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{"project":"LitCovid-PD-HP","denotations":[{"id":"T11","span":{"begin":5035,"end":5039},"obj":"Phenotype"},{"id":"T12","span":{"begin":5059,"end":5086},"obj":"Phenotype"},{"id":"T13","span":{"begin":5088,"end":5112},"obj":"Phenotype"},{"id":"T14","span":{"begin":5445,"end":5457},"obj":"Phenotype"},{"id":"T15","span":{"begin":9290,"end":9308},"obj":"Phenotype"},{"id":"T16","span":{"begin":22045,"end":22054},"obj":"Phenotype"},{"id":"T17","span":{"begin":22152,"end":22161},"obj":"Phenotype"},{"id":"T18","span":{"begin":22469,"end":22478},"obj":"Phenotype"},{"id":"T19","span":{"begin":22826,"end":22835},"obj":"Phenotype"},{"id":"T20","span":{"begin":25995,"end":26001},"obj":"Phenotype"},{"id":"T21","span":{"begin":26016,"end":26042},"obj":"Phenotype"}],"attributes":[{"id":"A11","pred":"hp_id","subj":"T11","obj":"http://purl.obolibrary.org/obo/HP_0001061"},{"id":"A12","pred":"hp_id","subj":"T12","obj":"http://purl.obolibrary.org/obo/HP_0008711"},{"id":"A13","pred":"hp_id","subj":"T13","obj":"http://purl.obolibrary.org/obo/HP_0001406"},{"id":"A14","pred":"hp_id","subj":"T14","obj":"http://purl.obolibrary.org/obo/HP_0000822"},{"id":"A15","pred":"hp_id","subj":"T15","obj":"http://purl.obolibrary.org/obo/HP_0000855"},{"id":"A16","pred":"hp_id","subj":"T16","obj":"http://purl.obolibrary.org/obo/HP_0002090"},{"id":"A17","pred":"hp_id","subj":"T17","obj":"http://purl.obolibrary.org/obo/HP_0002090"},{"id":"A18","pred":"hp_id","subj":"T18","obj":"http://purl.obolibrary.org/obo/HP_0002090"},{"id":"A19","pred":"hp_id","subj":"T19","obj":"http://purl.obolibrary.org/obo/HP_0002090"},{"id":"A20","pred":"hp_id","subj":"T20","obj":"http://purl.obolibrary.org/obo/HP_0002099"},{"id":"A21","pred":"hp_id","subj":"T21","obj":"http://purl.obolibrary.org/obo/HP_0002037"}],"text":"3 Result\n\n3.1 Prediction of active components and potential targets of QFPD\nFirstly, the DL ≥ 0.18 and OB ≥ 30 %.s were set as the standard to screen the chemical components obtained through online database TCMSP. Specifically, a total of 175 effective components of QFPD were screened from the TCM database, including 82 species of MSXG, 35 species of SGMH, 105 species of XCH, 21 species of WLS and 32 species of Others (Table 1 ). Among these effective components, 89 (50.86 %) components exited in more than two formulae; CID5280343 and CID5280794 were owned by MXSG, Others, SGMH and XCH; CID12303645 was owned by MXSG, Others, WLS and XCH (Fig. 1 A). Secondly, a total of 300 targets of QFPD were screened from the BATMAN-TCM database, including 192 targets of MSXG, 201 targets of SGMH, 221 targets of XCH, 96 targets of WLS and 99 targets of Others. Among these proteins, 21 (7%) targets exited in five formulae (Fig. 1B).\nTable 1 Effective components of QFPD.\nFormula N PubChem_Cid\nMSXG 82 CID10090416,CID10542808,CID10881804,CID11267805,CID114829,CID11558452,CID11602329,CID11975273,CID120074,CID12303645,CID124049,CID124052,CID13965473,CID14604077,CID14604078,CID14604081,CID15228663,CID15380912,CID162412,CID177149,CID193679,CID197678,CID23724664,CID25015742,CID268208,CID336327,CID354368,CID3764,CID439246,CID440833,CID442411,CID44257530,CID480774,CID480780,CID480787,CID480859,CID480873,CID49856081,CID503731,CID503737,CID5280343,CID5280378,CID5280448,CID5280544,CID5280794,CID5280863,CID5281619,CID5281654,CID5281789,CID5282768,CID5282805,CID5312521,CID5316900,CID5317300,CID5317478,CID5317479,CID5317480,CID5317652,CID5317768,CID5317777,CID5318437,CID5318585,CID5318679,CID5318869,CID5318998,CID5318999,CID5319013,CID5320083,CID5460988,CID5481234,CID5481948,CID5481949,CID5997,CID636883,CID637112,CID64971,CID6918970,CID73205,CID928837,CID9927807,CID15840593,CID15228662\nSGMH 35 CID1135,CID1174,CID3026,CID5789,CID6782,CID6998,CID8437,CID8679,CID13625,CID117158,CID159225,CID185,CID10019512,CID11438306,CID11869417,CID11870462,CID12315507,CID162350,CID16726037,CID222284,CID389888,CID3902,CID440833,CID5280343,CID5280445,CID5280544,CID5280794,CID5280863,CID5281331,CID5281605,CID5281616,CID5281628,CID5281654,CID5281779,CID5282768,CID5315890,CID5316876,CID5320945,CID5484202,CID5488781,CID5491637,CID64959,CID64982,CID676152,CID71307581,CID72307,CID13688752\nXCH 105 CID10090416,CID10542808,CID10881804,CID11267805,CID11438306,CID114829,CID11558452,CID11602329,CID117443,CID12303645,CID124049,CID124052,CID124211,CID13965473,CID14135323,CID14604077,CID14604078,CID14604081,CID15228662,CID15228663,CID15380912,CID156992,CID158311,CID15840593,CID159029,CID161271,CID162412,CID177149,CID185034,CID193679,CID197678,CID222284,CID23724664,CID25015742,CID25721350,CID268208,CID336327,CID354368,CID373261,CID3764,CID389001,CID389888,CID439246,CID442411,CID44257530,CID44258628,CID480774,CID480780,CID480787,CID480859,CID480873,CID49856081,CID503731,CID503737,CID5280343,CID5280378,CID5280442,CID5280448,CID5280794,CID5280863,CID5281605,CID5281619,CID5281654,CID5281674,CID5281703,CID5281789,CID5282768,CID5312521,CID5316900,CID5317300,CID5317478,CID5317479,CID5317480,CID5317652,CID5317768,CID5317777,CID5318437,CID5318585,CID5318679,CID5318869,CID5318998,CID5318999,CID5319013,CID5319042,CID5319252,CID5320083,CID5320315,CID5320399,CID5321865,CID5322059,CID5322078,CID5460988,CID5481234,CID5481948,CID5481949,CID5484202,CID636883,CID637112,CID64959,CID64971,CID64982,CID73205,CID821279,CID928837,CID9927807\nWLS 21 CID10181133,CID10743008,CID12303645,CID14036811,CID15225964,CID15226717,CID15976101,CID182232,CID222284,CID44575602,CID5283628,CID5471851,CID5471852,CID56668247,CID6436630,CID712316,CID73402,CID9064,CID9805290,CID14448075,CID14236575\nOthers 32 CID10212,CID11824478,CID122159,CID12303645,CID14057197,CID145659,CID17897,CID33934,CID373261,CID40429858,CID42607889,CID439246,CID442834,CID443024,CID5280343,CID5280445,CID5280794,CID5281326,CID5281617,CID5281781,CID5319406,CID5320621,CID5495928,CID5997,CID631170,CID632135,CID676152,CID712316,CID72344,CID79730,CID1149877,CID45359875\nFig. 1 Venn diagram of the five formulae’ active compounds and targets. A: compounds, B: targets.\n\n3.2 Functional and pathway enrichment analyses of QFPD targets\nAs shown in Fig. 2 , the 11 enriched GO terms of the targets in all five formulae were found, such as oxidoreductase activity, lipid metabolic process, lipid binding, small molecule metabolic process, homeostatic process, signal transducer activity and cell proliferation. Furthermore, the results of pathway enrichment analysis showed that the 7 KEGG pathways were significantly related to more than 4 formula groups, including dteroid biosynthesis, adipocytokine signaling pathway, neuroactive ligand-receptor interaction, steroid hormone biosynthesis, PPAR signaling pathway, arginine and proline metabolism and ABC transporters (Fig. 3 A). In addition, TTD analysis showed that the 8 diseases were significantly association with more than 3 formula groups, such as acne, Behcet'S disease, benign prostate hyperplasia, intrahepatic cholestasis and brain injury (Fig. 3B). However, the five formulae contained their specific (MSXG, SGMH, XCH, WLS and Others) GO, KEGG and TTD terms. For example, neurological system process and beta-Alanine metabolism terms were specific for MXSG; membrane organization and parasitic infections of the eye terms for SGMH; circadian rhythm and hypertension terms for XCH; nucleic acid binding transcription factor activity, ovarian steroidogenesis and chronic inflammatory diseases terms for WLS; fat digestion and absorption terms for Others.\nFig. 2 Bubble plot of the GO analysis of the five formulae’ targets.\nFig. 3 Bubble plot of the KEGG/TTD analysis of the five formulae’ targets. A: KEGG, B: TTD.\nIn the prediction of miRNAs in QFPD targets, MIR-183 and MIR-130A/B/301 were the highest linking terms to bind the five formulae targets and formulae Others was the highest group to bind miRNAs (Fig. 4 A). In addition, kinase prediction revealed CDK7 were significantly enriched in formulae MSXG, SGMH, XCH, WLS and Others (Fig. 4B). Finally, TF analysis showed that LXR was the highest linking TF to bind the four formulae targets and formulae WLS was the highest group to bind TFs (Fig. 4C).\nFig. 4 The miRNA, kinase and TF analysis of the five formulae’ targets by WebGestalt. Chord plot showing the five formulae’ targets present in the represented enriched miRNA, kinase and TF terms. Outer ring shows miRNA/kinase/TF term and log2 enrichment ratio (left) or five formulae grouping (right). Chords connect miRNA/kinase/TF term with formulae groups. A: miRNA, B: kinase, C: TF.\n\n3.3 Construction of PPI network and MCODE modules analysis\nTo further explore the functional relationship among five formulae, PPI networks were constructed through Metascape, and visual composition carried out by Cytoscape. Firstly, the potential 192 target genes of MXSG were analyzed by PPI network, and the results showed that there were 144 nodes and 510 edges, which represented the interaction between protein and function. The MXSG PPI network function module was confirmed by the MCODE plug-in and a list of the corresponding meaningful modules presented (Fig. 5 A). 3 modules scores were \u003e 2.5. Module 1 (score: 5.769) consisted of 13 nodes and the seed gene was COX7A1; Module 2 (score: 4.429) consisted of 14 nodes and the seed gene was ALDH1A1; module 3 (score: 5.0) consisted of 11 nodes and the seed gene was CNR2. KEGG pathway enrichment analysis showed that MXSG modules were enriched in neuroactive ligand-receptor interaction, calcium signaling pathway, inflammatory mediator regulation of TRP channels, et.al.\nFig. 5 KEGG analysis of MCODE modules. MCODE analysis was performed after the construction of the five formulae’ targets PPI; then, KEGG analysis was conducted on the MCODE modules. A: MXSG, B: Others, C: WLS, D: SGMH, E: XCH.\nSecondly, the potential 99 target genes of Others were analyzed by PPI network, and the results showed that there were 77 nodes and 194 edges. Only 1 module score were \u003e 2.5 (Fig. 5B). Module 1 (score: 5.769) consisted of 13 nodes and the seed gene was COX7A1. KEGG pathway enrichment analysis showed that Others modules were enriched in huntington's disease, glycolysis / gluconeogenesis, Notch signaling pathway, et.al.\nThirdly, the potential 96 target genes of WLS were analyzed by PPI network, and the results showed that there were 60 nodes and 143 edges. Only 1 modules score were \u003e 2.5 (Fig. 5C). Module 1 (score: 2.706) consisted of 17 nodes and the seed gene was CNR2. KEGG pathway enrichment analysis showed that WLS modules were enriched in thyroid hormone signaling pathway, adipocytokine signaling pathway, neuroactive ligand-receptor interaction, et.al.\nFourthly, the potential 201 target genes of SGMH were analyzed by PPI network, and the results showed that there were 153 nodes and 505 edges. 3 modules scores were \u003e 2.5 (Fig. 5D). Module 1 (score: 3.529) consisted of 17 nodes and the seed gene was ACSS1; Module 2 (score: 4.5) consisted of 7 nodes and the seed gene was CNR2; module 3 (score: 3.5) consisted of 8 nodes and the seed gene was PRKCG. KEGG pathway enrichment analysis showed that SGMH modules were enriched in insulin resistance, adipocytokine signaling pathway, Th17 cell differentiation, et.al.\nAt last, the potential 221 target genes of XCH were analyzed by PPI network, and the results showed that there were 166 nodes and 643 edges. 5 modules scores were \u003e 2.5 (Fig. 5E). Module 1 (score: 5.769) consisted of 13 nodes and the seed gene was COX7A1; Module 2 (score: 2.769) consisted of 13 nodes and the seed gene was RRM1; module 3 (score: 5.5) consisted of 12 nodes and the seed gene was CNR2; module 4 (score: 2.909) consisted of 11 nodes and the seed gene was ACSS1; module 5 (score: 3.0) consisted of 7 nodes and the seed gene was FFAR1. KEGG pathway enrichment analysis showed that XCH modules were enriched in calcium signaling pathway, cGMP-PKG signaling pathway, neuroactive ligand-receptor interaction, et.al.\n\n3.4 Network construction\nAfter using the BATMAN-TCM, we constructed five ingredients-target-pathway-disease networks, including MSXG, SGMH, XCH, WLS and Others. In order to emphasize the important network elements, we showed the networks that exhibit those targets with larger than 6, 5, 8, 7 and 4 linking compounds for MSXG, SGMH, XCH, WLS and Others, respectively (Fig. 6 ). MSXG network contained 31 key components, 50 proteins and 17 pathways; SGMH network contained 15 key components, 20 proteins and 8 pathways; WLS network contained 18 key components, 9 proteins and 4 pathways; XCH network contained 32 key components, 15 proteins and 12 pathways; Others network contained 10 key components, 13 proteins and 3 pathways. To find the potential drugs of formulae QFPD for COVID-19, a total of 67 hub components were used for ADMET analysis.\nFig. 6 The component-target-pathway-disease network. Purple polygons: PubChem ID of QFPD compounds; blue pentagrams: QFPD targets; yellow circles: KEGG pathway; red square: Therapeutic Target Database (TTD) disease term, green square: Online Mendelian Inheritance in Man (OMIN) disease term. A: MXSG, B: SGMH, C: WLS, D: XCH, E: Others. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)\n\n3.5 ADMET evaluation of the 67 key compounds\nSince in silico ADMET prediction can help early drug design and evaluation, ADMET properties of the 67 key compounds were predicted by SwissADME and pkCSM. Chemical properties including molecular weight (MW), rotatable bonds count, H-bond acceptors and donors count, TPSA and leadlikeness violations were calculated by SwissADME and shown as Fig. 8A. It is worth mentioning that 21 (31.34 %) compounds passed the stringent lead-like criteria (250 g/mol ≤ MW ≤ 350 g/mol, XLOGP ≤ 3.5 and rotatable bonds ≤ 7), which are excellent candidates for drug discovery (Fig. 7 A). And these lead-likeness compounds were further predicted by pkCSM, with the exception of S3 (low gastrointestinal absorption)\nFig. 7 Chemical properties statistics of hub components in the formulae. A: Molecular weight, B: rotatable bond count, C: H-bond acceptors count, D: H-bond donors count, E: topological polar surface area (TPSA), F: leadlikeness violations, G: pharmacokinetic and toxicity evaluated parameters of 20 leadlikeness compounds by pkCSM; green = good, yellow = tolerable, red = bad. Caco2: Caco-2 Permeability,HIA: Intestinal Absorption (Human), Skin: Skin Permeability, VDss: volume of distribution, FU: Fraction Unbound (Human), BBB: Blood Brain Barrier permeability, CNS: Central Nervous System permeability,TC: Total Clearance, OCT2: Renal Organic Cation Transporter 2, AMES: AMES toxicity, MTDD: Maximum Tolerated Dose (Human), hERG I/II: hERG I and II Inhibitors, LD50: Oral Rat Acute Toxicity (LD50), HT: Hepatotoxicity, SS: Skin Sensitisation, MT: Minnow toxicity. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)\nFig. 8 Schematic (3D and 2D) representation that molecular model of specific compounds of each formulae with COVID-19 proteins. A: M3 and E protein [ion channel], B: M3 and nsp13 [Helicase NCB site], C: S1 and nsp13 [Helicase ADP site], D: S1 and PLpro, E: X2 and Mpro, F: O2 and Mpro. M: MXSG, S: SGMH, X: XCH, O: Others.\nRegarding the absorption parameters, all 20 compounds (Table 2 ) presented a promising oral availability including the optimal Caco-2 cell permeability, HIA and skin permeability. The drug distribution results showed that most of the compounds distributed in tissue (VDss\u003e 0.45: tissue, VDss \u003c−0.15: plasma) with good unbound fraction scores, thus becoming available to interact with the pharmacological target. Only compound W5 and W11 were entirely unable to penetrate the blood-brain barrier (BBB) and central nervous system (CNS). In addition, 15 compounds presented a good renal elimination and were not substrates of the renal organic cation transporter 2 (OCT2). Finally, 14 compounds did not present any particular toxicity problems including AMES toxicity, maximum tolerated dose, hERG I inhibitor, hERG II inhibitor, oral rat acute toxicity (LD50), hepatotoxicity, skin sensitisation, and minnow toxicity (Fig. 7B).\nTable 2 20 potential active compounds from QFPD.\nPubchem Molecular Name Structure Pubchem Molecular Name Structure\nCID6918970 M3 ZINC5356864 CID10019512 S5 3-O-Methylviolanone\nCID336327 M5 Medicarpin CID9064 W5 Cianidanol\nCID14057197 O1 – CID182232 W11 (+)-Epicatechin\nCID42607889 O2 Alysifolinone CID25721350 X1 ZINC13130930\nCID3902 S1 letrozole CID14135323 X2 (2S)-dihydrobaicalein\nCID821279 X4 ZINC338038 CID439246 MXO1 naringenin\nCID440833 MS1 Leucocyanidol CID676152 SO1 SR-01,000,767,148\nCID177149 MX16 (+)-Vestitol CID11438306 SX1 cyclo(L-Tyr-l-Phe)\nCID114829 MX17 Liquiritigenin CID712316 WO1 (-)-taxifolin\nCID928837 MX8 ZINC519174 CID373261 XO1 Eriodyctiol (flavanone)\nM: MXSG, S: SGMH, X: XCH, O: Others.\n\n3.6 Molecular docking\nThe application of COVID-19 docking server and Discovery Studio software elucidated the interactions between the 20 lead-likeness compounds (S1, W5, MX17, MX16, W11, M5, XO1, MXO1, SO1, WO1, X4, MX8, M3, S5, SX1, O1, X2, X1, O2, MS1) and the 10 nonstructural and 2 structural proteins (Mpro, PLpro, nsp12 [RdRp with RNA], nsp12 [RdRp without RNA], nsp13 [Helicase ADP site], nsp13 [Helicase NCB site], nsp14 [ExoN], nsp14 [N7-MTase], nsp15 [endoribonuclease], nsp16 [2′-O-MTase], N protein NCB site, E protein [ion channel]). The docking scores were depicted in Table 3, Table 4 . The smaller of docking score, the lower of energy would be required, which means the binding between the compounds and the targets are stronger. There are 9 compounds presenting better bonding ability than other compounds.\nTable 3 Docking score between specific ingredients of QFPD and 2019-nCov proteins.\nMolecule M3 M5 S1 S5 W11 W5 X1 X2 X4 O1 O2\nMain Protease −7.7 −7.3 −6.8 −6.9 −7.2 −7.5 −7.1 −7.9 −7.3 −7.1 −7.4\nPapain-like protease −8.7 −8.5 −9.9 −7.7 −8 −8 −8.4 −8.7 −8.2 −8.6 −8.2\nRdRp with RNA −8.4 −8.5 −8.5 −8.3 −9.1 −9.1 −8.4 −8.2 −8.2 −8.3 −8.6\nRdRp without RNA −6.8 −6.8 −6.9 −6.8 −6.7 −6.9 −7.1 −7.2 −6.5 −7.3 −7.1\nHelicase ADP site −6.3 −6.3 −7.3 −6.5 −6 −6 −6.3 −6.5 −6.2 −6.1 −6.5\nHelicase NCB site −7.9 −6.9 −7.2 −7.1 −7.2 −7.2 −7.4 −7.5 −7.3 −7.4 −7.4\nNsp14(ExoN) −6.8 −6.6 −6.3 −6.4 −6.9 −6.8 −6.7 −6.9 −6.4 −6.6 −6.9\nNsp14(N7-MTase) −8.8 −8.1 −8.5 −7.5 −8.4 −8.4 −8.3 −8.5 −8 −8.3 −8.5\nNsp15(endoribonuclease) −6.6 −6.3 −6.3 −5.9 −6.2 −6.2 −6.2 −6.3 −6.2 −6.4 −6.4\nNsp16(2′-O-MTase) −7.5 −7.4 −7.5 −7.2 −8.2 −8.2 −7.7 −7.9 −7.7 −7.9 −8.4\nN protein NCB site −7.6 −7.5 −7.8 −7.6 −7.6 −7.6 −8 −8 −7.5 −7.6 −7.6\nE protein(ion channel) −8.1 −7 −7.8 −6.7 −6.4 −6.4 −7.2 −7.3 −7.2 −7.1 −6.8\nM: MXSG, S: SGMH, X: XCH, O: Others.\nTable 4 Evaluation of the effect of QFPD on the robustness disturbance of COVID-19 network.\nTopology MXSG SGMH XCH WLS Others BXTM YDBF\nDTS 25.66 26.71 21.02 17.64 23.16 14.52 22.71\nAC −4.63 −5.21 −3.02 −3.49 −5.38 −2.32 −3.78\nAPL 13.35 13.40 9.96 6.49 10.66 4.59 11.17\nCoC −1.44 −1.64 −1.59 −1.30 −1.23 −1.15 −1.25\nClC −6.24 −6.46 −6.45 −6.36 −5.88 −6.46 −6.52\nAverage connectivity (AC), Connection centrality (CoC), Closeness centrality (ClC): the larger the quotient is, the more stable the network and, the less the influence made by the drug. Disturbance total score (DTS), Average length of shortest path (APL), : the larger the quotient is, the less stable the network and, the larger the influence made by the drug. Negative control formula: BXTM. Positive control formula: YDBF.\nM3 (Fig. 8A), a specific compound in formulae MXSG, showed eight interactions with E protein [ion channel] including Pi-sigma, Pi-alkyl and Alkyl, which were connected with TYR 57, ALA 32, ILE 46 and PRO 54, etc.; additionally, M3 (Fig. 8B) showed five interactions with nsp13 [Helicase NCB site] including Unfavorable Donor-Donor, Pi-alkyl and Alkyl, which were connected with ASN 559, ARG 409, LEU 42 and PRO 406. S1 (Fig. 8C), a specific compound in formulae SGMH, showed seven interactions with nsp13 [Helicase ADP site] including H-bond interactions, van der waals, Amide-Pi stacked and Pi-alkyl, which were connected with ALA 313, ASP 374, GLN 537 and SER 289, etc.; additionally, S1 (Fig. 8D) showed five interactions with PLpro including Pi-anion, Pi-Pi stacked, Pi-Pi T-shaped and Pi-alkyl, which were connected with TYR 264, ASP 164, TYR 268 and PRO 248. X2 (Fig. 8E), a specific compound in formulae XCH, showed seven interactions with Mpro including H-bond interactions, Pi-Donor hydrogen bond and Pi-alkyl, which were connected with MET 165, GLU 166, LEU 141 and CYS 145, etc. O2 (Fig. 8F), a specific compound in formulae Others, showed seven interactions with Mpro including H-bond interactions, Carbon hydrogen bond, Pi-anion, Pi-sulfur and Pi-alkyl, which were connected with MET 131, GLY 71, LEU 100 and CYS 115, etc.\nMS1 (Fig. 9 A), a compound in formulae MXSG and SGMH, showed eleven interactions with N protein NCB site including H-bond interactions, Pi-Donor hydrogen bond, Pi-sigma, Pi-Pi stacked and Pi-alkyl, which were connected with SER 51, THR 109, ALA 50 and PRO 42, etc.; additionally, MS1 (Fig. 9B) showed five interactions with nsp14 [ExoN] including H-bond interactions and Pi-Pi stacked, which were connected with GLU 92, PHE 190, ASP 273 and VAL 91, etc. MX16 (Fig. 9C), a compound in formulae MXSG and XCH, showed seven interactions with nsp15 [endoribonuclease] including H-bond interactions, Alkyl and Pi-alkyl, which were connected with PRO 343, VAL 275, LYS 344 and SER 293, etc. SX1 (Fig. 9D), a compound in formulae SGMH and XCH, showed two interactions with nsp14 [N7-MTase] including Pi-Pi stacked and Pi-alkyl, which were connected with PHE 426; additionally, SX1 (Fig. 9E) showed five interactions with nsp15 [endoribonuclease] including H-bond interactions, Alkyl and Pi-alkyl, which were connected with LYS 344, LYS 289, VAL 291 and PRO 343. WO1 (Fig. 9F), a compound in formulae WLS and Others, showed seven interactions with nsp16 [2′-O-MTase] including H-bond interactions, Carbon hydrogen bond, Pi-Pi T-shaped, Pi-alkyl and Pi-anion, which were connected with PHE 149, CYS 115, ASP 99 and SER 74, etc.; additionally, WO1 (Fig. 9G) showed seven interactions with nsp12 [RdRp without RNA] including H-bond interactions, Carbon hydrogen bond, Unfavorable Donor-Donor, Pi-cation and Pi-anion, which were connected with THR 556, ARG 553, ASP 623 and SER 682, etc. XO1 (Fig. 9H), a compound in formulae XCH and Others, showed ten interactions with nsp12 [RdRp with RNA] including H-bond interactions, Pi-Donor hydrogen bond, Pi-Pi T-shaped and Pi-alkyl, which were connected with CYS 813, GLY 590, LYS 593 and ASP 865, etc.\nFig. 9 Schematic (3D and 2D) representation that molecular model of common compounds of the five formulae with COVID-19 proteins. A: MS1 and N protein NCB site, B: MS1 and nsp14 [ExoN], C: MX16 and nsp15 [endoribonuclease], D: SX1 and nsp14 [N7-MTase], E: SX1 and nsp15 [endoribonuclease], F: WO1 and nsp16 [2′-O-MTase], G: WO1 and nsp12 [RdRp without RNA], H: XO1 and nsp12 [RdRp with RNA]. MS: MXSG and SGMH, MX: MXSG and XCH, SX: SGMH and XCH, WO: WLS and Others, XO: XCH and Others.\n\n3.7 ACE2 and CD147 expression across tissues and co-expression genes\nSince 2019-nCov may enter other tissues and organs through ACE2 and CD147 binding, we firstly explored the expression and distribution of ACE2 and CD147 across 53 tissues. Fig. 10 A showed that the 5 top expression tissues of ACE2 were terminal ileum, testis, visceral (omentum), left ventricle and kidney cortex, which are 3 fold change higher than lung. And the 5 top expression tissues of CD147 were testis, visceral (omentum), left ventricle, aorta, atrial appendage and transformed fibroblasts. Then, to further understand whether QFPD only targets pneumonia or 2019-nCov, we obtained 200 co-expression genes of ACE2, 200 co-expression genes of CD147, 470 pneumonia-associated proteins, 119 HCoV-associated host proteins, and 476 co-expression genes of ACE2 in colonic epithelial cells. Fig. 10B displayed that QFPD had some common targets with these five sets, while specific 254 targets for QFPD, indicating other mechanisms of QFPD on COVID-19 in addition to 2019-nCov, pneumonia, ACE2 and CD147 related functions.\nFig. 10 ACE2 and CD147 expression across tissues and co-expression genes. A: Radar plot of ACE2 and CD147 expression across 53 tissues. The expression values were converted to base-2 logarithm. Red triangle and square mean the top 5 expression tissues. B: UpSet plot of proteins among QFPD, HCoV (Host_protein), pneumonia, ACE2 co-expression genes (ACE2_database), CD147 co-expression genes (CD147_database), and ACE2 co-expression genes in colonic epithelial cells (ACE2_colonic). The horizontal bar graph at the bottom left shows the total number of proteins for each group set. Circles and vertical lines in the x-axis mark the corresponding data sets being compared. The vertical bar graph at the top quantitates the number of proteins in the comparisons. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)\n\n3.8 Validation of drug positioning for QFPD against COVID-19 via the robustness of disease network\nFirstly, the robustness of whole networks against formula attack was assessed to evaluate QFPD attack on the COVID-19 disease network. Interestingly, Table 4 and Fig. 11 showed that MSXG, SGMH, XCH, WLS and Others attack on the COVID-19 network were characterized by greater disturbance score than negative control (BXTM), and increasing dependence on hub nodes, indicating greater fragility under formula attack. In addition, SGMH, MSXG, and Others exerted higher disturbance score than the positive control (YDBF) Table 5 .\nFig. 11 Evaluation of the effect of QFPD on the robustness disturbance of COVID-19 network. Blue normal distribution: drug attack on random networks as a null distribution for the permutation test. Red vertical line: the disturbance rate of the drug to the real disease network. Fist row: MXSG, second row: SGMH, third row: XCH, fourth row: WLS, fifth row: Others. Fist column: average connectivity, second row: average length of shortest path, third row: connection centrality, fourth row: closeness centrality. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)\nTable 5 Docking score between common ingredients of QFPD and 2019-nCov proteins.\nMolecule MS1 MX16 MX17 MX8 MXO1 SO1 SX1 WO1 XO1\nMain Protease −7.6 −7 −7.8 −7.8 −7.8 −7.2 −7.1 −7.4 −7.4\nPapain-like protease −8 −8.2 −8.1 −8.1 −8.2 −8.2 −9.2 −8.7 −8.3\nRdRp with RNA −8.6 −8 −7.9 −7.9 −8 −8.4 −8.2 −8.8 −9.5\nRdRp without RNA −7.1 −6.8 −6.8 −6.8 −7 −7.1 −6.9 −7.5 −7.1\nHelicase ADP site −6.2 −6.5 −6.3 −6.1 −6.2 −6.4 −6.8 −6.2 −6.8\nHelicase NCB site −7.4 −7.1 −7.2 −7.2 −7.5 −7.5 −7.4 −7.6 −7.5\nNsp14(ExoN) −7.2 −6.6 −6.6 −6.7 −6.9 −7 −6.9 −7.1 −7.1\nNsp14(N7-MTase) −8.6 −8.3 −8.4 −8.3 −8.6 −8.3 −9.4 −8.7 −8.7\nNsp15(endoribonuclease) −6.2 −6.8 −6.4 −6.4 −6.3 −6.5 −6.8 −6.4 −6.6\nNsp16(2′-O-MTase) −8.1 −7.4 −7.6 −7.8 −7.8 −8.3 −7.7 −8.4 −8.2\nN protein NCB site −8.1 −7.9 −8 −8 −7.6 −7.8 −7.5 −7.7 −7.9\nE protein(ion channel) −6.4 −6.9 −7.2 −7.2 −6.9 −6.9 −7.9 −6.8 −6.8\nM: MXSG, S: SGMH, X: XCH, O: Others.\nNext, to illustrate the mechanism of QFPD against COVID-19, a formula-attacked target-KEGG pathway network was constructed (Fig. 12 ). This network showed that MSXG, SGMH, XCH, WLS and Others interacted with 8 drug-attacked nodes (Cdc20, Ido1, Ifng, Il10, Il6, Ptger4, Spi1, Tnf), and 24 drug-attacked nodes in the COVID-19 network were related to Graft-versus-host disease, cytokine-cytokine receptor interaction, asthma, influenza A, inflammatory bowel disease, JAK-STAT signaling, etc.\nFig. 12 Disturbance analysis of QFPD for COVID-19 network. A: Venn diagram of the five formulae’ attacked targets. B: Formula-attacked target-KEGG pathway network; green square: formula, yellow diamond: attacked target, red circle: KEGG pathways. The bigger the size of the nodes is, the higher the degree is. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)"}

LitCovid-PD-GO-BP

LitCovid-sentences

LitCovid-PD-GlycoEpitope

{"project":"LitCovid-PD-GlycoEpitope","denotations":[{"id":"T2","span":{"begin":15008,"end":15011},"obj":"GlycoEpitope"},{"id":"T3","span":{"begin":15336,"end":15339},"obj":"GlycoEpitope"},{"id":"T4","span":{"begin":20153,"end":20156},"obj":"GlycoEpitope"},{"id":"T5","span":{"begin":20432,"end":20435},"obj":"GlycoEpitope"},{"id":"T6","span":{"begin":21226,"end":21229},"obj":"GlycoEpitope"},{"id":"T7","span":{"begin":21257,"end":21260},"obj":"GlycoEpitope"},{"id":"T8","span":{"begin":24797,"end":24800},"obj":"GlycoEpitope"}],"attributes":[{"id":"A2","pred":"glyco_epitope_db_id","subj":"T2","obj":"http://www.glycoepitope.jp/epitopes/AN0690"},{"id":"A3","pred":"glyco_epitope_db_id","subj":"T3","obj":"http://www.glycoepitope.jp/epitopes/AN0690"},{"id":"A4","pred":"glyco_epitope_db_id","subj":"T4","obj":"http://www.glycoepitope.jp/epitopes/AN0690"},{"id":"A5","pred":"glyco_epitope_db_id","subj":"T5","obj":"http://www.glycoepitope.jp/epitopes/AN0690"},{"id":"A6","pred":"glyco_epitope_db_id","subj":"T6","obj":"http://www.glycoepitope.jp/epitopes/AN0690"},{"id":"A7","pred":"glyco_epitope_db_id","subj":"T7","obj":"http://www.glycoepitope.jp/epitopes/AN0690"},{"id":"A8","pred":"glyco_epitope_db_id","subj":"T8","obj":"http://www.glycoepitope.jp/epitopes/AN0690"}],"text":"3 Result\n\n3.1 Prediction of active components and potential targets of QFPD\nFirstly, the DL ≥ 0.18 and OB ≥ 30 %.s were set as the standard to screen the chemical components obtained through online database TCMSP. Specifically, a total of 175 effective components of QFPD were screened from the TCM database, including 82 species of MSXG, 35 species of SGMH, 105 species of XCH, 21 species of WLS and 32 species of Others (Table 1 ). Among these effective components, 89 (50.86 %) components exited in more than two formulae; CID5280343 and CID5280794 were owned by MXSG, Others, SGMH and XCH; CID12303645 was owned by MXSG, Others, WLS and XCH (Fig. 1 A). Secondly, a total of 300 targets of QFPD were screened from the BATMAN-TCM database, including 192 targets of MSXG, 201 targets of SGMH, 221 targets of XCH, 96 targets of WLS and 99 targets of Others. Among these proteins, 21 (7%) targets exited in five formulae (Fig. 1B).\nTable 1 Effective components of QFPD.\nFormula N PubChem_Cid\nMSXG 82 CID10090416,CID10542808,CID10881804,CID11267805,CID114829,CID11558452,CID11602329,CID11975273,CID120074,CID12303645,CID124049,CID124052,CID13965473,CID14604077,CID14604078,CID14604081,CID15228663,CID15380912,CID162412,CID177149,CID193679,CID197678,CID23724664,CID25015742,CID268208,CID336327,CID354368,CID3764,CID439246,CID440833,CID442411,CID44257530,CID480774,CID480780,CID480787,CID480859,CID480873,CID49856081,CID503731,CID503737,CID5280343,CID5280378,CID5280448,CID5280544,CID5280794,CID5280863,CID5281619,CID5281654,CID5281789,CID5282768,CID5282805,CID5312521,CID5316900,CID5317300,CID5317478,CID5317479,CID5317480,CID5317652,CID5317768,CID5317777,CID5318437,CID5318585,CID5318679,CID5318869,CID5318998,CID5318999,CID5319013,CID5320083,CID5460988,CID5481234,CID5481948,CID5481949,CID5997,CID636883,CID637112,CID64971,CID6918970,CID73205,CID928837,CID9927807,CID15840593,CID15228662\nSGMH 35 CID1135,CID1174,CID3026,CID5789,CID6782,CID6998,CID8437,CID8679,CID13625,CID117158,CID159225,CID185,CID10019512,CID11438306,CID11869417,CID11870462,CID12315507,CID162350,CID16726037,CID222284,CID389888,CID3902,CID440833,CID5280343,CID5280445,CID5280544,CID5280794,CID5280863,CID5281331,CID5281605,CID5281616,CID5281628,CID5281654,CID5281779,CID5282768,CID5315890,CID5316876,CID5320945,CID5484202,CID5488781,CID5491637,CID64959,CID64982,CID676152,CID71307581,CID72307,CID13688752\nXCH 105 CID10090416,CID10542808,CID10881804,CID11267805,CID11438306,CID114829,CID11558452,CID11602329,CID117443,CID12303645,CID124049,CID124052,CID124211,CID13965473,CID14135323,CID14604077,CID14604078,CID14604081,CID15228662,CID15228663,CID15380912,CID156992,CID158311,CID15840593,CID159029,CID161271,CID162412,CID177149,CID185034,CID193679,CID197678,CID222284,CID23724664,CID25015742,CID25721350,CID268208,CID336327,CID354368,CID373261,CID3764,CID389001,CID389888,CID439246,CID442411,CID44257530,CID44258628,CID480774,CID480780,CID480787,CID480859,CID480873,CID49856081,CID503731,CID503737,CID5280343,CID5280378,CID5280442,CID5280448,CID5280794,CID5280863,CID5281605,CID5281619,CID5281654,CID5281674,CID5281703,CID5281789,CID5282768,CID5312521,CID5316900,CID5317300,CID5317478,CID5317479,CID5317480,CID5317652,CID5317768,CID5317777,CID5318437,CID5318585,CID5318679,CID5318869,CID5318998,CID5318999,CID5319013,CID5319042,CID5319252,CID5320083,CID5320315,CID5320399,CID5321865,CID5322059,CID5322078,CID5460988,CID5481234,CID5481948,CID5481949,CID5484202,CID636883,CID637112,CID64959,CID64971,CID64982,CID73205,CID821279,CID928837,CID9927807\nWLS 21 CID10181133,CID10743008,CID12303645,CID14036811,CID15225964,CID15226717,CID15976101,CID182232,CID222284,CID44575602,CID5283628,CID5471851,CID5471852,CID56668247,CID6436630,CID712316,CID73402,CID9064,CID9805290,CID14448075,CID14236575\nOthers 32 CID10212,CID11824478,CID122159,CID12303645,CID14057197,CID145659,CID17897,CID33934,CID373261,CID40429858,CID42607889,CID439246,CID442834,CID443024,CID5280343,CID5280445,CID5280794,CID5281326,CID5281617,CID5281781,CID5319406,CID5320621,CID5495928,CID5997,CID631170,CID632135,CID676152,CID712316,CID72344,CID79730,CID1149877,CID45359875\nFig. 1 Venn diagram of the five formulae’ active compounds and targets. A: compounds, B: targets.\n\n3.2 Functional and pathway enrichment analyses of QFPD targets\nAs shown in Fig. 2 , the 11 enriched GO terms of the targets in all five formulae were found, such as oxidoreductase activity, lipid metabolic process, lipid binding, small molecule metabolic process, homeostatic process, signal transducer activity and cell proliferation. Furthermore, the results of pathway enrichment analysis showed that the 7 KEGG pathways were significantly related to more than 4 formula groups, including dteroid biosynthesis, adipocytokine signaling pathway, neuroactive ligand-receptor interaction, steroid hormone biosynthesis, PPAR signaling pathway, arginine and proline metabolism and ABC transporters (Fig. 3 A). In addition, TTD analysis showed that the 8 diseases were significantly association with more than 3 formula groups, such as acne, Behcet'S disease, benign prostate hyperplasia, intrahepatic cholestasis and brain injury (Fig. 3B). However, the five formulae contained their specific (MSXG, SGMH, XCH, WLS and Others) GO, KEGG and TTD terms. For example, neurological system process and beta-Alanine metabolism terms were specific for MXSG; membrane organization and parasitic infections of the eye terms for SGMH; circadian rhythm and hypertension terms for XCH; nucleic acid binding transcription factor activity, ovarian steroidogenesis and chronic inflammatory diseases terms for WLS; fat digestion and absorption terms for Others.\nFig. 2 Bubble plot of the GO analysis of the five formulae’ targets.\nFig. 3 Bubble plot of the KEGG/TTD analysis of the five formulae’ targets. A: KEGG, B: TTD.\nIn the prediction of miRNAs in QFPD targets, MIR-183 and MIR-130A/B/301 were the highest linking terms to bind the five formulae targets and formulae Others was the highest group to bind miRNAs (Fig. 4 A). In addition, kinase prediction revealed CDK7 were significantly enriched in formulae MSXG, SGMH, XCH, WLS and Others (Fig. 4B). Finally, TF analysis showed that LXR was the highest linking TF to bind the four formulae targets and formulae WLS was the highest group to bind TFs (Fig. 4C).\nFig. 4 The miRNA, kinase and TF analysis of the five formulae’ targets by WebGestalt. Chord plot showing the five formulae’ targets present in the represented enriched miRNA, kinase and TF terms. Outer ring shows miRNA/kinase/TF term and log2 enrichment ratio (left) or five formulae grouping (right). Chords connect miRNA/kinase/TF term with formulae groups. A: miRNA, B: kinase, C: TF.\n\n3.3 Construction of PPI network and MCODE modules analysis\nTo further explore the functional relationship among five formulae, PPI networks were constructed through Metascape, and visual composition carried out by Cytoscape. Firstly, the potential 192 target genes of MXSG were analyzed by PPI network, and the results showed that there were 144 nodes and 510 edges, which represented the interaction between protein and function. The MXSG PPI network function module was confirmed by the MCODE plug-in and a list of the corresponding meaningful modules presented (Fig. 5 A). 3 modules scores were \u003e 2.5. Module 1 (score: 5.769) consisted of 13 nodes and the seed gene was COX7A1; Module 2 (score: 4.429) consisted of 14 nodes and the seed gene was ALDH1A1; module 3 (score: 5.0) consisted of 11 nodes and the seed gene was CNR2. KEGG pathway enrichment analysis showed that MXSG modules were enriched in neuroactive ligand-receptor interaction, calcium signaling pathway, inflammatory mediator regulation of TRP channels, et.al.\nFig. 5 KEGG analysis of MCODE modules. MCODE analysis was performed after the construction of the five formulae’ targets PPI; then, KEGG analysis was conducted on the MCODE modules. A: MXSG, B: Others, C: WLS, D: SGMH, E: XCH.\nSecondly, the potential 99 target genes of Others were analyzed by PPI network, and the results showed that there were 77 nodes and 194 edges. Only 1 module score were \u003e 2.5 (Fig. 5B). Module 1 (score: 5.769) consisted of 13 nodes and the seed gene was COX7A1. KEGG pathway enrichment analysis showed that Others modules were enriched in huntington's disease, glycolysis / gluconeogenesis, Notch signaling pathway, et.al.\nThirdly, the potential 96 target genes of WLS were analyzed by PPI network, and the results showed that there were 60 nodes and 143 edges. Only 1 modules score were \u003e 2.5 (Fig. 5C). Module 1 (score: 2.706) consisted of 17 nodes and the seed gene was CNR2. KEGG pathway enrichment analysis showed that WLS modules were enriched in thyroid hormone signaling pathway, adipocytokine signaling pathway, neuroactive ligand-receptor interaction, et.al.\nFourthly, the potential 201 target genes of SGMH were analyzed by PPI network, and the results showed that there were 153 nodes and 505 edges. 3 modules scores were \u003e 2.5 (Fig. 5D). Module 1 (score: 3.529) consisted of 17 nodes and the seed gene was ACSS1; Module 2 (score: 4.5) consisted of 7 nodes and the seed gene was CNR2; module 3 (score: 3.5) consisted of 8 nodes and the seed gene was PRKCG. KEGG pathway enrichment analysis showed that SGMH modules were enriched in insulin resistance, adipocytokine signaling pathway, Th17 cell differentiation, et.al.\nAt last, the potential 221 target genes of XCH were analyzed by PPI network, and the results showed that there were 166 nodes and 643 edges. 5 modules scores were \u003e 2.5 (Fig. 5E). Module 1 (score: 5.769) consisted of 13 nodes and the seed gene was COX7A1; Module 2 (score: 2.769) consisted of 13 nodes and the seed gene was RRM1; module 3 (score: 5.5) consisted of 12 nodes and the seed gene was CNR2; module 4 (score: 2.909) consisted of 11 nodes and the seed gene was ACSS1; module 5 (score: 3.0) consisted of 7 nodes and the seed gene was FFAR1. KEGG pathway enrichment analysis showed that XCH modules were enriched in calcium signaling pathway, cGMP-PKG signaling pathway, neuroactive ligand-receptor interaction, et.al.\n\n3.4 Network construction\nAfter using the BATMAN-TCM, we constructed five ingredients-target-pathway-disease networks, including MSXG, SGMH, XCH, WLS and Others. In order to emphasize the important network elements, we showed the networks that exhibit those targets with larger than 6, 5, 8, 7 and 4 linking compounds for MSXG, SGMH, XCH, WLS and Others, respectively (Fig. 6 ). MSXG network contained 31 key components, 50 proteins and 17 pathways; SGMH network contained 15 key components, 20 proteins and 8 pathways; WLS network contained 18 key components, 9 proteins and 4 pathways; XCH network contained 32 key components, 15 proteins and 12 pathways; Others network contained 10 key components, 13 proteins and 3 pathways. To find the potential drugs of formulae QFPD for COVID-19, a total of 67 hub components were used for ADMET analysis.\nFig. 6 The component-target-pathway-disease network. Purple polygons: PubChem ID of QFPD compounds; blue pentagrams: QFPD targets; yellow circles: KEGG pathway; red square: Therapeutic Target Database (TTD) disease term, green square: Online Mendelian Inheritance in Man (OMIN) disease term. A: MXSG, B: SGMH, C: WLS, D: XCH, E: Others. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)\n\n3.5 ADMET evaluation of the 67 key compounds\nSince in silico ADMET prediction can help early drug design and evaluation, ADMET properties of the 67 key compounds were predicted by SwissADME and pkCSM. Chemical properties including molecular weight (MW), rotatable bonds count, H-bond acceptors and donors count, TPSA and leadlikeness violations were calculated by SwissADME and shown as Fig. 8A. It is worth mentioning that 21 (31.34 %) compounds passed the stringent lead-like criteria (250 g/mol ≤ MW ≤ 350 g/mol, XLOGP ≤ 3.5 and rotatable bonds ≤ 7), which are excellent candidates for drug discovery (Fig. 7 A). And these lead-likeness compounds were further predicted by pkCSM, with the exception of S3 (low gastrointestinal absorption)\nFig. 7 Chemical properties statistics of hub components in the formulae. A: Molecular weight, B: rotatable bond count, C: H-bond acceptors count, D: H-bond donors count, E: topological polar surface area (TPSA), F: leadlikeness violations, G: pharmacokinetic and toxicity evaluated parameters of 20 leadlikeness compounds by pkCSM; green = good, yellow = tolerable, red = bad. Caco2: Caco-2 Permeability,HIA: Intestinal Absorption (Human), Skin: Skin Permeability, VDss: volume of distribution, FU: Fraction Unbound (Human), BBB: Blood Brain Barrier permeability, CNS: Central Nervous System permeability,TC: Total Clearance, OCT2: Renal Organic Cation Transporter 2, AMES: AMES toxicity, MTDD: Maximum Tolerated Dose (Human), hERG I/II: hERG I and II Inhibitors, LD50: Oral Rat Acute Toxicity (LD50), HT: Hepatotoxicity, SS: Skin Sensitisation, MT: Minnow toxicity. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)\nFig. 8 Schematic (3D and 2D) representation that molecular model of specific compounds of each formulae with COVID-19 proteins. A: M3 and E protein [ion channel], B: M3 and nsp13 [Helicase NCB site], C: S1 and nsp13 [Helicase ADP site], D: S1 and PLpro, E: X2 and Mpro, F: O2 and Mpro. M: MXSG, S: SGMH, X: XCH, O: Others.\nRegarding the absorption parameters, all 20 compounds (Table 2 ) presented a promising oral availability including the optimal Caco-2 cell permeability, HIA and skin permeability. The drug distribution results showed that most of the compounds distributed in tissue (VDss\u003e 0.45: tissue, VDss \u003c−0.15: plasma) with good unbound fraction scores, thus becoming available to interact with the pharmacological target. Only compound W5 and W11 were entirely unable to penetrate the blood-brain barrier (BBB) and central nervous system (CNS). In addition, 15 compounds presented a good renal elimination and were not substrates of the renal organic cation transporter 2 (OCT2). Finally, 14 compounds did not present any particular toxicity problems including AMES toxicity, maximum tolerated dose, hERG I inhibitor, hERG II inhibitor, oral rat acute toxicity (LD50), hepatotoxicity, skin sensitisation, and minnow toxicity (Fig. 7B).\nTable 2 20 potential active compounds from QFPD.\nPubchem Molecular Name Structure Pubchem Molecular Name Structure\nCID6918970 M3 ZINC5356864 CID10019512 S5 3-O-Methylviolanone\nCID336327 M5 Medicarpin CID9064 W5 Cianidanol\nCID14057197 O1 – CID182232 W11 (+)-Epicatechin\nCID42607889 O2 Alysifolinone CID25721350 X1 ZINC13130930\nCID3902 S1 letrozole CID14135323 X2 (2S)-dihydrobaicalein\nCID821279 X4 ZINC338038 CID439246 MXO1 naringenin\nCID440833 MS1 Leucocyanidol CID676152 SO1 SR-01,000,767,148\nCID177149 MX16 (+)-Vestitol CID11438306 SX1 cyclo(L-Tyr-l-Phe)\nCID114829 MX17 Liquiritigenin CID712316 WO1 (-)-taxifolin\nCID928837 MX8 ZINC519174 CID373261 XO1 Eriodyctiol (flavanone)\nM: MXSG, S: SGMH, X: XCH, O: Others.\n\n3.6 Molecular docking\nThe application of COVID-19 docking server and Discovery Studio software elucidated the interactions between the 20 lead-likeness compounds (S1, W5, MX17, MX16, W11, M5, XO1, MXO1, SO1, WO1, X4, MX8, M3, S5, SX1, O1, X2, X1, O2, MS1) and the 10 nonstructural and 2 structural proteins (Mpro, PLpro, nsp12 [RdRp with RNA], nsp12 [RdRp without RNA], nsp13 [Helicase ADP site], nsp13 [Helicase NCB site], nsp14 [ExoN], nsp14 [N7-MTase], nsp15 [endoribonuclease], nsp16 [2′-O-MTase], N protein NCB site, E protein [ion channel]). The docking scores were depicted in Table 3, Table 4 . The smaller of docking score, the lower of energy would be required, which means the binding between the compounds and the targets are stronger. There are 9 compounds presenting better bonding ability than other compounds.\nTable 3 Docking score between specific ingredients of QFPD and 2019-nCov proteins.\nMolecule M3 M5 S1 S5 W11 W5 X1 X2 X4 O1 O2\nMain Protease −7.7 −7.3 −6.8 −6.9 −7.2 −7.5 −7.1 −7.9 −7.3 −7.1 −7.4\nPapain-like protease −8.7 −8.5 −9.9 −7.7 −8 −8 −8.4 −8.7 −8.2 −8.6 −8.2\nRdRp with RNA −8.4 −8.5 −8.5 −8.3 −9.1 −9.1 −8.4 −8.2 −8.2 −8.3 −8.6\nRdRp without RNA −6.8 −6.8 −6.9 −6.8 −6.7 −6.9 −7.1 −7.2 −6.5 −7.3 −7.1\nHelicase ADP site −6.3 −6.3 −7.3 −6.5 −6 −6 −6.3 −6.5 −6.2 −6.1 −6.5\nHelicase NCB site −7.9 −6.9 −7.2 −7.1 −7.2 −7.2 −7.4 −7.5 −7.3 −7.4 −7.4\nNsp14(ExoN) −6.8 −6.6 −6.3 −6.4 −6.9 −6.8 −6.7 −6.9 −6.4 −6.6 −6.9\nNsp14(N7-MTase) −8.8 −8.1 −8.5 −7.5 −8.4 −8.4 −8.3 −8.5 −8 −8.3 −8.5\nNsp15(endoribonuclease) −6.6 −6.3 −6.3 −5.9 −6.2 −6.2 −6.2 −6.3 −6.2 −6.4 −6.4\nNsp16(2′-O-MTase) −7.5 −7.4 −7.5 −7.2 −8.2 −8.2 −7.7 −7.9 −7.7 −7.9 −8.4\nN protein NCB site −7.6 −7.5 −7.8 −7.6 −7.6 −7.6 −8 −8 −7.5 −7.6 −7.6\nE protein(ion channel) −8.1 −7 −7.8 −6.7 −6.4 −6.4 −7.2 −7.3 −7.2 −7.1 −6.8\nM: MXSG, S: SGMH, X: XCH, O: Others.\nTable 4 Evaluation of the effect of QFPD on the robustness disturbance of COVID-19 network.\nTopology MXSG SGMH XCH WLS Others BXTM YDBF\nDTS 25.66 26.71 21.02 17.64 23.16 14.52 22.71\nAC −4.63 −5.21 −3.02 −3.49 −5.38 −2.32 −3.78\nAPL 13.35 13.40 9.96 6.49 10.66 4.59 11.17\nCoC −1.44 −1.64 −1.59 −1.30 −1.23 −1.15 −1.25\nClC −6.24 −6.46 −6.45 −6.36 −5.88 −6.46 −6.52\nAverage connectivity (AC), Connection centrality (CoC), Closeness centrality (ClC): the larger the quotient is, the more stable the network and, the less the influence made by the drug. Disturbance total score (DTS), Average length of shortest path (APL), : the larger the quotient is, the less stable the network and, the larger the influence made by the drug. Negative control formula: BXTM. Positive control formula: YDBF.\nM3 (Fig. 8A), a specific compound in formulae MXSG, showed eight interactions with E protein [ion channel] including Pi-sigma, Pi-alkyl and Alkyl, which were connected with TYR 57, ALA 32, ILE 46 and PRO 54, etc.; additionally, M3 (Fig. 8B) showed five interactions with nsp13 [Helicase NCB site] including Unfavorable Donor-Donor, Pi-alkyl and Alkyl, which were connected with ASN 559, ARG 409, LEU 42 and PRO 406. S1 (Fig. 8C), a specific compound in formulae SGMH, showed seven interactions with nsp13 [Helicase ADP site] including H-bond interactions, van der waals, Amide-Pi stacked and Pi-alkyl, which were connected with ALA 313, ASP 374, GLN 537 and SER 289, etc.; additionally, S1 (Fig. 8D) showed five interactions with PLpro including Pi-anion, Pi-Pi stacked, Pi-Pi T-shaped and Pi-alkyl, which were connected with TYR 264, ASP 164, TYR 268 and PRO 248. X2 (Fig. 8E), a specific compound in formulae XCH, showed seven interactions with Mpro including H-bond interactions, Pi-Donor hydrogen bond and Pi-alkyl, which were connected with MET 165, GLU 166, LEU 141 and CYS 145, etc. O2 (Fig. 8F), a specific compound in formulae Others, showed seven interactions with Mpro including H-bond interactions, Carbon hydrogen bond, Pi-anion, Pi-sulfur and Pi-alkyl, which were connected with MET 131, GLY 71, LEU 100 and CYS 115, etc.\nMS1 (Fig. 9 A), a compound in formulae MXSG and SGMH, showed eleven interactions with N protein NCB site including H-bond interactions, Pi-Donor hydrogen bond, Pi-sigma, Pi-Pi stacked and Pi-alkyl, which were connected with SER 51, THR 109, ALA 50 and PRO 42, etc.; additionally, MS1 (Fig. 9B) showed five interactions with nsp14 [ExoN] including H-bond interactions and Pi-Pi stacked, which were connected with GLU 92, PHE 190, ASP 273 and VAL 91, etc. MX16 (Fig. 9C), a compound in formulae MXSG and XCH, showed seven interactions with nsp15 [endoribonuclease] including H-bond interactions, Alkyl and Pi-alkyl, which were connected with PRO 343, VAL 275, LYS 344 and SER 293, etc. SX1 (Fig. 9D), a compound in formulae SGMH and XCH, showed two interactions with nsp14 [N7-MTase] including Pi-Pi stacked and Pi-alkyl, which were connected with PHE 426; additionally, SX1 (Fig. 9E) showed five interactions with nsp15 [endoribonuclease] including H-bond interactions, Alkyl and Pi-alkyl, which were connected with LYS 344, LYS 289, VAL 291 and PRO 343. WO1 (Fig. 9F), a compound in formulae WLS and Others, showed seven interactions with nsp16 [2′-O-MTase] including H-bond interactions, Carbon hydrogen bond, Pi-Pi T-shaped, Pi-alkyl and Pi-anion, which were connected with PHE 149, CYS 115, ASP 99 and SER 74, etc.; additionally, WO1 (Fig. 9G) showed seven interactions with nsp12 [RdRp without RNA] including H-bond interactions, Carbon hydrogen bond, Unfavorable Donor-Donor, Pi-cation and Pi-anion, which were connected with THR 556, ARG 553, ASP 623 and SER 682, etc. XO1 (Fig. 9H), a compound in formulae XCH and Others, showed ten interactions with nsp12 [RdRp with RNA] including H-bond interactions, Pi-Donor hydrogen bond, Pi-Pi T-shaped and Pi-alkyl, which were connected with CYS 813, GLY 590, LYS 593 and ASP 865, etc.\nFig. 9 Schematic (3D and 2D) representation that molecular model of common compounds of the five formulae with COVID-19 proteins. A: MS1 and N protein NCB site, B: MS1 and nsp14 [ExoN], C: MX16 and nsp15 [endoribonuclease], D: SX1 and nsp14 [N7-MTase], E: SX1 and nsp15 [endoribonuclease], F: WO1 and nsp16 [2′-O-MTase], G: WO1 and nsp12 [RdRp without RNA], H: XO1 and nsp12 [RdRp with RNA]. MS: MXSG and SGMH, MX: MXSG and XCH, SX: SGMH and XCH, WO: WLS and Others, XO: XCH and Others.\n\n3.7 ACE2 and CD147 expression across tissues and co-expression genes\nSince 2019-nCov may enter other tissues and organs through ACE2 and CD147 binding, we firstly explored the expression and distribution of ACE2 and CD147 across 53 tissues. Fig. 10 A showed that the 5 top expression tissues of ACE2 were terminal ileum, testis, visceral (omentum), left ventricle and kidney cortex, which are 3 fold change higher than lung. And the 5 top expression tissues of CD147 were testis, visceral (omentum), left ventricle, aorta, atrial appendage and transformed fibroblasts. Then, to further understand whether QFPD only targets pneumonia or 2019-nCov, we obtained 200 co-expression genes of ACE2, 200 co-expression genes of CD147, 470 pneumonia-associated proteins, 119 HCoV-associated host proteins, and 476 co-expression genes of ACE2 in colonic epithelial cells. Fig. 10B displayed that QFPD had some common targets with these five sets, while specific 254 targets for QFPD, indicating other mechanisms of QFPD on COVID-19 in addition to 2019-nCov, pneumonia, ACE2 and CD147 related functions.\nFig. 10 ACE2 and CD147 expression across tissues and co-expression genes. A: Radar plot of ACE2 and CD147 expression across 53 tissues. The expression values were converted to base-2 logarithm. Red triangle and square mean the top 5 expression tissues. B: UpSet plot of proteins among QFPD, HCoV (Host_protein), pneumonia, ACE2 co-expression genes (ACE2_database), CD147 co-expression genes (CD147_database), and ACE2 co-expression genes in colonic epithelial cells (ACE2_colonic). The horizontal bar graph at the bottom left shows the total number of proteins for each group set. Circles and vertical lines in the x-axis mark the corresponding data sets being compared. The vertical bar graph at the top quantitates the number of proteins in the comparisons. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)\n\n3.8 Validation of drug positioning for QFPD against COVID-19 via the robustness of disease network\nFirstly, the robustness of whole networks against formula attack was assessed to evaluate QFPD attack on the COVID-19 disease network. Interestingly, Table 4 and Fig. 11 showed that MSXG, SGMH, XCH, WLS and Others attack on the COVID-19 network were characterized by greater disturbance score than negative control (BXTM), and increasing dependence on hub nodes, indicating greater fragility under formula attack. In addition, SGMH, MSXG, and Others exerted higher disturbance score than the positive control (YDBF) Table 5 .\nFig. 11 Evaluation of the effect of QFPD on the robustness disturbance of COVID-19 network. Blue normal distribution: drug attack on random networks as a null distribution for the permutation test. Red vertical line: the disturbance rate of the drug to the real disease network. Fist row: MXSG, second row: SGMH, third row: XCH, fourth row: WLS, fifth row: Others. Fist column: average connectivity, second row: average length of shortest path, third row: connection centrality, fourth row: closeness centrality. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)\nTable 5 Docking score between common ingredients of QFPD and 2019-nCov proteins.\nMolecule MS1 MX16 MX17 MX8 MXO1 SO1 SX1 WO1 XO1\nMain Protease −7.6 −7 −7.8 −7.8 −7.8 −7.2 −7.1 −7.4 −7.4\nPapain-like protease −8 −8.2 −8.1 −8.1 −8.2 −8.2 −9.2 −8.7 −8.3\nRdRp with RNA −8.6 −8 −7.9 −7.9 −8 −8.4 −8.2 −8.8 −9.5\nRdRp without RNA −7.1 −6.8 −6.8 −6.8 −7 −7.1 −6.9 −7.5 −7.1\nHelicase ADP site −6.2 −6.5 −6.3 −6.1 −6.2 −6.4 −6.8 −6.2 −6.8\nHelicase NCB site −7.4 −7.1 −7.2 −7.2 −7.5 −7.5 −7.4 −7.6 −7.5\nNsp14(ExoN) −7.2 −6.6 −6.6 −6.7 −6.9 −7 −6.9 −7.1 −7.1\nNsp14(N7-MTase) −8.6 −8.3 −8.4 −8.3 −8.6 −8.3 −9.4 −8.7 −8.7\nNsp15(endoribonuclease) −6.2 −6.8 −6.4 −6.4 −6.3 −6.5 −6.8 −6.4 −6.6\nNsp16(2′-O-MTase) −8.1 −7.4 −7.6 −7.8 −7.8 −8.3 −7.7 −8.4 −8.2\nN protein NCB site −8.1 −7.9 −8 −8 −7.6 −7.8 −7.5 −7.7 −7.9\nE protein(ion channel) −6.4 −6.9 −7.2 −7.2 −6.9 −6.9 −7.9 −6.8 −6.8\nM: MXSG, S: SGMH, X: XCH, O: Others.\nNext, to illustrate the mechanism of QFPD against COVID-19, a formula-attacked target-KEGG pathway network was constructed (Fig. 12 ). This network showed that MSXG, SGMH, XCH, WLS and Others interacted with 8 drug-attacked nodes (Cdc20, Ido1, Ifng, Il10, Il6, Ptger4, Spi1, Tnf), and 24 drug-attacked nodes in the COVID-19 network were related to Graft-versus-host disease, cytokine-cytokine receptor interaction, asthma, influenza A, inflammatory bowel disease, JAK-STAT signaling, etc.\nFig. 12 Disturbance analysis of QFPD for COVID-19 network. A: Venn diagram of the five formulae’ attacked targets. B: Formula-attacked target-KEGG pathway network; green square: formula, yellow diamond: attacked target, red circle: KEGG pathways. The bigger the size of the nodes is, the higher the degree is. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)"}

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