STEM, taught by Dr. Crowthers, is a course focused on scientific and technical writing as well as the execution of long-term independent research projects. As part of the first chapter of this course, students complete a six-month independent research project of their choosing, during which they are guided on how to design, conduct, and write about their studies. Shown below is a quad chart representing my research so far.
A study of genomic mutations and structural changes in protein during oncogenesis of lung adenocarcinoma, followed by molecular docking simulations to test docking abilities of mutated receptors.
Lung adenocarcinoma, a subtype of non-small cell lung cancer, is a type of cancer that occurs when in the outer portions of the lung area which is typically initiated by somatic mutations such as KRAS, EGFR, TP53, etc. Targeted therapies are being developed to specifically track down to their somatic mutations and be able to mitigate the cancer at the root level. This approach will open doors for more individualized treatment and better outcomes for individuals regarding cancer treatments outcomes. Datasets from cBioPortal and the COSMIC database were used to track down specific mutations occurring in EGFR and KRAS to determine what mutations are translated to growth of LUAD within a patient. This information made it possible to track mutation to mutated protein that takes part in the prognosis of the cancer within patients. After which databases such as the Protein Data Bank were used to find specific proteins and then continue the analysis on tools such as Pymol to analyze mutation sites as well as their effects on the change of the structure of the protein. Low RMSD values compiled with the placement of the mutation within the protein suggested localized changes which impact the function of the protein. This was followed by molecular docking simulations to closely observe the drug binding affinity of the mutated proteins.
Research Question: How do the KRAS G12C and EGFR L858R mutations alter the protein’s structural and regulatory regions in lung adenocarcinoma, and what implications do these changes have for its oncogenic activity and mutation-specific drug targeting?
Hypothesis:The G12C and L858R cause local structural changes within the protein which affects the drug binding affinity and capabilities of the given mutated protein.
To learn more about my Project: Click this link!
Three-dimensional structures of EGFR and KRAS were obtained from the Protein Data Bank (PDB). Both wild-type proteins and clinically relevant mutations (EGFR L858R and KRAS G12C) were analyzed. Protein structures were prepared by removing water molecules, ions, and any co-crystallized ligands to ensure accurate docking simulations. Hydrogen atoms were added as part of the preparation process. The structures of two targeted cancer therapies—erlotinib (EGFR inhibitor) and sotorasib (KRAS G12C inhibitor)—were obtained from public chemical databases and converted into the appropriate format for docking. Molecular docking simulations were performed using AutoDock Vina to estimate the binding affinity between each drug and both wild-type and mutant protein structures. For each protein–ligand complex, the docking pose with the lowest predicted binding energy (kcal/mol) was selected for analysis. Binding affinities were compared between wild-type and mutant proteins to evaluate how specific mutations influence drug interaction. Structural alignments and RMSD calculations were conducted to assess conformational differences associated with each mutation.
The results of the molecular docking simulations showed differences in binding affinity between wild-type and mutant protein structures. These differences suggest that specific mutations can change how well a drug binds to its target. In some cases, the mutation slightly altered the predicted binding strength, which may reflect structural changes in the protein’s binding pocket. Structural alignments showed that while the overall protein structure remained mostly similar between wild-type and mutant forms, small local changes occurred near the mutation site. Even small changes in this region can influence how a drug fits and interacts with the protein. This helps explain why certain targeted therapies are designed specifically for mutated proteins. Although molecular docking provides useful predictions, it is important to note that these results are computational models. Real biological systems are more complex, and experimental validation would be needed to confirm the findings. This project demonstrates how computational tools can be used to study drug–protein interactions in cancer-related mutations. By comparing wild-type and mutant forms of EGFR and KRAS, it becomes possible to observe how genetic changes may influence targeted therapy effectiveness. The results support the idea that specific mutations can affect drug binding behavior, which is why precision medicine focuses on matching treatments to a patient’s genetic profile. Overall, this project highlights the importance of bioinformatics and molecular modeling in modern cancer research and drug development.