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Publication Abstract from PubMed

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.

Directed Evolution of a Selective and Sensitive Serotonin Sensor via Machine Learning.,Unger EK, Keller JP, Altermatt M, Liang R, Matsui A, Dong C, Hon OJ, Yao Z, Sun J, Banala S, Flanigan ME, Jaffe DA, Hartanto S, Carlen J, Mizuno GO, Borden PM, Shivange AV, Cameron LP, Sinning S, Underhill SM, Olson DE, Amara SG, Temple Lang D, Rudnick G, Marvin JS, Lavis LD, Lester HA, Alvarez VA, Fisher AJ, Prescher JA, Kash TL, Yarov-Yarovoy V, Gradinaru V, Looger LL, Tian L Cell. 2020 Dec 23;183(7):1986-2002.e26. doi: 10.1016/j.cell.2020.11.040. Epub, 2020 Dec 16. PMID:33333022^[1]

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.