CSC01 shows promise as a potential inhibitor of the oncogenic G13D mutant of KRAS: an in silico approach

Evolution of computational techniques against various KRAS mutants in search for therapeutic drugs: a review article

KRAS was (Kirsten rat sarcoma viral oncogene homolog) revealed as an important target in current therapeutic cancer research because alteration of RAS (rat sarcoma viral oncogene homolog) protein has a critical role in malignant modification, tumor angiogenesis, and metastasis. For cancer treatment, designing competitive inhibitors for this attractive target was difficult. Nevertheless, computational investigations of the protein's dynamic behavior displayed the existence of temporary pockets that could be used to design allosteric inhibitors. The last decade witnessed intensive efforts to discover KRAS inhibitors. In 2021, the first KRAS G12C covalent inhibitor, AMG 510, received FDA (Food and drug administration) approval as an anticancer medication that paved the path for future treatment strategies against this target. Computer-aided drug designing discovery has long been used in drug development research targeting different KRAS mutants. In this review, the major breakthroughs in computational methods adapted to discover novel compounds for different mutations have been discussed. Undoubtedly, virtual screening and molecular dynamic (MD) simulation and molecular docking are the most considered approach, producing hits that can be employed in subsequent refinements. After comprehensive analysis, Afatinib and Quercetin were computationally identified as hits in different publications. Several authors conducted covalent docking studies with acryl amide warheads groups containing inhibitors. Future studies are needed to demonstrate their true potential. In-depth studies focusing on various allosteric pockets demonstrate that the switch I/II pocket is a suitable site for drug designing. In addition, machine learning and deep learning based approaches provide new insights for developing anti-KRAS drugs. We believe that this review provides extensive information to researchers globally and encourages further development in this particular area of research.

Investigation of the MDM2-binding potential of de novo designed peptides using enhanced sampling simulations

The tumor suppressor p53 plays a crucial role in cellular responses to various stresses, regulating key processes such as apoptosis, senescence, and DNA repair. Dysfunctional p53, prevalent in approximately 50 % of human cancers, contributes to tumor development and resistance to treatment. This study employed deep learning-based protein design and structure prediction methods to identify novel high-affinity peptide binders (Pep1 and Pep2) targeting MDM2, with the aim of disrupting its interaction with p53. Extensive all-atom molecular dynamics simulations highlighted the stability of the designed peptide in complex with the target, supported by several structural analyses, including RMSD, RMSF, Rg, SASA, PCA, and free energy landscapes. Using the steered molecular dynamics and umbrella sampling simulations, we elucidate the dissociation dynamics of p53, Pep1, and Pep2 from MDM2. Notable differences in interaction profiles were observed, emphasizing the distinct dissociation patterns of each peptide. In conclusion, the results of our umbrella sampling simulations suggest Pep1 as a higher-affinity MDM2 binder compared to p53 and Pep2, positioning it as a potential inhibitor of the MDM2-p53 interaction. Using state-of-the-art protein design tools and advanced MD simulations, this study provides a comprehensive framework for rational in silico design of peptide binders with therapeutic implications in disrupting MDM2-p53 interactions for anticancer interventions.