Machine learning-guided engineering of genetically encoded fluorescent calcium indicators.
Nat Comput Sci
; 4(3): 224-236, 2024 Mar.
Article
em En
| MEDLINE
| ID: mdl-38532137
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 published fast variants. Furthermore, we identified a combinatorial mutation with extraordinary dynamic range, eGCaMP2+, which outperforms the tested sixth-, seventh- and eighth-generation GCaMPs. These findings demonstrate the value of machine learning as a tool to facilitate the efficient engineering of proteins for desired biophysical characteristics.
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Cálcio
/
Sinalização do Cálcio
Idioma:
En
Revista:
Nat Comput Sci
/
Nat. comput. sci
/
Nature computational science
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Estados Unidos