Machine learning-guided engineering of genetically encoded fluorescent calcium indicators.

ABSTRACT: 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. As a result, we identified the ensemble-derived GCaMP (eGCaMP) variants, eGCaMP and eGCaMP⁺, which achieve both faster kinetics and larger ∆F/F₀ responses upon stimulation than previously publis

SUBMITTER: Wait SJ 

PROVIDER: S-EPMC11878291 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature