Ashley Westerfield
Ashley Westerfield, a senior bioengineering major at Stanford University, built machine learning models to predict binding affinity data for potential K-Ras inhibitors.
K-Ras is an oncogenic GTPase that has been implicated in a variety of human colonic and pancreatic cancers.
This summer at the Ó³»´«Ã½ taught me how important it is to have the courage to tackle problems that everyone else considers impossible to solve. Everyone I’ve met here has a seemingly boundless amount of this scientific courage, and engaging with them this summer shaped my decision to pursue a career in research. At the Ó³»´«Ã½, no goal is too lofty, no idea too unusual, no technology too out-of-reach – and I’ve learned that this mindset is a powerful one to bring to science, health, and medicine.Endogenously responsible for controlling cell proliferation, differentiation, and development through binding to GDP/GTP, the K-Ras protein acts as a conformational switch that regulates many downstream pathways in the cell. Mutations in K-Ras – particularly the exchange of glycine for aspartic acid at the 12th amino acid (G12D) – cause the protein to become constitutively active, inducing uncontrolled cell growth. As a result, a small molecule that binds to and inactivates K-Ras can potentially halt cell growth, allowing the cancer to become more treatable by conventional cancer therapeutics like chemotherapy. Our goal is to develop K-Ras inhibitor compounds with nanomolar level affinity. To help develop such a compound, we built 2D and 3D QSAR models, a machine learning technique that allows us to predict the binding affinity (and other properties) of new compounds by analyzing the 2D and 3D structures from a library of known compounds. To validate our models, we applied the leave-one-out cross validation method, and monitored several metrics, including the °ù²,±ç², and RMSE of the known activity against the predicted activity. These models will allow us to predict the binding activity of new K-Ras inhibitors before they are synthesized, which will help us triage compounds that will bind to K-Ras with nanomolar affinity.
Project: Developing high-affinity small molecule inhibitors of mutant K-Ras using 2D and 3D QSAR models
Mentor: Alisha Caliman, Center for the Development of Therapeutics (CDoT)
