Multi-decade efforts have yielded voltage gating voltage (∆V1/2) data on almost 500 single mutants of the big potassium (BK) channel. The left panel shows all residues of the BK channel where at least one mutation has been characterized experimentally. While representing enormous efforts, these data alone remain too scarce for deriving quantitative models for predicting the mutational effects using data-driven machine learning approaches. By incorporating physical features derived from molecular modeling and simulations, we show that a random forest model trained using 80% of the experimental shift in gating voltage is able to predict unseen experimental data with 0.79 correlation. The model is also able to capture a central role of hydrophobic gating in BK channels as well as new experimental results on several novel mutations. Nordquist et al 2023
Image Credit: Erik Nordquist, enordquist@umass.edu
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