Back

Exploring the potential of Celtis aetnensis compounds through Molecular Docking for Hepatocellular carcinoma.

Nadiger, K. K.; K, R.; Tamiraikani, T.

2024-03-03 cancer biology
10.1101/2024.02.28.582503 bioRxiv
Show abstract

Hepatocellular carcinoma (HCC) accounts for 1% of the liver malignances; HCC progresses in hepatocytes primarily as a result of inflammation, oxidative stress, and primary liver disease. Thanks to the in-silico methods that help in the identification of the targets and the drugs/potential drugs that can inhibit these proteins that cater to the growth of the cancer cells. Plant actives have been long considered as potential sources of anticancer drugs. In this regard, we studied the actives from Celtis tournefortii Lam (Celtis aetnensis) using GCMS analysis. The protein structures of three receptors--NF-B P50 homodimer, FGF receptor 4, and vascular endothelial growth factor receptor 2 (VEGFR2)--were then subjected to docking studies using phytocompounds from Celtis aetnensis. Three receptor proteins were used in the docking analysis against 7 ligands. In this study, the PDB was used to obtain the structures of 3 cancer-related receptor proteins. The proteins were produced by eliminating water molecules, ligands using PyMol, and were then exposed to docking experiments. Docking studies revealed that the compounds with a binding energy ranging from -1.83 kcal/mol to -6.12 kcal/mol. The docking results revealed that eicosatetraenoic acid has a binding energy of-6.12 kcal/mol against the FGFR4 receptor. Sulfadoxines binding energy to the VEGFR2 receptor protein was-4.9 kcal/mol. Promethazine sulfoxide docks with an energy of -5.36 kcal/mol against NFKB P50. By attaching to the protein, these substances demonstrated good inhibitory activity. The results are supportive that these compounds may be used to treat HCC.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.