ArticlePloS one2025
In silico analysis to explore the therapeutic potential of propolis-derived small molecules as matriptase inhibitors to suppress breast cancer growth and metastasis.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed.
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is a major cause of death in women, and various drug therapies are used for its treatment. However, current therapies have many side effects and limitations. Propolis, a resinous product of bee hives, possesses a variety of biological activities, including anticancer and chemo-protective properties. The present study aimed to investigate the potential suitability of propolis-derived compounds to inhibit matriptase (MT-SP1), a potential protein target for breast cancer treatment, through comprehensive computational analysis. The MT-SP1 protein structure (PDB ID: 1EAX) was retrieved, energy-minimized, and validated. Five propolis-derived compounds with the highest binding energies to MT-SP1 were selected after virtual screening. Molecular docking of these selected ligands revealed binding energies ranging from -8.4 to -9.1 kcal/mol. Stable complex formation was validated by an additional 250 ns of molecular dynamics simulations. The HOMO-LUMO and DFT characteristics provided further evidence of the chemical reactivity and stability of these five ligands at the MT-SP1 active site. Screening of compounds for drug-likeness, pharmacokinetics (ADMET profiles), and toxicity identified two promising small molecules (PubChem IDs of ligands 72307 and 129827386) as potential drug candidates for inhibiting MT-SP1. However, experimental validation through in vitro or in vivo studies is necessary to confirm these computational findings and explore their therapeutic potential for breast cancer treatment.
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