Sarah J Wait, Marc Expòsit, Sophia Lin, Michael Rappleye, Justin Daho Lee, Samuel A Colby, Lily Torp, Anthony Asencio, Annette Smith, Michael Regnier, Farid Moussavi-Harami, David Baker, Christina K Kim, Andre Berndt
Here we used machine learning to engineer genetically encoded fluorescent indicators, protein-based sensors critical for real-time monitoring of biological activity. We used machine learning to predict the outcomes of sensor mutagenesis by analyzing established libraries that link sensor sequences to functions. Using the GCaMP calcium indicator as a scaffold, we developed an ensemble of three regression models trained on experimentally derived GCaMP mutation libraries. The trained ensemble performed an in silico functional screen on 1,423 novel, uncharacterized GCaMP variants...
March 2024: Nature computational science