A molecular model of the big-conductance potassium (BK) channels is of great interest as BK channels are a crucial member of the cellular communication network. It is implicated in numerous diseases including muscular dystrophy, epilepsy and stroke, yet remains a challenging drug target since the details of the gating mechanism remain unclear. But importantly, there are cryo-EM structures of both the open and closed conformations, and over 550 mutations have been functionally annotated. Leveraging these structures, I constructed a physics-based description of the effect of each possible mutation using MD simulations and computational mutagenesis, then trained these descriptors on the mutagenesis data set using machine learning methods. The result is a quantitative model predicting the function of any BK mutation. I plan to map the predicted mutation effects to the structure to identify a pathway of residues whose mutations result in large functional perturbations; the result will be a view of the allosteric pathways of BK gating. To validate the predictions and iteratively refine the model, I will submit the most promising predicted mutation sites to experimental collaborators in Jiamin Cui’s lab at WashU.