Directed Evolution of a Selective and Sensitive Serotonin Sensor via Machine Learning

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. [Display omitted] •Developed a machine learning approach for rapid binding-pocket redesign•Engineered a high dynamic range, sensitive, selective sensor for 5-HT: iSeroSnFR•Demonstrated the use of iSeroSnFR for fiber photometry in awake behaving mice•Developed a clinically relevant assay using iSeroSnFR for 5-HT transporter function Machine-learning-guided binding-pocket redesign enables engineering of genetically encoded sensor for serotonin that detects serotonin release in freely behaving mice and is used for the development of an assay for serotonin transporter function and modulation by drugs.