The Free Energy Landscape (FEL), a representation of the energetic configurational space underlying the thermodynamics and kinetics of any molecular process, can be used to describe the conformational substate selection processes of molecules. Therefore, in the case of a mutated molecule, if the FEL can be restored to an active state, our working hypothesis is that the activity of the protein will also be restored. The recent work in our lab has been aimed at building tools and gaining foundational knowledge pertaining to the conformational dynamics of proteins, particularly p53. This project focuses on using machine learning on the FEL of p53 to assess the restored native functionality to Y220C mutated p53 by the successful allosteric effector PK11000. Using PK11000 as a model system, four other potential allosteric effectors were tested as well. The FEL can be captured using results collected from running cpptraj on data from Molecular Dynamics (MD) simulations. Using supervised machine learning and energetic training data, we create a multi-layer perceptron neural network model to predict the conformational substate selection of a molecule. The application of this method lies within the ability to determine the capabilities of a drug in rescuing the native functionality of a molecule.
"},{"label":"Degree","value":["Bachelor of Arts with Departmental Honors"]},{"label":"Administrative Note","value":"Drupal Node ID: 33882