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LitCovid-PubTator

LitCovid-PMC-OGER-BB

LitCovid-PD-FMA-UBERON

LitCovid-PD-UBERON

{"project":"LitCovid-PD-UBERON","denotations":[{"id":"T7","span":{"begin":3264,"end":3268},"obj":"Body_part"},{"id":"T8","span":{"begin":3322,"end":3341},"obj":"Body_part"},{"id":"T9","span":{"begin":3322,"end":3327},"obj":"Body_part"},{"id":"T10","span":{"begin":3328,"end":3333},"obj":"Body_part"},{"id":"T11","span":{"begin":3346,"end":3368},"obj":"Body_part"},{"id":"T12","span":{"begin":3354,"end":3368},"obj":"Body_part"},{"id":"T13","span":{"begin":3575,"end":3579},"obj":"Body_part"}],"attributes":[{"id":"A7","pred":"uberon_id","subj":"T7","obj":"http://purl.obolibrary.org/obo/UBERON_0000014"},{"id":"A8","pred":"uberon_id","subj":"T8","obj":"http://purl.obolibrary.org/obo/UBERON_0000120"},{"id":"A9","pred":"uberon_id","subj":"T9","obj":"http://purl.obolibrary.org/obo/UBERON_0000178"},{"id":"A10","pred":"uberon_id","subj":"T10","obj":"http://purl.obolibrary.org/obo/UBERON_0000955"},{"id":"A11","pred":"uberon_id","subj":"T11","obj":"http://purl.obolibrary.org/obo/UBERON_0001017"},{"id":"A12","pred":"uberon_id","subj":"T12","obj":"http://purl.obolibrary.org/obo/UBERON_0001016"},{"id":"A13","pred":"uberon_id","subj":"T13","obj":"http://purl.obolibrary.org/obo/UBERON_0000014"}],"text":"2 Materials and methods\n\n2.1 Data preparation\nCompounds of the main herb in formula MSXG, SGMH, XCH, WLS and Others were searched in TCMSP [13], and screened based on drug-likeness (DL) ≥0.18 [14] and oral bioavailability (OB) ≥30 % [15]. Then, the corresponding Pubchem CIDs of the compounds were retrieved from the Pubchem database [16]. Finally, BATMAN-TCM [17], an bioinformatics analysis tool for studying TCM’s molecular mechanisms, was used to identify potential target genes of the active components (uploaded by Pubchem CIDs). To make the results more credible, we set the cutoff score ≥ 30 as the standard. Finally, to discovery the co-differentially presented targets in the five formulae, we conducted pan-formula analysis using Venn diagrams (http://bioinformatics.psb.ugent.be/webtools/Venn/).\n\n2.2 Functional and pathway enrichment analyses of QFPD targets\nTo better understand the functional involvements of MSXG, SGMH, XCH, WLS and Others targets, bioinformatics analyses of multiple formulae targets were first performed, including Gene Ontology (GO) function term, KEGG biological pathway and OMIM/TTD disease enrichment analyses. Then, kinase, microRNA and transcriptional factor (TF) enrichment analyses of the five formulae targets were conducted using the tool WebGestalt (http://bioinfo.vanderbilt.edu/webgestalt) [18] and the bubble and chord plot map were drawn with the R language ggplot2 and GOplot installation package. P-values were adjusted for multiple testing by Benjamini-Hochberg adjustment.\n\n2.3 Construction of PPI network and MCODE modules analysis\nTo further explore the pharmacological mechanisms, five PPI networks were built including: MSXG, SGMH, XCH, WLS and Others targets PPI network. Specifically, the five kinds of target proteins were respectively uploaded to Metascape to build PPI networks, with the species limited to “Homo sapiens”. Next, MCODE analysis [19], a method for finding densely connected modules in PPI networks, was carried out by Cytoscape 3.2.1 (http://www.cytoscape.org/) [20]. Finally, KEGG (Kyoto Encyclopedia of Genes and Genomes) signaling pathway enrichment analysis was further conducted on the identified functional modules of MSXG, SGMH, XCH, WLS and Others targets PPI networks, respectively.\n\n2.4 Network construction\nBased on the five formulae’s active components, BATMAN-TCM was used to set up five networks of components-target-pathway-disease (MSXG, SGMH, XCH, WLS and Others). To emphasize the important elements of the five networks, we only exhibited the hub targets according to the default criteria (targets with no fewer than 6, 5, 8, 7 and 4 linking compounds for MSXG, SGMH, XCH, WLS and Others, respectively). Finally, these important linking compounds of MSXG, SGMH, XCH, WLS and Others networks were obtained for further analysis.\n\n2.5 ADMET evaluation of the predicted active compounds\nBased on the SwissADME database [21], the physicochemical properties of the active components was predicted, including molecular weight (MW), rotatable bonds count, H-bond acceptors and donors count, TPSA and leadlikeness violations. Second, pharmacokinetic properties was predicted through pkCSM database [22], which contained the absorption (Caco-2 cell permeability, HIA and skin permeability), distribution (VDss, unbound fraction, blood-brain barrier and central nervous system permeability), excretion (total clearance and renal OCT2 substrate) and toxicity (AMES toxicity, maximum tolerated dose, hERG I inhibitor, hERG II inhibitor, oral rat acute toxicity (LD50), hepatotoxicity, skin sensitisation, and minnow toxicity).\n\n2.6 Molecular docking\nTo facilitate drug discovery against COVID-19, we used COVID-19 Docking Server (https://ncov.schanglab.org.cn/index.php) [23] to predict the binding modes between 12 COVID-19 targets and the 20 lead-likeness of QFPD. Specifically, the 10 nonstructural and 2 structural proteins of 2019-nCov were collected (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 and E protein [ion channel]); and the corresponding Protein Data Bank (PDB)codes were 6LU7, 4OW0, 3H5Y (with RNA), 3H5Y (without RNA), 6JYT (ADP site), 6JYT (NCB site), 5C8S (ExoN),5C8S (N7-MTase), 2RHB, 2XYR, 4KYJ, and 5 × 29, respectively. Finally, Discovery Studio software elucidated the 14 best docking results between compounds and the COVID-19 target proteins.\n\n2.7 ACE2 and CD147 expression across tissues and co-expression genes\nTo understand the expression and distribution of ACE2 and CD147 across tissues, a radar plot including 53 tissues was performed through COXPRESdb [24]. And the top 200 co-expression genes of ACE2 and CD147 (P \u003c 1E-16) were obtained, respectively. Then, text mining method from the literature was used to screen for pneumonia-associated genes through COREMINE (http://www.coremine.com/). In addition, co-expression genes of ACE2 in colonic epithelial cells [25] and HCoV-associated host proteins with references [26] were obtained. Finally, we performed UpsetView analysis (http://www.ehbio.com/ImageGP/) between these five sets of proteins and QFPD targets.\n\n2.8 Validation of drug positioning for QFPD against COVID-19 via the robustness of disease network\nSince QFPD effects on COVID-19 via multi-component and multi-target, we evaluate the potential efficacy of QFPD through TCMATCOV platform, which uses the quantitative evaluation algorithm of multi-target drugs to disturb the disease network. Specifically, the disturbing effect of drugs on diseases is simulated by deleting disease network nodes. The disturbance rate of drugs is calculated by comparing the changes of network topology characteristics before and after drug intervention, which is used to evaluate the intervention effect of drugs on diseases. Firstly, COVID-19 disease network was constructed based on specific cytokines of COVID-19 [27] and differentially expressed genes of SARS (GSE36969, GSE51387, GSE68820). Then, this platform uses four kinds of network topology characteristics to evaluate the robustness of COVID-19 network, including network average connectivity, network average shortest path, connectivity centrality and compactness centrality. And the five formulae (MSXG, SGMH, XCH, WLS and Others) disturbance scores are calculated according to the changes before and after drug intervention. Finally, the disturbance effect of the five formulae on the COVID-19 network was compared with null models with the total score of the disturbance, and the higher the value is, the higher the damage degree of drugs to the stability of the network is [12]. We take Banxia tianma baizhu decoction (BXTM) as negative control; and another efficient formula Yi du bi fei decoction (YDBF) as positive control."}

LitCovid-PD-MONDO

LitCovid-PD-CLO

LitCovid-PD-CHEBI

LitCovid-PD-HP

{"project":"LitCovid-PD-HP","denotations":[{"id":"T10","span":{"begin":4915,"end":4924},"obj":"Phenotype"}],"attributes":[{"id":"A10","pred":"hp_id","subj":"T10","obj":"http://purl.obolibrary.org/obo/HP_0002090"}],"text":"2 Materials and methods\n\n2.1 Data preparation\nCompounds of the main herb in formula MSXG, SGMH, XCH, WLS and Others were searched in TCMSP [13], and screened based on drug-likeness (DL) ≥0.18 [14] and oral bioavailability (OB) ≥30 % [15]. Then, the corresponding Pubchem CIDs of the compounds were retrieved from the Pubchem database [16]. Finally, BATMAN-TCM [17], an bioinformatics analysis tool for studying TCM’s molecular mechanisms, was used to identify potential target genes of the active components (uploaded by Pubchem CIDs). To make the results more credible, we set the cutoff score ≥ 30 as the standard. Finally, to discovery the co-differentially presented targets in the five formulae, we conducted pan-formula analysis using Venn diagrams (http://bioinformatics.psb.ugent.be/webtools/Venn/).\n\n2.2 Functional and pathway enrichment analyses of QFPD targets\nTo better understand the functional involvements of MSXG, SGMH, XCH, WLS and Others targets, bioinformatics analyses of multiple formulae targets were first performed, including Gene Ontology (GO) function term, KEGG biological pathway and OMIM/TTD disease enrichment analyses. Then, kinase, microRNA and transcriptional factor (TF) enrichment analyses of the five formulae targets were conducted using the tool WebGestalt (http://bioinfo.vanderbilt.edu/webgestalt) [18] and the bubble and chord plot map were drawn with the R language ggplot2 and GOplot installation package. P-values were adjusted for multiple testing by Benjamini-Hochberg adjustment.\n\n2.3 Construction of PPI network and MCODE modules analysis\nTo further explore the pharmacological mechanisms, five PPI networks were built including: MSXG, SGMH, XCH, WLS and Others targets PPI network. Specifically, the five kinds of target proteins were respectively uploaded to Metascape to build PPI networks, with the species limited to “Homo sapiens”. Next, MCODE analysis [19], a method for finding densely connected modules in PPI networks, was carried out by Cytoscape 3.2.1 (http://www.cytoscape.org/) [20]. Finally, KEGG (Kyoto Encyclopedia of Genes and Genomes) signaling pathway enrichment analysis was further conducted on the identified functional modules of MSXG, SGMH, XCH, WLS and Others targets PPI networks, respectively.\n\n2.4 Network construction\nBased on the five formulae’s active components, BATMAN-TCM was used to set up five networks of components-target-pathway-disease (MSXG, SGMH, XCH, WLS and Others). To emphasize the important elements of the five networks, we only exhibited the hub targets according to the default criteria (targets with no fewer than 6, 5, 8, 7 and 4 linking compounds for MSXG, SGMH, XCH, WLS and Others, respectively). Finally, these important linking compounds of MSXG, SGMH, XCH, WLS and Others networks were obtained for further analysis.\n\n2.5 ADMET evaluation of the predicted active compounds\nBased on the SwissADME database [21], the physicochemical properties of the active components was predicted, including molecular weight (MW), rotatable bonds count, H-bond acceptors and donors count, TPSA and leadlikeness violations. Second, pharmacokinetic properties was predicted through pkCSM database [22], which contained the absorption (Caco-2 cell permeability, HIA and skin permeability), distribution (VDss, unbound fraction, blood-brain barrier and central nervous system permeability), excretion (total clearance and renal OCT2 substrate) and toxicity (AMES toxicity, maximum tolerated dose, hERG I inhibitor, hERG II inhibitor, oral rat acute toxicity (LD50), hepatotoxicity, skin sensitisation, and minnow toxicity).\n\n2.6 Molecular docking\nTo facilitate drug discovery against COVID-19, we used COVID-19 Docking Server (https://ncov.schanglab.org.cn/index.php) [23] to predict the binding modes between 12 COVID-19 targets and the 20 lead-likeness of QFPD. Specifically, the 10 nonstructural and 2 structural proteins of 2019-nCov were collected (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 and E protein [ion channel]); and the corresponding Protein Data Bank (PDB)codes were 6LU7, 4OW0, 3H5Y (with RNA), 3H5Y (without RNA), 6JYT (ADP site), 6JYT (NCB site), 5C8S (ExoN),5C8S (N7-MTase), 2RHB, 2XYR, 4KYJ, and 5 × 29, respectively. Finally, Discovery Studio software elucidated the 14 best docking results between compounds and the COVID-19 target proteins.\n\n2.7 ACE2 and CD147 expression across tissues and co-expression genes\nTo understand the expression and distribution of ACE2 and CD147 across tissues, a radar plot including 53 tissues was performed through COXPRESdb [24]. And the top 200 co-expression genes of ACE2 and CD147 (P \u003c 1E-16) were obtained, respectively. Then, text mining method from the literature was used to screen for pneumonia-associated genes through COREMINE (http://www.coremine.com/). In addition, co-expression genes of ACE2 in colonic epithelial cells [25] and HCoV-associated host proteins with references [26] were obtained. Finally, we performed UpsetView analysis (http://www.ehbio.com/ImageGP/) between these five sets of proteins and QFPD targets.\n\n2.8 Validation of drug positioning for QFPD against COVID-19 via the robustness of disease network\nSince QFPD effects on COVID-19 via multi-component and multi-target, we evaluate the potential efficacy of QFPD through TCMATCOV platform, which uses the quantitative evaluation algorithm of multi-target drugs to disturb the disease network. Specifically, the disturbing effect of drugs on diseases is simulated by deleting disease network nodes. The disturbance rate of drugs is calculated by comparing the changes of network topology characteristics before and after drug intervention, which is used to evaluate the intervention effect of drugs on diseases. Firstly, COVID-19 disease network was constructed based on specific cytokines of COVID-19 [27] and differentially expressed genes of SARS (GSE36969, GSE51387, GSE68820). Then, this platform uses four kinds of network topology characteristics to evaluate the robustness of COVID-19 network, including network average connectivity, network average shortest path, connectivity centrality and compactness centrality. And the five formulae (MSXG, SGMH, XCH, WLS and Others) disturbance scores are calculated according to the changes before and after drug intervention. Finally, the disturbance effect of the five formulae on the COVID-19 network was compared with null models with the total score of the disturbance, and the higher the value is, the higher the damage degree of drugs to the stability of the network is [12]. We take Banxia tianma baizhu decoction (BXTM) as negative control; and another efficient formula Yi du bi fei decoction (YDBF) as positive control."}

LitCovid-PD-GO-BP

{"project":"LitCovid-PD-GO-BP","denotations":[{"id":"T24","span":{"begin":1180,"end":1202},"obj":"http://purl.obolibrary.org/obo/GO_0000981"},{"id":"T25","span":{"begin":1204,"end":1206},"obj":"http://purl.obolibrary.org/obo/GO_0000981"},{"id":"T26","span":{"begin":2106,"end":2123},"obj":"http://purl.obolibrary.org/obo/GO_0007165"},{"id":"T27","span":{"begin":2106,"end":2115},"obj":"http://purl.obolibrary.org/obo/GO_0023052"},{"id":"T28","span":{"begin":3384,"end":3393},"obj":"http://purl.obolibrary.org/obo/GO_0007588"},{"id":"T29","span":{"begin":4176,"end":4187},"obj":"http://purl.obolibrary.org/obo/GO_0022831"}],"text":"2 Materials and methods\n\n2.1 Data preparation\nCompounds of the main herb in formula MSXG, SGMH, XCH, WLS and Others were searched in TCMSP [13], and screened based on drug-likeness (DL) ≥0.18 [14] and oral bioavailability (OB) ≥30 % [15]. Then, the corresponding Pubchem CIDs of the compounds were retrieved from the Pubchem database [16]. Finally, BATMAN-TCM [17], an bioinformatics analysis tool for studying TCM’s molecular mechanisms, was used to identify potential target genes of the active components (uploaded by Pubchem CIDs). To make the results more credible, we set the cutoff score ≥ 30 as the standard. Finally, to discovery the co-differentially presented targets in the five formulae, we conducted pan-formula analysis using Venn diagrams (http://bioinformatics.psb.ugent.be/webtools/Venn/).\n\n2.2 Functional and pathway enrichment analyses of QFPD targets\nTo better understand the functional involvements of MSXG, SGMH, XCH, WLS and Others targets, bioinformatics analyses of multiple formulae targets were first performed, including Gene Ontology (GO) function term, KEGG biological pathway and OMIM/TTD disease enrichment analyses. Then, kinase, microRNA and transcriptional factor (TF) enrichment analyses of the five formulae targets were conducted using the tool WebGestalt (http://bioinfo.vanderbilt.edu/webgestalt) [18] and the bubble and chord plot map were drawn with the R language ggplot2 and GOplot installation package. P-values were adjusted for multiple testing by Benjamini-Hochberg adjustment.\n\n2.3 Construction of PPI network and MCODE modules analysis\nTo further explore the pharmacological mechanisms, five PPI networks were built including: MSXG, SGMH, XCH, WLS and Others targets PPI network. Specifically, the five kinds of target proteins were respectively uploaded to Metascape to build PPI networks, with the species limited to “Homo sapiens”. Next, MCODE analysis [19], a method for finding densely connected modules in PPI networks, was carried out by Cytoscape 3.2.1 (http://www.cytoscape.org/) [20]. Finally, KEGG (Kyoto Encyclopedia of Genes and Genomes) signaling pathway enrichment analysis was further conducted on the identified functional modules of MSXG, SGMH, XCH, WLS and Others targets PPI networks, respectively.\n\n2.4 Network construction\nBased on the five formulae’s active components, BATMAN-TCM was used to set up five networks of components-target-pathway-disease (MSXG, SGMH, XCH, WLS and Others). To emphasize the important elements of the five networks, we only exhibited the hub targets according to the default criteria (targets with no fewer than 6, 5, 8, 7 and 4 linking compounds for MSXG, SGMH, XCH, WLS and Others, respectively). Finally, these important linking compounds of MSXG, SGMH, XCH, WLS and Others networks were obtained for further analysis.\n\n2.5 ADMET evaluation of the predicted active compounds\nBased on the SwissADME database [21], the physicochemical properties of the active components was predicted, including molecular weight (MW), rotatable bonds count, H-bond acceptors and donors count, TPSA and leadlikeness violations. Second, pharmacokinetic properties was predicted through pkCSM database [22], which contained the absorption (Caco-2 cell permeability, HIA and skin permeability), distribution (VDss, unbound fraction, blood-brain barrier and central nervous system permeability), excretion (total clearance and renal OCT2 substrate) and toxicity (AMES toxicity, maximum tolerated dose, hERG I inhibitor, hERG II inhibitor, oral rat acute toxicity (LD50), hepatotoxicity, skin sensitisation, and minnow toxicity).\n\n2.6 Molecular docking\nTo facilitate drug discovery against COVID-19, we used COVID-19 Docking Server (https://ncov.schanglab.org.cn/index.php) [23] to predict the binding modes between 12 COVID-19 targets and the 20 lead-likeness of QFPD. Specifically, the 10 nonstructural and 2 structural proteins of 2019-nCov were collected (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 and E protein [ion channel]); and the corresponding Protein Data Bank (PDB)codes were 6LU7, 4OW0, 3H5Y (with RNA), 3H5Y (without RNA), 6JYT (ADP site), 6JYT (NCB site), 5C8S (ExoN),5C8S (N7-MTase), 2RHB, 2XYR, 4KYJ, and 5 × 29, respectively. Finally, Discovery Studio software elucidated the 14 best docking results between compounds and the COVID-19 target proteins.\n\n2.7 ACE2 and CD147 expression across tissues and co-expression genes\nTo understand the expression and distribution of ACE2 and CD147 across tissues, a radar plot including 53 tissues was performed through COXPRESdb [24]. And the top 200 co-expression genes of ACE2 and CD147 (P \u003c 1E-16) were obtained, respectively. Then, text mining method from the literature was used to screen for pneumonia-associated genes through COREMINE (http://www.coremine.com/). In addition, co-expression genes of ACE2 in colonic epithelial cells [25] and HCoV-associated host proteins with references [26] were obtained. Finally, we performed UpsetView analysis (http://www.ehbio.com/ImageGP/) between these five sets of proteins and QFPD targets.\n\n2.8 Validation of drug positioning for QFPD against COVID-19 via the robustness of disease network\nSince QFPD effects on COVID-19 via multi-component and multi-target, we evaluate the potential efficacy of QFPD through TCMATCOV platform, which uses the quantitative evaluation algorithm of multi-target drugs to disturb the disease network. Specifically, the disturbing effect of drugs on diseases is simulated by deleting disease network nodes. The disturbance rate of drugs is calculated by comparing the changes of network topology characteristics before and after drug intervention, which is used to evaluate the intervention effect of drugs on diseases. Firstly, COVID-19 disease network was constructed based on specific cytokines of COVID-19 [27] and differentially expressed genes of SARS (GSE36969, GSE51387, GSE68820). Then, this platform uses four kinds of network topology characteristics to evaluate the robustness of COVID-19 network, including network average connectivity, network average shortest path, connectivity centrality and compactness centrality. And the five formulae (MSXG, SGMH, XCH, WLS and Others) disturbance scores are calculated according to the changes before and after drug intervention. Finally, the disturbance effect of the five formulae on the COVID-19 network was compared with null models with the total score of the disturbance, and the higher the value is, the higher the damage degree of drugs to the stability of the network is [12]. We take Banxia tianma baizhu decoction (BXTM) as negative control; and another efficient formula Yi du bi fei decoction (YDBF) as positive control."}

LitCovid-sentences

2_test

{"project":"2_test","denotations":[{"id":"32554251-24735618-6393492","span":{"begin":142,"end":144},"obj":"24735618"},{"id":"32554251-17033281-6393493","span":{"begin":195,"end":197},"obj":"17033281"},{"id":"32554251-22837674-6393494","span":{"begin":236,"end":238},"obj":"22837674"},{"id":"32554251-26400175-6393495","span":{"begin":337,"end":339},"obj":"26400175"},{"id":"32554251-26879404-6393496","span":{"begin":363,"end":365},"obj":"26879404"},{"id":"32554251-15980575-6393497","span":{"begin":1342,"end":1344},"obj":"15980575"},{"id":"32554251-12525261-6393498","span":{"begin":1912,"end":1914},"obj":"12525261"},{"id":"32554251-14597658-6393499","span":{"begin":2045,"end":2047},"obj":"14597658"},{"id":"32554251-28256516-6393500","span":{"begin":2919,"end":2921},"obj":"28256516"},{"id":"32554251-25860834-6393501","span":{"begin":3193,"end":3195},"obj":"25860834"},{"id":"32554251-30462320-6393502","span":{"begin":4747,"end":4749},"obj":"30462320"},{"id":"32554251-31986264-6393503","span":{"begin":6010,"end":6012},"obj":"31986264"},{"id":"32554251-30809144-6393504","span":{"begin":6734,"end":6736},"obj":"30809144"}],"text":"2 Materials and methods\n\n2.1 Data preparation\nCompounds of the main herb in formula MSXG, SGMH, XCH, WLS and Others were searched in TCMSP [13], and screened based on drug-likeness (DL) ≥0.18 [14] and oral bioavailability (OB) ≥30 % [15]. Then, the corresponding Pubchem CIDs of the compounds were retrieved from the Pubchem database [16]. Finally, BATMAN-TCM [17], an bioinformatics analysis tool for studying TCM’s molecular mechanisms, was used to identify potential target genes of the active components (uploaded by Pubchem CIDs). To make the results more credible, we set the cutoff score ≥ 30 as the standard. Finally, to discovery the co-differentially presented targets in the five formulae, we conducted pan-formula analysis using Venn diagrams (http://bioinformatics.psb.ugent.be/webtools/Venn/).\n\n2.2 Functional and pathway enrichment analyses of QFPD targets\nTo better understand the functional involvements of MSXG, SGMH, XCH, WLS and Others targets, bioinformatics analyses of multiple formulae targets were first performed, including Gene Ontology (GO) function term, KEGG biological pathway and OMIM/TTD disease enrichment analyses. Then, kinase, microRNA and transcriptional factor (TF) enrichment analyses of the five formulae targets were conducted using the tool WebGestalt (http://bioinfo.vanderbilt.edu/webgestalt) [18] and the bubble and chord plot map were drawn with the R language ggplot2 and GOplot installation package. P-values were adjusted for multiple testing by Benjamini-Hochberg adjustment.\n\n2.3 Construction of PPI network and MCODE modules analysis\nTo further explore the pharmacological mechanisms, five PPI networks were built including: MSXG, SGMH, XCH, WLS and Others targets PPI network. Specifically, the five kinds of target proteins were respectively uploaded to Metascape to build PPI networks, with the species limited to “Homo sapiens”. Next, MCODE analysis [19], a method for finding densely connected modules in PPI networks, was carried out by Cytoscape 3.2.1 (http://www.cytoscape.org/) [20]. Finally, KEGG (Kyoto Encyclopedia of Genes and Genomes) signaling pathway enrichment analysis was further conducted on the identified functional modules of MSXG, SGMH, XCH, WLS and Others targets PPI networks, respectively.\n\n2.4 Network construction\nBased on the five formulae’s active components, BATMAN-TCM was used to set up five networks of components-target-pathway-disease (MSXG, SGMH, XCH, WLS and Others). To emphasize the important elements of the five networks, we only exhibited the hub targets according to the default criteria (targets with no fewer than 6, 5, 8, 7 and 4 linking compounds for MSXG, SGMH, XCH, WLS and Others, respectively). Finally, these important linking compounds of MSXG, SGMH, XCH, WLS and Others networks were obtained for further analysis.\n\n2.5 ADMET evaluation of the predicted active compounds\nBased on the SwissADME database [21], the physicochemical properties of the active components was predicted, including molecular weight (MW), rotatable bonds count, H-bond acceptors and donors count, TPSA and leadlikeness violations. Second, pharmacokinetic properties was predicted through pkCSM database [22], which contained the absorption (Caco-2 cell permeability, HIA and skin permeability), distribution (VDss, unbound fraction, blood-brain barrier and central nervous system permeability), excretion (total clearance and renal OCT2 substrate) and toxicity (AMES toxicity, maximum tolerated dose, hERG I inhibitor, hERG II inhibitor, oral rat acute toxicity (LD50), hepatotoxicity, skin sensitisation, and minnow toxicity).\n\n2.6 Molecular docking\nTo facilitate drug discovery against COVID-19, we used COVID-19 Docking Server (https://ncov.schanglab.org.cn/index.php) [23] to predict the binding modes between 12 COVID-19 targets and the 20 lead-likeness of QFPD. Specifically, the 10 nonstructural and 2 structural proteins of 2019-nCov were collected (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 and E protein [ion channel]); and the corresponding Protein Data Bank (PDB)codes were 6LU7, 4OW0, 3H5Y (with RNA), 3H5Y (without RNA), 6JYT (ADP site), 6JYT (NCB site), 5C8S (ExoN),5C8S (N7-MTase), 2RHB, 2XYR, 4KYJ, and 5 × 29, respectively. Finally, Discovery Studio software elucidated the 14 best docking results between compounds and the COVID-19 target proteins.\n\n2.7 ACE2 and CD147 expression across tissues and co-expression genes\nTo understand the expression and distribution of ACE2 and CD147 across tissues, a radar plot including 53 tissues was performed through COXPRESdb [24]. And the top 200 co-expression genes of ACE2 and CD147 (P \u003c 1E-16) were obtained, respectively. Then, text mining method from the literature was used to screen for pneumonia-associated genes through COREMINE (http://www.coremine.com/). In addition, co-expression genes of ACE2 in colonic epithelial cells [25] and HCoV-associated host proteins with references [26] were obtained. Finally, we performed UpsetView analysis (http://www.ehbio.com/ImageGP/) between these five sets of proteins and QFPD targets.\n\n2.8 Validation of drug positioning for QFPD against COVID-19 via the robustness of disease network\nSince QFPD effects on COVID-19 via multi-component and multi-target, we evaluate the potential efficacy of QFPD through TCMATCOV platform, which uses the quantitative evaluation algorithm of multi-target drugs to disturb the disease network. Specifically, the disturbing effect of drugs on diseases is simulated by deleting disease network nodes. The disturbance rate of drugs is calculated by comparing the changes of network topology characteristics before and after drug intervention, which is used to evaluate the intervention effect of drugs on diseases. Firstly, COVID-19 disease network was constructed based on specific cytokines of COVID-19 [27] and differentially expressed genes of SARS (GSE36969, GSE51387, GSE68820). Then, this platform uses four kinds of network topology characteristics to evaluate the robustness of COVID-19 network, including network average connectivity, network average shortest path, connectivity centrality and compactness centrality. And the five formulae (MSXG, SGMH, XCH, WLS and Others) disturbance scores are calculated according to the changes before and after drug intervention. Finally, the disturbance effect of the five formulae on the COVID-19 network was compared with null models with the total score of the disturbance, and the higher the value is, the higher the damage degree of drugs to the stability of the network is [12]. We take Banxia tianma baizhu decoction (BXTM) as negative control; and another efficient formula Yi du bi fei decoction (YDBF) as positive control."}

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