Predicting the mechanism of Tiannanxing-Shengjiang drug pair in treating pain using network pharmacology and molecular docking technology
Copyright© Bentham Science Publishers; For any queries, please email at epubbenthamscience.net..
OBJECTIVE: This study aimed to analyze the potential targets and mechanism of Tiannanxing-Shengjiang drug pair in pain treatment using network pharmacology and molecular docking technology.
METHODS: The active components and target proteins of Tiannanxing-Shengjiang were obtained from the TCMSP database. The pain-related genes were acquired from the DisGeNET database. The common target genes between Tiannanxing-Shengjiang and pain were identified and subjected to the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway enrichment analyses on the DAVID website. AutoDockTools and molecular dynamics simulation analysis were used to assess the binding of the components with the target proteins.
RESULTS: Ten active components were screened out, such as stigmasterol, β-sitosterol, and dihydrocapsaicin. A total of 63 common targets between the drug and pain were identified. GO analysis showed that the targets were mainly associated with biological processes such as inflammatory response and forward regulation of the EKR1 and EKR2 cascade. KEGG analysis revealed 53 enriched pathways, including pain-related calcium signaling, cholinergic synaptic signaling, and serotonergic pathway. Five compounds and 7 target proteins showed good binding affinities. These data suggest that Tiannanxing-Shengjiang may alleviate pain through specific targets and signaling pathways.
CONCLUSION: The active ingredients in Tiannanxing-Shengjiang might alleviate pain by regulating genes such as CNR1, ESR1, MAPK3, CYP3A4, JUN, and HDAC1 through the signaling pathways including intracellular calcium ion conduction, cholinergic prominent signaling, and cancer signaling pathway.
Medienart: |
E-Artikel |
---|
Erscheinungsjahr: |
2023 |
---|---|
Erschienen: |
2023 |
Enthalten in: |
Zur Gesamtaufnahme - year:2023 |
---|---|
Enthalten in: |
Current computer-aided drug design - (2023) vom: 25. Mai |
Sprache: |
Englisch |
---|
Beteiligte Personen: |
Wang, Boning [VerfasserIn] |
---|
Links: |
---|
Themen: |
Journal Article |
---|
Anmerkungen: |
Date Revised 26.05.2023 published: Print-Electronic Citation Status Publisher |
---|
doi: |
10.2174/1573409919666230525122447 |
---|
funding: |
|
---|---|
Förderinstitution / Projekttitel: |
|
PPN (Katalog-ID): |
NLM357340477 |
---|
LEADER | 01000naa a22002652 4500 | ||
---|---|---|---|
001 | NLM357340477 | ||
003 | DE-627 | ||
005 | 20231226072400.0 | ||
007 | cr uuu---uuuuu | ||
008 | 231226s2023 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.2174/1573409919666230525122447 |2 doi | |
028 | 5 | 2 | |a pubmed24n1191.xml |
035 | |a (DE-627)NLM357340477 | ||
035 | |a (NLM)37231756 | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
100 | 1 | |a Wang, Boning |e verfasserin |4 aut | |
245 | 1 | 0 | |a Predicting the mechanism of Tiannanxing-Shengjiang drug pair in treating pain using network pharmacology and molecular docking technology |
264 | 1 | |c 2023 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a ƒaComputermedien |b c |2 rdamedia | ||
338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
500 | |a Date Revised 26.05.2023 | ||
500 | |a published: Print-Electronic | ||
500 | |a Citation Status Publisher | ||
520 | |a Copyright© Bentham Science Publishers; For any queries, please email at epubbenthamscience.net. | ||
520 | |a OBJECTIVE: This study aimed to analyze the potential targets and mechanism of Tiannanxing-Shengjiang drug pair in pain treatment using network pharmacology and molecular docking technology | ||
520 | |a METHODS: The active components and target proteins of Tiannanxing-Shengjiang were obtained from the TCMSP database. The pain-related genes were acquired from the DisGeNET database. The common target genes between Tiannanxing-Shengjiang and pain were identified and subjected to the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway enrichment analyses on the DAVID website. AutoDockTools and molecular dynamics simulation analysis were used to assess the binding of the components with the target proteins | ||
520 | |a RESULTS: Ten active components were screened out, such as stigmasterol, β-sitosterol, and dihydrocapsaicin. A total of 63 common targets between the drug and pain were identified. GO analysis showed that the targets were mainly associated with biological processes such as inflammatory response and forward regulation of the EKR1 and EKR2 cascade. KEGG analysis revealed 53 enriched pathways, including pain-related calcium signaling, cholinergic synaptic signaling, and serotonergic pathway. Five compounds and 7 target proteins showed good binding affinities. These data suggest that Tiannanxing-Shengjiang may alleviate pain through specific targets and signaling pathways | ||
520 | |a CONCLUSION: The active ingredients in Tiannanxing-Shengjiang might alleviate pain by regulating genes such as CNR1, ESR1, MAPK3, CYP3A4, JUN, and HDAC1 through the signaling pathways including intracellular calcium ion conduction, cholinergic prominent signaling, and cancer signaling pathway | ||
650 | 4 | |a Journal Article | |
650 | 4 | |a Molecular docking technology | |
650 | 4 | |a Shengjiang | |
650 | 4 | |a Tiannanxing | |
650 | 4 | |a network pharmacology | |
650 | 4 | |a pain | |
650 | 4 | |a signaling pathway | |
700 | 1 | |a Wang, Yanlei |e verfasserin |4 aut | |
700 | 1 | |a Mao, Peng |e verfasserin |4 aut | |
700 | 1 | |a Zhang, Yi |e verfasserin |4 aut | |
700 | 1 | |a Li, Yifan |e verfasserin |4 aut | |
700 | 1 | |a Liu, Xing |e verfasserin |4 aut | |
700 | 1 | |a Fan, Bifa |e verfasserin |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Current computer-aided drug design |d 2008 |g (2023) vom: 25. Mai |w (DE-627)NLM191691046 |x 1875-6697 |7 nnns |
773 | 1 | 8 | |g year:2023 |g day:25 |g month:05 |
856 | 4 | 0 | |u http://dx.doi.org/10.2174/1573409919666230525122447 |3 Volltext |
912 | |a GBV_USEFLAG_A | ||
912 | |a GBV_NLM | ||
951 | |a AR | ||
952 | |j 2023 |b 25 |c 05 |