Abstract

Simple SummaryThe most well-known oncogene and one with the highest rates of mutation across all cancers is K-Ras, also known as the Kirsten rat sarcoma viral oncogene homologue. It is also linked to certain cancers with a high mortality rate, including pancreatic ductal adenocarcinoma (PDAC), non-small-cell lung cancer (NSCLC), and colorectal cancer (CRC). The aim of this study was to make peptides that inhibit K-Ras G12V by computer-aided drug design methods. Our results showed that the top four selected peptides interact with K-Ras more strongly than the reference-peptide and can stop K-Ras from activating. Our binding affinity analyses demonstrated that the developed peptides can inhibit K-Ras and slow cancer growth.Ras plays a pivotal function in cell proliferation and is an important protein in signal transduction pathways. Mutations in genes encoding the Ras protein drive the signaling cascades essential for malignant transformation, tumour angiogenesis, and metastasis and are responsible for above 30% of all human cancers. There is evidence that N-Ras, K-Ras, and H-Ras play significant roles in human cancer. The mutated K-Ras protein is typically observed in malignant growths. Mutant K-Ras is the most common in lung, colon, and pancreatic cancers. The purpose of this research was to create peptides that inhibit K-Ras G12V. The crystal structure of the mutant K-Ras G12V-H-REV107 complex was obtained from a protein data bank. Further, we used a residue scan approach to create unique peptides from the reference peptide (H-REV107). AMBER molecular dynamics simulations were used to test the stability of the top four proposed peptides (based on binding free energies). Our findings showed that the top four selected peptides had stronger interactions with K-Ras than the reference peptide and have the ability to block the activation function of K-Ras. Our extensive analyses of binding affinities showed that our designed peptide possesses the potential to inhibit K-Ras and to reduce the progression of cancer.

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call