Research Paper Volume 13, Issue 19 pp 22867—22882

Computational study of effective matrix metalloproteinase 9 (MMP9) targeting natural inhibitors

Naimeng Liu2, , Xinhui Wang3, , Hao Wu1, , Xiaye Lv5, , Haoqun Xie4, , Zhen Guo4, , Jing Wang4, , Gaojing Dou2,4, , Chenxi Zhang1, , Mindan Sun1, ,

  • 1 Department of Nephrology, The First Hospital of Jilin University, Changchun, China
  • 2 Department of Breast Surgery, The First Hospital of Jilin University, Changchun, China
  • 3 Department of Oncology, The First Hospital of Jilin University, Changchun, China
  • 4 Clinical College, Jilin University, Changchun, China
  • 5 Department of Hematology, The First Clinical Medical School of Lanzhou University, Lanzhou, Gansu, China

Received: June 11, 2021       Accepted: September 10, 2021       Published: October 4, 2021
How to Cite

Copyright: © 2021 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Object: The present study screened ideal lead natural compounds that could target and inhibit matrix metalloproteinase 9 (MMP9) protein from the ZINC database to develop drugs for clear cell renal cell carcinoma (CCRCC)-targeted treatment.

Methods: Discovery Studio 4.5 was used to compare and screen the ligands with the reference drug, solasodine, to identify ideal candidate compounds that could inhibit MMP9. The LibDock module was used to analyze compounds that could strongly bind to MMP9, and the top 20 compounds determined by the LibDock score were selected for further research. ADME and TOPKAT modules were used to choose the safe compounds from these 20 compounds. The selected compounds were analyzed using the CDOCKER module for molecular docking and feature mapping for pharmacophore prediction. The stability of these compound–MMP9 complexes was analyzed by molecular dynamic simulation. Cell counting kit-8, colony-forming, and scratch assays were used to analyze the anti-CCRCC effects of these ligands.

Results: Strong binding to MMP9 was exhibited by 6,762 ligands. Among the top 20 compounds, sappanol and sventenin exhibited nearly undefined blood–brain barrier level and lower aqueous solubility, carcinogenicity, and hepatotoxicity than the positive control drug, solasodine. Additionally, these compounds exhibited lower potential energies with MMP9, and the ligand–MMP9 complexes were stable in the natural environment. Furthermore, sappanol inhibited CCRCC cell migration and proliferation.

Conclusion: Sappanol and sventenin are safe and reliable compounds to target and inhibit MMP9. Sappanol can CCRCC cell migration and proliferation. These two compounds may give new thought to the targeted therapy for patients with CCRCC.


CCRCC: Clear cell renal cell carcinoma; MMPs: Matrix metalloproteinases; ECM: Extracellular matrix; CKD: Chronic kidney disease; ADME: Absorption distribution metabolic excretion; HB: Hydrogen bond; BBB level: Blood brain barrier level; CYP2D6: Cytochrome P450 2D6 inhibition; PPB level: Plasma protein binding properties level; DTP: Developmental toxicity potential; NTP: National Toxicology Program dataset.