A predictive model of voltage gating of BK channels via physical modeling and machine learning.

Abstract

Machine learning has made revolutionary impacts in numerous scientific fields in recent years, especially structural biology. Nonetheless, there remain many challenging problems where data scarcity precludes a purely data-driven approach, such as quantitative modeling of mutational effects on the function of a specific protein. An effective approach for overcoming the data scarcity problem is to incorporate physical principles, such as using information generated from molecular modeling and simulations. Here, we focus on the prediction of voltage gating of the big potassium (BK) channels, which play important roles in muscle contraction and neural transmission. BK channels have been implicated in hypertension, epilepsy and stroke, and yet remains a challenging drug target. At present, cryo-EM structures are available for both the open and closed states, and over 500 mutations have been functionally characterized. We first derived a set of physics-based features to quantify the effects of each possible mutation using computational mutagenesis and molecular simulations, and then trained random forest models using these descriptors on the experimental mutagenesis data set. The resulting model recapitulates the existing mutations with RMSE = 30 mV and R = 0.6 on unseen test data. The model can be further applied to identify potential hotspots for designing novel mutant channels with drastically changed voltage gating properties and to identify possible pathway of allosteric regulation. We expect that the model can be further improved by additional experimental validation and iterative refinement.