研究開發血清素檢測新方法
作者：
小柯機器人發布時間：2020/12/17 15:12:11
美國加州大學Lin Tian和霍華德·休斯醫學院Loren L. Looger研究組合作取得最新進展。他們通過機器學習指導選擇性敏感的血清素傳感器的進化。這一研究成果於2020年12月16日發表在國際頂尖學術期刊《細胞》雜誌上。
在機器學習的指導下，他們開發並應用了綁定袋重新設計策略，以創建高性能的可溶性螢光5-羥色胺傳感器（iSeroSnFR），從而可以光學檢測毫秒級的5-羥色胺瞬變。他們證明，iSeroSnFR可用於檢測恐懼條件、社交互動和睡眠/覺醒過渡過程中行為自由的小鼠中血清素釋放。他們還開發了一種5-羥色胺轉運蛋白功能和藥物調節的可靠方法。他們希望機器學習指導的綁定口袋重新設計和iSeroSnFR分別在開發其他傳感器，以及體外和體內血清素檢測方面具有廣泛用途。
據了解，5-羥色胺在認知中起著核心作用，並且是大多數精神疾病藥物的靶標。現有藥物療效有限。創建改進版本將需要更好地了解5-羥色胺能迴路，這已因人們無法以高時空解析度監測5-羥色胺的釋放和運輸而受到阻礙。
附：英文原文
Title: Directed Evolution of a Selective and Sensitive Serotonin Sensor via Machine Learning
Author: 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
Issue&Volume: 2020-12-16
Abstract: Serotonin plays a central role in cognition and is the target of most pharmaceuticalsfor psychiatric disorders. Existing drugs have limited efficacy; creation of improvedversions will require better understanding of serotonergic circuitry, which has beenhampered by our inability to monitor serotonin release and transport with high spatialand temporal resolution. We developed and applied a binding-pocket redesign strategy,guided by machine learning, to create a high-performance, soluble, fluorescent serotoninsensor (iSeroSnFR), enabling optical detection of millisecond-scale serotonin transients.We demonstrate that iSeroSnFR can be used to detect serotonin release in freely behavingmice during fear conditioning, social interaction, and sleep/wake transitions. Wealso developed a robust assay of serotonin transporter function and modulation bydrugs. We expect that both machine-learning-guided binding-pocket redesign and iSeroSnFRwill have broad utility for the development of other sensors and in vitro and in vivo serotonin detection, respectively.
DOI: 10.1016/j.cell.2020.11.040
Source: https://www.cell.com/cell/fulltext/S0092-8674(20)31612-3
Cell：《細胞》，創刊於1974年。隸屬於細胞出版社，最新IF：36.216