Computational Design of Potent siRNA for BRAF Oncogene Silencing for Enhancing Cancer Therapy

The discovery of oncogenic BRAF mutations has prompted the development of inhibitors, yet resistance remains widespread. A more effective strategy involves targeting BRAF mRNA with siRNA to overcome resistance to BRAF inhibitors. This study aims to design potent siRNA for BRAF oncogene silencing using a computational approach. The full coding sequence of BRAF was retrieved from the NCBI database and potential siRNAs were predicted using the Ui-Tei, Reynolds, and Amarzguioui rules. Identified siRNAs were further analyzed using various prediction systems and parameters, including their interaction with the hAgo2 protein. The results identified that seven siRNAs (siRNA 23, siRNA 24, siRNA 26, siRNA 34, siRNA 37, and siRNA 38) with high potential for targeting BRAF mRNA. These siRNAs demonstrated a GC content above 30%, a high prediction score, a low probabilities of self-folding, and strong binding affinities to the target mRNA. Among these, three siRNAs (siRNA 28, siRNA 34, and siRNA 38) exhibited the most negative binding energy values, namely -352.88, -353.48, and -356.46 kcal/mol, respectively. Structural analyses of hAgo2, siRNAs, and their interactions revealed that siRNA 34 (5'-UUCCAAAUGCAUAUACAUCUG-3') and siRNA 38 (5'-UUGUUGAUGUUUGAAUAAGGU-3') had the highest potential as BRAF silencers. The siRNA 34 and siRNA 38 exhibited the highest potential for BRAF silencing. This study provides a comprehensive computational approach for designing siRNAs, aiming to enhance the effectiveness of siRNA-based therapeutic strategies.