Next Generation Sequencing (NGS) and Artificial Intelligence for structural and Functional Analysis of KRAS G12C in Complex with Novel Inhibitors
Uma Kumari, Karishma Karishma · Journal of Emerging Technologies and Innovative Research · 2025
Lung cancer is still one of the most dangerous cancers in the world, and non-small cell lung cancer (NSCLC) is the most dominant subtype. Among many genetic drivers of NSCLC, the KRAS-G12C mutation is an important one that drives uncontrolled cell growth through sustained activation of MAPK and PI3K/AKT signaling pathways. Nevertheless, inhibition of KRAS mutations has for a long time been elusive because of the high binding affinity of KRAS to GTP and the lack of proper binding sites. New developments in Next-Generation Sequencing (NGS) and Artificial Intelligence (AI) provide exciting prospects for the elucidation and targeting of KRAS-mediated oncogenesis.This research is centered on the integrative analysis of KRAS-G12C with the use of advanced computational approaches. We utilized NGS to identify KRAS-G12C mutations in patient samples and inspected genomic changes using sequence scanning programs like InterProScan. Structural analysis was conducted at high resolution molecular visualization, creating detailed protein-ligand interaction and structural motif visualization, including helices, sheets, and loops. Molecular docking simulations confirmed the engagement of KRAS-G12C with the in-cancer-targeted inhibitor JAB-16 (PDB ID: 9KPM) exhibiting excellent structural resemblance (RMSD scores 0.411 and 0.598) to homologous protein models.AI-based methods, such as AlphaFold for predicting structures and deep learning algorithms for molecular dynamics simulations, were employed to simulate conformational changes and interaction dynamics. Embedding analyses (t-SNE plots, hierarchical clustering, and heatmaps) identified significant biochemical patterns, including conserved functional domains like the G1 P-loop and switch regions important for GTP binding and hydrolysis. Structural validation by ERRAT confirmed high-quality predicted protein models.The results underscore the strength of combining NGS and AI technologies to improve precision oncology through better structural knowledge of KRAS-G12C and drug discovery. This strategy opens up avenues for designing noninvasive imaging probes as well as targeted therapy against KRAS-mutant lung cancer.