The DOI matches its Crossref record; 119 of the 119 references it cites were found in a registry or an index.
Membrane and matrix localization of proteinases: a common theme in tumor cell invasion and angiogenesis
Open accessAbstract
Green tea polyphenolic catechins have been shown to prevent various types of diseases such as pulmonary hypertension (PAH), cancer and cardiac and neurological disorders. Matrix metalloproteinases (MMPs) play an important role in the development of PAH. The present study demonstrated that among the four green tea catechins (EGCG, ECG, EC and EGC), EGCG and ECG inhibit pro-/active MMP-9 activities in pulmonary artery smooth muscle cell culture supernatant. Based on the above, we investigated the interactions of pro-/active MMP-9 with the green tea catechins by computational methods. In silico molecular docking analysis revealed a strong interaction between pro-/active MMP-9 and EGCG/ECG, and galloyl group appears to be responsible for this enhanced interaction. The molecular docking studies corroborate our experimental observation that EGCG and ECG are mainly active in preventing both the proMMP-9 and MMP-9 activities.
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Cites
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Metadata
- Authors
- Moscatelli, D., & Rifkin, D. B.
- Venue
- Biochimica et Biophysica Acta (BBA) - Reviews on Cancer
- Publisher
- Elsevier BV
- Published
- 1988-08
- Volume
- 948
- Issue
- 1
- Pages
- 67-85
- ISSN
- 0304-419X (ISSN-L)
- OpenAlex
- W1987299633
- Cited by
- 352
- References
- 119
- FWCI
- 15.54
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Moscatelli, D., & Rifkin, D. B. (1988). Membrane and matrix localization of proteinases: a common theme in tumor cell invasion and angiogenesis. Biochimica et Biophysica Acta (BBA) - Reviews on Cancer. https://doi.org/10.1016/0304-419x(88)90005-4
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
