Active Ingredients Research of SYF Based on Network Pharmacology, Molecular Docking and SPR
Objective: To study the active ingredients of SYF in the prevention and treatment of COVID-19 using network pharmacology and molecular docking technology. Methods: The active components and targets of SYF for prevention and treatment of COVID-19 were screened by network pharmacology. The SARS-CoV-2 Spike protein was interacted with the screened core molecules by MOE software. Surface plasmon resonance (SPR) was used to determine the intermolecular forces of SYF and selected active ingredients. Results: Through network pharmacological analysis, 185 intersection targets of TCM and disease were obtained. Twenty core active ingredients were obtained, including Indigo, Forsythiaside, Phillyrin, Rutin, etc. Further molecular matchmaking of the core active ingredients showed that Forsythoside B, Forsythoside A, Phillyrin, rutin and other active ingredients with strong affinity compared to the positive compound Molnupiravir were found. SPR assay showed that SYF had A strong affinity with 2019-nCoV Spike, and the active ingredient Forsythoside A had a higher affinity with 2019-nCoV Spike than Molnupiravir, a positive compound already on the market. Conclusions: SYF compound has A strong affinity with 2019-nCoV Spike, and its core components, Forsythoside A and Rutin, have shown strong affinity with 2019-nCoV Spike in molecular docking and SPR experiments, and are guiding ingredients for the study of SYF for the prevention and treatment of COVID-19.
Authors
- Changqiong Xu (ORCID: https://orcid.org/0000-0003-1152-2835)
- X. Li
- Ran Li (ORCID: https://orcid.org/0000-0002-5658-0273)
- Shaojie Li (ORCID: https://orcid.org/0000-0002-8374-9564)
- Liqiong Xia
Institutions
- Yichun University (CN)
- Shaoguan University (CN)
- Shanghai University of Traditional Chinese Medicine (CN)
- Zhuhai Hospital of Integrated Traditional Chinese and Western Medicine (CN)
- Yueyang Hospital (CN)
- Loudi Central Hospital (CN)
Publication Details
- Journal
- American Journal of Health Research
- Published
- 2025-06-22
- DOI
- https://doi.org/10.11648/j.ajhr.20251303.18
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Shaoguan University