bioRxivBioinformaticsPreprint
In-silico predictive identification of K-RasG12V inhibitors in natural compounds
Abstract
As RAS protein is highly significant in signaling pathways, involving cell growth, differentiation and apoptosis; the Ras GTPase proteins play a significant as a master switch in the appearance of many diseases, including 20-30% of all cancers. So, the K-RasG12V mutant was selected as a drug target in present study. This mutant is involved in gastric cancer, lung and pancreatic carcinoma, and colon cancers. So, we employed the structure-based drug design methods and molecular dynamics simulations to undergo virtual screening on natural products small molecules and predicted some new potent therapeutic inhibitors. Finally, ZINC15671852, ZINC85592862, ZINC85567582 and ZINC03616630 final Hits were identified as potent inhibitors from among more than 79,000 bioactive compounds from natural resource. Molecular Mechanics Poisson-Boltzmann Surface Area (MM-P/GBSA) calculation results have also demonstrated that these molecules obtained higher binding free energy than co-crystalized reference ligand.
- research focus: drug response / drug discovery
- approach: machine learning (classical)
- approach: simulation
- Citations
- 0
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