficacy of medicinal plants.</p></sec><sec id="sec2"><title>2. Materials and Methods</title><sec id="sec2.1"><title>2.1. Data Collection and Molecular Docking</title><p>The pathogenesis of T2DM is concerned with various proteins. We retrieved the information of these proteins from KEGG Pathway database [<xref ref-type="bibr" rid="B33">33</xref>] and DrugBank [<xref ref-type="bibr" rid="B34">34</xref>] (<xref ref-type="fig" rid="fig1">Figure 1</xref>). The pathway of T2DM was downloaded from the KEGG website (<ext-link ext-link-type="uri" xlink:href="http://www.genome.jp/dbget-bin/www_bget?hsa04930">http://www.genome.jp/dbget-bin/www_bget?hsa04930</ext-link>), and the information of T2DM-related proteins was collected. In DrugBank, we first retrieved the FDA-approved drugs for T2DM and then found the target proteins for each drug. Then we searched the ligand-protein complex structure (x-ray or NMR) for each protein from RCSB protein data bank (<ext-link ext-link-type="uri" xlink:href="http://www.rcsb.org/pdb/home/home.do">http://www.rcsb.org/pdb/home/home.do</ext-link>). Finally, thirty-three proteins and their information were listed in <xref ref-type="table" rid="tab1">Table 1</xref>.</p><p>The 3D structures of natural products contained in medicinal plants were retrieved from the Universal Natural Product Database (UNPD) which comprised more than 208 thousands of natural products [<xref ref-type="bibr" rid="B35">54</xref>, <xref ref-type="bibr" rid="B36">55</xref>]. The AutoDock 4.0 [<xref ref-type="bibr" rid="B37">56</xref>, <xref ref-type="bibr" rid="B38">57</xref>] was adopted to perform the virtual screening, and binding free energy-based docking score (<italic>pK</italic><sub><italic>i</italic></sub>) was used to evaluate the affinity between each compound and each protein. For each protein, the hetero atoms of the ligand-protein complex structure were deleted and the polar hydrogen atoms were added. The binding site of each protein was defined as a 40 × 40 × 40 Å cube around the original ligand with a spacing of 0.375 Å between the grid points. The center of binding site was located in the center of the original ligand. The molecular docking was conducted according to the protocol described previously [<xref ref-type="bibr" rid="B39">58</xref>].</p></sec><sec id="sec2.2"><title>2.2. Drug-Target Network Construction and Analysis</title><p>The drug-target network was constructed by linking the compound with target protein if the docking score satisfied the thresholds that were used to determine whether the interaction between compound and protein was strong. According to our previous study, the thresholds were set as follow: the docking score should be greater than 7.00 and the score of original ligand of corresponding protein and the top percentage of rank of docking score should be less than 10% [<xref ref-type="bibr" rid="B35">54</xref>]. The edge value was the docking score of corresponding compound and protein. Finally, the DTN consisted of 32 target proteins, 18999 compounds (the UNPD ID, chemical name, formula, molecular weight, and CAS registry number of each compound were listed in Table S1, see Table S1 in Supplementary Material available online at <ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1155/2013/203614">http://dx.doi.org/10.1155/2013/203614</ext-link>), and 35076 edges (Supplementary Table S2). The glucocorticoid receptor (P04150) did not have any compounds. The compounds were derived from 1669 medicinal plants distinguished by Latin names. The DTN of potentially active compounds and proteins related with T2DM was used as a bridge to build the relationship between compound or medicinal plant and T2DM.</p></sec><sec id="sec2.3"><title>2.3. Chemical Space Analysis</title><p>The analysis of the distribution of compounds in the chemical space was conducted by principal component analysis (PCA) module in Discovery Studio. The PCA model was built with 8 descriptors: <italic>A</italic> log <italic>P</italic>, molecular weight, number of hydrogen-bond donors, number of hydrogen-bond acceptors, number of rotatable bonds, number of rings, number of aromatic rings, and molecular fractional polar surface area. The variances of PC1, PC2, and PC3 for compounds in <xref ref-type="fig" rid="fig2">Figure 2</xref> were 0.488, 0.186, and 0.145, respectively. The PCA of 25 FDA-approved small-molecule drugs retrieved from DrugBank was performed in the same process as above.</p></sec><sec id="sec2.4"><title>2.4. Prediction Model</title><p>Natural products are multitarget agents. The average number of target proteins was 1.84 in the DTN. Therefore, we proposed that the prediction efficacy (PE) of a compound for T2DM was the sum of its all edge values (docking scores) in the DTN:
<disp-formula id="EEq1"><label>(1)</label><mml:math id="M1"><mml:mtable><mml:mtr><mml:mtd><mml:mtext>P</mml:mtext><mml:msub><mml:mrow><mml:mtext>E</mml:mtext></mml:mrow><mml:mrow><mml:mtext>compound</mml:mtext></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mrow><mml:munderover><mml:mstyle displaystyle="true"><mml:mo stretchy="false">∑</mml:mo></mml:mstyle><mml:mrow><mml:mi>j</mml:mi><mml:mo>∈</mml:mo><mml:mi>P</mml:mi></mml:mrow><mml:mrow></mml:mrow></mml:munderover><mml:mrow><mml:mtext>scor</mml:mtext><mml:msub><mml:mrow><mml:mtext>e</mml:mtext></mml:mrow><mml:mrow><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mrow><mml:mo>,</mml:mo></mml:mtd></mml:mtr></mml:mtable></mml:math></disp-formula>where <italic>P</italic> was the set of proteins related to T2DM and score<sub><italic>j</italic></sub> was the docking score between this compound and <italic>j</italic>th protein. The PE<sub>compound</sub> for each compound was listed in Table S3.</p><p>Similarly, the prediction efficacy of a medicinal plant was defined as the sum of PE of compounds contained in this plant:
<disp-formula id="EEq2"><label>(2)</label><mml:math id="M2"><mml:mtable><mml:mtr><mml:mtd><mml:mtext>P</mml:mtext><mml:msub><mml:mrow><mml:mtext>E</mml:mtext></mml:mrow><mml:mrow><mml:mtext>plant</mml:mtext></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mrow><mml:munderover><mml:mstyle displaystyle="true"><mml:mo stretchy="false">∑</mml:mo></mml:mstyle><mml:mrow><mml:mi>i</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow></mml:munderover><mml:mrow><mml:mtext>P</mml:mtext><mml:msub><mml:mrow><mml:mtext>E</mml:mtext></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mtext>compound</mml:mtext></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub></mml:mrow></mml:mrow><mml:mo>,</mml:mo></mml:mtd></mml:mtr></mml:mtable></mml:math></disp-formula>where <italic>N</italic> denoted the number of compounds contained in the medicinal plant. The PE<sub>plant</sub> for each medicinal plant was listed in Table S4.</p></sec></sec><sec id="sec3"><title>3. Results and Discussion</title><sec id="sec3.1"><title>3.1. Drug-Likeness of Medicinal Natural Products for T2DM</title><p>The natural products contained in medicinal plants for T2DM had good drug-like properties. Lipinski CA and colleagues proposed the “rule of five” (molecular weight (MW) less than 500 Da, the number of hydrogen bond acceptors (HBA) less than 10, the number of hydrogen bond donors (HBD) less than 5, and octanol-water partition coefficient (<italic>A</italic> log <italic>P</italic>) less than five) [<xref ref-type="bibr" rid="B40">59</xref>, <xref ref-type="bibr" rid="B41">60</xref>] to estimate solubility and permeability of compounds in drug discovery. That is, a compound was unlikely to be a drug if it disobeyed the rules. The mean and median of MW, HBA, HBD, and <italic>A</italic> log <italic>P</italic> of these compounds were 540.43, 494.62; 6.3, 5; 2.5, 2; and 4.94, 5.07; respectively. It indicated that most compounds would be drug-like. The wide distribution of natural products in chemical space (<xref ref-type="fig" rid="fig2">Figure 2</xref>) showed that there would be vast property (structural and functional) diversity. Moreover, the large overlap between natural products and 25 FDA-approved small-molecule drugs for T2DM demonstrated that natural products contained in these medicinal plants had a hopeful prospect for drug discovery for T2DM.</p></sec><sec id="sec3.2"><title>3.2. Prediction Efficacy of Natural Product and Medicinal Plant</title><p>Herb medicines could simultaneously target multiple physiological processes through interactions between multiple compounds and cellular target proteins. For example, there were 105 distinct compounds contained in <italic>Hypericum perforatum</italic>, and 21 compounds existed in DTN. The herbal medicines could influence the biological system through interactions between multi-component and multi-target and thus reverse the biological networks from disease state to health state. Since a group of compounds contained in the herbal medicine could play a therapeutic role, the dosage could be reduced to reduce toxicity and side effects. For example, UNPD43323 (ormojine), UNPD194973 (ormosinin), and UNPD194973 (strychnohexamine) were the top three potential compounds (Supplementary Table S3). ormojine, ormosinin, and strychnohexamine had 27, 24, and 23 targets, respectively. The polypharmacology of natural products was very common.</p><p>The predicted efficacy of the top twenty medicinal plants for T2DM was listed in <xref ref-type="table" rid="tab2">Table 2</xref>. There were five plants (<italic>Hypericum perforatum, Ganoderma lucidum, Holarrhena antidysenterica, Celastrus orbiculatus, </italic>and<italic> Murraya euchrestifolia</italic>) where prediction efficacy was higher than 1000. We searched the literatures which reported the anti-T2DM bioactivities of the top twenty medicinal plants (<xref ref-type="table" rid="tab2">Table 2</xref>) and found that 15 medicinal plants had information of definite effectiveness against T2DM. For example, Arokiyaraj and colleagues evaluated the antihyperglycemic activity of <italic>Hypericum perforatum</italic> in diabetic rats, and it produced significant reduction in plasma glucose level [<xref ref-type="bibr" rid="B42">35</xref>].</p></sec><sec id="sec3.3"><title>3.3. Clinical Herbal Formula</title><p>Tangminling which was a widely used herbal formula in China to treat T2DM comprised eleven medicinal herbs (<italic>Trichosanthes kirilowii</italic>, <italic>Citrus sinensis</italic>, <italic>Bupleurum chinense</italic>, <italic>Rheum officinale</italic>, <italic>Astragalus membranaceus</italic>, <italic>Pinellia ternata</italic>, <italic>Scutellaria discolor</italic>, <italic>Crataegus pinnatifida var</italic>. <italic>major</italic>, <italic>Paeonia albiflora</italic>, <italic>Prunus mume</italic>, and <italic>Picrorhiza kurroa</italic>) [<xref ref-type="bibr" rid="B3">3</xref>]. The prediction efficacy of each medicinal plant was 493.04, 199.26, 36.06, 29.08, 15.12, 14.80, 7.83, 7.09, 7.07, 7.06, and 7.04, respectively. It indicated that all plants could play a role in the treatment of T2DM. However, the prediction efficacy of eleven herbs differed considerably from each other. It meant that <italic>Trichosanthes kirilowii</italic> and <italic>Citrus sinensis</italic> played major roles (sovereign herbs). Meanwhile, The others worked as assistants which may strengthen the efficacy of sovereign herbs or reduce the toxicity.</p></sec></sec><sec id="sec4"><title>4. Conclusions</title><p>Medicinal plants are potentially important for novel therapeutic drugs. It is currently estimated that approximately 420,000 plant species exist in nature [<xref ref-type="bibr" rid="B61">61</xref>]. However, only 10,000 of all plants have documented medicinal use [<xref ref-type="bibr" rid="B62">62</xref>]. Therefore, there are potentially many more important pharmaceutical applications of plants to be exploited. Traditional method (from selecting plants to separating compounds following bioassay) is time-consuming. In this work, we developed a molecular docking score-weighted prediction model based on drug-target network to evaluate the efficacy of natural products and medicinal plants for T2DM. Natural products contained in the medicinal plants would target several cellular target proteins. The prediction efficacy of this model took into account all potential interactions between multicomponents and targets. Therefore, the prediction efficacy was an overall evaluation at systems level. Fifteen out of the top twenty medicinal plants had reported bioactivity against T2DM in literatures. This approach may promote the research on the use of medicinal plants to treat T2DM and drug discovery from natural products.</p></sec><sec sec-type="supplementary-material" id="supplementary-material-sec"><title>Supplementary Material</title><supplementary-material content-type="local-data" id="f1"><caption><p>The supplementary materials comprise four tables of large datasets. Table S1 listed the identification information of 18999 natural products. Table S2 listed the natural products-target proteins interaction network (DTN). Table S3 and Table S4 listed the prediction efficacy of natural products and medicinal plants for T2DM, respectively.</p></caption><media xlink:href="203614.f1.zip"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec></body><back><ack><title>Conflict of Interests</title><p>The authors declare that they have no conflict of interests.</p></ack><ack><title>Acknowledgments</title><p>This work was financially supported by the National Key Special Project of Science and Technology for Innovation Drugs (Grant nos. 2012ZX09501001-004 and 2013ZX09402202). 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The black dots and green triangles represent natural products and FDA-approved drugs, respectively.</p></caption><graphic xlink:href="ECAM2013-203614.002"></graphic></fig><table-wrap id="tab1" orientation="portrait" position="float"><label>Table 1</label><caption><p>List of 33 proteins related with T2DM for molecular docking.</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="left" rowspan="1" colspan="1">Index</th><th align="center" rowspan="1" colspan="1">UniProt entry</th><th align="center" rowspan="1" colspan="1">PDB entry</th><th align="left" rowspan="1" colspan="1">Protein name</th></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">1</td><td align="center" rowspan="1" colspan="1">O43451</td><td align="center" rowspan="1" colspan="1">3CTT</td><td align="left" rowspan="1" colspan="1">Maltase-glucoamylase, intestinal</td></tr><tr><td align="left" rowspan="1" colspan="1">2</td><td align="center" rowspan="1" colspan="1">P01308</td><td align="center" rowspan="1" colspan="1">1TYM</td><td align="left" rowspan="1" colspan="1">Insulin</td></tr><tr><td align="left" rowspan="1" colspan="1">3</td><td align="center" rowspan="1" colspan="1">P01375</td><td align="center" rowspan="1" colspan="1">2AZ5</td><td align="left" rowspan="1" colspan="1">Tumor necrosis factor alpha</td></tr><tr><td align="left" rowspan="1" colspan="1">4</td><td align="center" rowspan="1" colspan="1">P04150</td><td align="center" rowspan="1" colspan="1">3H52</td><td align="left" rowspan="1" colspan="1">Glucocorticoid receptor</td></tr><tr><td align="left" rowspan="1" colspan="1">5</td><td align="center" rowspan="1" colspan="1">P04746</td><td align="center" rowspan="1" colspan="1">1XDO</td><td align="left" rowspan="1" colspan="1">Pancreatic alpha-amylase</td></tr><tr><td align="left" rowspan="1" colspan="1">6</td><td align="center" rowspan="1" colspan="1">P05121</td><td align="center" rowspan="1" colspan="1">3UT3</td><td align="left" rowspan="1" colspan="1">Plasminogen activator inhibitor 1</td></tr><tr><td align="left" rowspan="1" colspan="1">7</td><td align="center" rowspan="1" colspan="1">P06213</td><td align="center" rowspan="1" colspan="1">3EKN</td><td align="left" rowspan="1" colspan="1">Insulin receptor</td></tr><tr><td align="left" rowspan="1" colspan="1">8</td><td align="center" rowspan="1" colspan="1">P07339</td><td align="center" rowspan="1" colspan="1">1LYW</td><td align="left" rowspan="1" colspan="1">Cathepsin D</td></tr><tr><td align="left" rowspan="1" colspan="1">9</td><td align="center" rowspan="1" colspan="1">P08069</td><td align="center" rowspan="1" colspan="1">3I81</td><td align="left" rowspan="1" colspan="1">Insulin-like growth factor 1 receptor</td></tr><tr><td align="left" rowspan="1" colspan="1">10</td><td align="center" rowspan="1" colspan="1">P11474</td><td align="center" rowspan="1" colspan="1">3K6P</td><td align="left" rowspan="1" colspan="1">Steroid hormone receptor ERR1</td></tr><tr><td align="left" rowspan="1" colspan="1">11</td><td align="center" rowspan="1" colspan="1">P12821</td><td align="center" rowspan="1" colspan="1">3L3N</td><td align="left" rowspan="1" colspan="1">Angiotensin-converting enzyme</td></tr><tr><td align="left" rowspan="1" colspan="1">12</td><td align="center" rowspan="1" colspan="1">P13569</td><td align="center" rowspan="1" colspan="1">3GD7</td><td align="left" rowspan="1" colspan="1">Cystic fibrosis transmembrane conductance regulator</td></tr><tr><td align="left" rowspan="1" colspan="1">13</td><td align="center" rowspan="1" colspan="1">P14410</td><td align="center" rowspan="1" colspan="1">3LPP</td><td align="left" rowspan="1" colspan="1">Sucrase-isomaltase, intestinal</td></tr><tr><td align="left" rowspan="1" colspan="1">14</td><td align="center" rowspan="1" colspan="1">P14618</td><td align="center" rowspan="1" colspan="1">3BJF</td><td align="left" rowspan="1" colspan="1">Pyruvate kinase isozymes M1/M2</td></tr><tr><td align="left" rowspan="1" colspan="1">15</td><td align="center" rowspan="1" colspan="1">P14735</td><td align="center" rowspan="1" colspan="1">3E4A</td><td align="left" rowspan="1" colspan="1">Insulin-degrading enzyme</td></tr><tr><td align="left" rowspan="1" colspan="1">16</td><td align="center" rowspan="1" colspan="1">P19367</td><td align="center" rowspan="1" colspan="1">1DGK</td><td align="left" rowspan="1" colspan="1">Hexokinase-1</td></tr><tr><td align="left" rowspan="1" colspan="1">17</td><td align="center" rowspan="1" colspan="1">P27361</td><td align="center" rowspan="1" colspan="1">2ZOQ</td><td align="left" rowspan="1" colspan="1">Mitogen-activated protein kinase 3</td></tr><tr><td align="left" rowspan="1" colspan="1">18</td><td align="center" rowspan="1" colspan="1">P27487</td><td align="center" rowspan="1" colspan="1">3G0D</td><td align="left" rowspan="1" colspan="1">Dipeptidyl peptidase 4</td></tr><tr><td align="left" rowspan="1" colspan="1">19</td><td align="center" rowspan="1" colspan="1">P27986</td><td align="center" rowspan="1" colspan="1">4A55</td><td align="left" rowspan="1" colspan="1">Phosphatidylinositol 3-kinase regulatory subunit alpha</td></tr><tr><td align="left" rowspan="1" colspan="1">20</td><td align="center" rowspan="1" colspan="1">P28482</td><td align="center" rowspan="1" colspan="1">3I5Z</td><td align="left" rowspan="1" colspan="1">Mitogen-activated protein kinase 1</td></tr><tr><td align="left" rowspan="1" colspan="1">21</td><td align="center" rowspan="1" colspan="1">P30613</td><td align="center" rowspan="1" colspan="1">2VGF</td><td align="left" rowspan="1" colspan="1">Pyruvate kinase isozymes R/L</td></tr><tr><td align="left" rowspan="1" colspan="1">22</td><td align="center" rowspan="1" colspan="1">P35557</td><td align="center" rowspan="1" colspan="1">3IMX</td><td align="left" rowspan="1" colspan="1">Glucokinase</td></tr><tr><td align="left" rowspan="1" colspan="1">23</td><td align="center" rowspan="1" colspan="1">P35568</td><td align="center" rowspan="1" colspan="1">2Z8C</td><td align="left" rowspan="1" colspan="1">Insulin receptor substrate 1</td></tr><tr><td align="left" rowspan="1" colspan="1">24</td><td align="center" rowspan="1" colspan="1">P37231</td><td align="center" rowspan="1" colspan="1">3H0A</td><td align="left" rowspan="1" colspan="1">Peroxisome proliferator-activated receptor gamma</td></tr><tr><td align="left" rowspan="1" colspan="1">25</td><td align="center" rowspan="1" colspan="1">P42336</td><td align="center" rowspan="1" colspan="1">3HHM</td><td align="left" rowspan="1" colspan="1">Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform</td></tr><tr><td align="left" rowspan="1" colspan="1">26</td><td align="center" rowspan="1" colspan="1">P42345</td><td align="center" rowspan="1" colspan="1">1FAP</td><td align="left" rowspan="1" colspan="1">Serine/threonine-protein kinase mTOR</td></tr><tr><td align="left" rowspan="1" colspan="1">27</td><td align="center" rowspan="1" colspan="1">P43220</td><td align="center" rowspan="1" colspan="1">3C59</td><td align="left" rowspan="1" colspan="1">Glucagon-like peptide 1 receptor</td></tr><tr><td align="left" rowspan="1" colspan="1">28</td><td align="center" rowspan="1" colspan="1">P45983</td><td align="center" rowspan="1" colspan="1">3PZE</td><td align="left" rowspan="1" colspan="1">Mitogen-activated protein kinase 8</td></tr><tr><td align="left" rowspan="1" colspan="1">29</td><td align="center" rowspan="1" colspan="1">P45984</td><td align="center" rowspan="1" colspan="1">3NPC</td><td align="left" rowspan="1" colspan="1">Mitogen-activated protein kinase 9</td></tr><tr><td align="left" rowspan="1" colspan="1">30</td><td align="center" rowspan="1" colspan="1">P48736</td><td align="center" rowspan="1" colspan="1">3SD5</td><td align="left" rowspan="1" colspan="1">Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit gamma isoform</td></tr><tr><td align="left" rowspan="1" colspan="1">31</td><td align="center" rowspan="1" colspan="1">P53779</td><td align="center" rowspan="1" colspan="1">3TTI</td><td align="left" rowspan="1" colspan="1">Mitogen-activated protein kinase 10</td></tr><tr><td align="left" rowspan="1" colspan="1">32</td><td align="center" rowspan="1" colspan="1">P62508</td><td align="center" rowspan="1" colspan="1">2P7A</td><td align="left" rowspan="1" colspan="1">Estrogen-related receptor gamma</td></tr><tr><td align="left" rowspan="1" colspan="1">33</td><td align="center" rowspan="1" colspan="1">Q9BYF1</td><td align="center" rowspan="1" colspan="1">1R4L</td><td align="left" rowspan="1" colspan="1">Angiotensin-converting enzyme 2</td></tr></tbody></table></table-wrap><table-wrap id="tab2" orientation="portrait" position="float"><label>Table 2</label><caption><p>Top twenty potential medicinal plants.</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="left" rowspan="1" colspan="1">Rank</th><th align="center" rowspan="1" colspan="1">Latin name</th><th align="center" rowspan="1" colspan="1">PE<sub>plant</sub></th><th align="center" rowspan="1" colspan="1">Reported bioactivity</th></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">1</td><td align="center" rowspan="1" colspan="1"><italic>Hypericum perforatum </italic></td><td align="center" rowspan="1" colspan="1">1777.81</td><td align="center" rowspan="1" colspan="1">[<xref ref-type="bibr" rid="B42">35</xref>, <xref ref-type="bibr" rid="B43">36</xref>]</td></tr><tr><td align="left" rowspan="1" colspan="1">2</td><td align="center" rowspan="1" colspan="1"><italic>Ganoderma lucidum </italic></td><td align="center" rowspan="1" colspan="1">1560.05</td><td align="center" rowspan="1" colspan="1">[<xref ref-type="bibr" rid="B44">37</xref>]</td></tr><tr><td align="left" rowspan="1" colspan="1">3</td><td align="center" rowspan="1" colspan="1"><italic>Holarrhena antidysenterica </italic></td><td align="center" rowspan="1" colspan="1">1147.22</td><td align="center" rowspan="1" colspan="1">[<xref ref-type="bibr" rid="B45">38</xref>, <xref ref-type="bibr" rid="B46">39</xref>]</td></tr><tr><td align="left" rowspan="1" colspan="1">4</td><td align="center" rowspan="1" colspan="1"><italic>Celastrus orbiculatus </italic></td><td align="center" rowspan="1" colspan="1">1089.44</td><td align="center" rowspan="1" colspan="1">N/A</td></tr><tr><td align="left" rowspan="1" colspan="1">5</td><td align="center" rowspan="1" colspan="1"><italic>Murraya euchrestifolia </italic></td><td align="center" rowspan="1" colspan="1">1066.97</td><td align="center" rowspan="1" colspan="1">N/A</td></tr><tr><td align="left" rowspan="1" colspan="1">6</td><td align="center" rowspan="1" colspan="1"><italic>Melia azedarach </italic></td><td align="center" rowspan="1" colspan="1">980.47</td><td align="center" rowspan="1" colspan="1">[<xref ref-type="bibr" rid="B47">40</xref>]</td></tr><tr><td align="left" rowspan="1" colspan="1">7</td><td align="center" rowspan="1" colspan="1"><italic>Datura metel </italic></td><td align="center" rowspan="1" colspan="1">894.36</td><td align="center" rowspan="1" colspan="1">[<xref ref-type="bibr" rid="B48">41</xref>, <xref ref-type="bibr" rid="B49">42</xref>]</td></tr><tr><td align="left" rowspan="1" colspan="1">8</td><td align="center" rowspan="1" colspan="1"><italic>Ficus microcarpa </italic></td><td align="center" rowspan="1" colspan="1">837.65</td><td align="center" rowspan="1" colspan="1">[<xref ref-type="bibr" rid="B50">43</xref>]</td></tr><tr><td align="left" rowspan="1" colspan="1">9</td><td align="center" rowspan="1" colspan="1"><italic>Tripterygium wilfordii </italic></td><td align="center" rowspan="1" colspan="1">785.30</td><td align="center" rowspan="1" colspan="1">[<xref ref-type="bibr" rid="B51">44</xref>]</td></tr><tr><td align="left" rowspan="1" colspan="1">10</td><td align="center" rowspan="1" colspan="1"><italic>Pachysandra terminalis </italic></td><td align="center" rowspan="1" colspan="1">740.38</td><td align="center" rowspan="1" colspan="1">N/A</td></tr><tr><td align="left" rowspan="1" colspan="1">11</td><td align="center" rowspan="1" colspan="1"><italic>Calendula officinalis </italic></td><td align="center" rowspan="1" colspan="1">729.77</td><td align="center" rowspan="1" colspan="1">[<xref ref-type="bibr" rid="B52">45</xref>]</td></tr><tr><td align="left" rowspan="1" colspan="1">12</td><td align="center" rowspan="1" colspan="1"><italic>Vitis vinifera </italic></td><td align="center" rowspan="1" colspan="1">719.77</td><td align="center" rowspan="1" colspan="1">[<xref ref-type="bibr" rid="B53">46</xref>]</td></tr><tr><td align="left" rowspan="1" colspan="1">13</td><td align="center" rowspan="1" colspan="1"><italic>Melia toosendan </italic></td><td align="center" rowspan="1" colspan="1">711.49</td><td align="center" rowspan="1" colspan="1">N/A</td></tr><tr><td align="left" rowspan="1" colspan="1">14</td><td align="center" rowspan="1" colspan="1"><italic>Mangifera indica </italic></td><td align="center" rowspan="1" colspan="1">677.08</td><td align="center" rowspan="1" colspan="1">[<xref ref-type="bibr" rid="B54">47</xref>]</td></tr><tr><td align="left" rowspan="1" colspan="1">15</td><td align="center" rowspan="1" colspan="1"><italic>Piper nigrum </italic></td><td align="center" rowspan="1" colspan="1">667.41</td><td align="center" rowspan="1" colspan="1">[<xref ref-type="bibr" rid="B55">48</xref>]</td></tr><tr><td align="left" rowspan="1" colspan="1">16</td><td align="center" rowspan="1" colspan="1"><italic>Solanum dulcamara </italic></td><td align="center" rowspan="1" colspan="1">667.12</td><td align="center" rowspan="1" colspan="1">[<xref ref-type="bibr" rid="B56">49</xref>]</td></tr><tr><td align="left" rowspan="1" colspan="1">17</td><td align="center" rowspan="1" colspan="1"><italic>Garcinia hanburyi </italic></td><td align="center" rowspan="1" colspan="1">641.41</td><td align="center" rowspan="1" colspan="1">N/A</td></tr><tr><td align="left" rowspan="1" colspan="1">18</td><td align="center" rowspan="1" colspan="1"><italic>Momordica charantia </italic></td><td align="center" rowspan="1" colspan="1">632.37</td><td align="center" rowspan="1" colspan="1">[<xref ref-type="bibr" rid="B57">50</xref>, <xref ref-type="bibr" rid="B58">51</xref>]</td></tr><tr><td align="left" rowspan="1" colspan="1">19</td><td align="center" rowspan="1" colspan="1"><italic>Lantana camara </italic></td><td align="center" rowspan="1" colspan="1">625.64</td><td align="center" rowspan="1" colspan="1">[<xref ref-type="bibr" rid="B59">52</xref>]</td></tr><tr><td align="left" rowspan="1" colspan="1">20</td><td align="center" rowspan="1" colspan="1"><italic>Ceriops tagal </italic></td><td align="center" rowspan="1" colspan="1">623.13</td><td align="center" rowspan="1" colspan="1">[<xref ref-type="bibr" rid="B60">53</xref>]</td></tr></tbody></table></table-wrap></floats-group></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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