Directed Evolution of a Selective and Sensitive Serotonin Sensor via Machine Learning.
Cell
; 183(7): 1986-2002.e26, 2020 12 23.
Article
em En
| MEDLINE
| ID: mdl-33333022
ABSTRACT
Serotonin plays a central role in cognition and is the target of most pharmaceuticals for psychiatric disorders. Existing drugs have limited efficacy; creation of improved versions will require better understanding of serotonergic circuitry, which has been hampered by our inability to monitor serotonin release and transport with high spatial and temporal resolution. We developed and applied a binding-pocket redesign strategy, guided by machine learning, to create a high-performance, soluble, fluorescent serotonin sensor (iSeroSnFR), enabling optical detection of millisecond-scale serotonin transients. We demonstrate that iSeroSnFR can be used to detect serotonin release in freely behaving mice during fear conditioning, social interaction, and sleep/wake transitions. We also developed a robust assay of serotonin transporter function and modulation by drugs. We expect that both machine-learning-guided binding-pocket redesign and iSeroSnFR will have broad utility for the development of other sensors and in vitro and in vivo serotonin detection, respectively.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Serotonina
/
Evolução Molecular Direcionada
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Aprendizado de Máquina
Tipo de estudo:
Diagnostic_studies
Limite:
Animals
/
Humans
Idioma:
En
Revista:
Cell
Ano de publicação:
2020
Tipo de documento:
Article