(418d) In-Silico Pipeline to Discover Small Molecules Overcoming Mutation Induced Drug Resistance for EGFR

Mutation-induced therapeutic resistance is one of the central challenges to overcome when developing efficient treatments for non-small cell lung cancer (NSCLC). For instance, certain mutations in the epidermal growth factor receptor (EGFR) gene within patients’ tumors could lead to resistance to available kinase inhibitor therapeutics. Such resistance could be due to the mutation-induced conformational changes in the targeted EGFR protein. This poster presents our effort to develop an in-silico pipeline to predict the mechanisms of mutation-induced drug resistance. We built a structure prediction, conformational exploration, and molecular docking pipeline that combines three computational approaches. The first is AlphaFold2 which can predict 3D conformation of a protein based on its sequence. The second is Molecular Dynamics (MD) simulations performed by Gromacs that can generate possible conformational ensembles for a protein in explicit solvent. The third is Molecular Docking performed by Autodock Vina which can identify potential small-molecule inhibitors. Using our pipeline, we investigated how the mutations of the EGFR gene may result in changes in the binding affinity of small molecules. We first used alphaFold2 to predict the 3D conformation of wild-type and mutant EGFR kinase domain. Then we conducted molecular dynamics simulations to generate multiple possible conformations for these proteins in explicit solvents. Lastly, we carried out molecular docking to screen small molecules that can bind to these conformations. We benchmarked the performance of our in-silico pipeline using a pool of known EGFR kinase inhibitors and known decoy molecules. Our results show the potential of this in-silico pipeline in discovering potential small molecule candidates for structural variants of EGFR.