Machine Learning Ensemble Directed Engineering of Genetically Encoded Fluorescent Calcium Indicators.

ABSTRACT: In this study, we focused on the transformative potential of machine learning in the engineering of genetically encoded fluorescent indicators (GEFIs), protein-based sensing tools that are critical for real-time monitoring of biological activity. GEFIs are complex proteins with multiple dynamic states, rendering optimization by trial-and-error mutagenesis a challenging problem. We applied an alternative approach using 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. We used the trained ensemble to perform an in silico functional screen on 1423

SUBMITTER: Wait SJ 

PROVIDER: S-EPMC10441480 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature