Content Tags

There are no tags.

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

Authors
Elizabeth K. Unger, Jacob P. Keller, Michael Altermatt, Ruqiang Liang, Aya Matsui, Chunyang Dong, Olivia J. Hon, Zi Yao, Junqing Sun, Samba Banala, Meghan E. Flanigan, David A. Jaffe, Samantha Hartanto, Jane Carlen, Grace O. Mizuno, Phillip M. Borden, Amol V. Shivange, Lindsay P. Cameron, Steffen Sinning, Suzanne M. Underhill, David E. Olson, Susan G. Amara, Duncan Temple Lang, Gary Rudnick, Jonathan S. Marvin, Luke D. Lavis, Henry A. Lester, Veronica A. Alvarez, Andrew J. Fisher, Jennifer A. Prescher, Thomas L. Kash, Vladimir Yarov-Yarovoy, Viviana Gradinaru, Loren L. Looger, Lin Tian

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.

Stay in the loop.

Subscribe to our newsletter for a weekly update on the latest podcast, news, events, and jobs postings.