Expanding the olfactory code by in silico decoding of odor-receptor chemical space
Expanding the olfactory code by in silico decoding of odor-receptor chemical space
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DOI：
10.7554/elife.01120
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发表时间：
2013-10-01
期刊：
影响因子：
7.7
通讯作者：
Ray, Anandasankar
中科院分区：
文献类型：
--
作者：
Boyle, Sean Michael;McInally, Shane;Ray, Anandasankar
外周嗅觉系统中的信息编码取决于两个基本因素：个体气味与气味受体子集的相互作用，以及个体受体-气味相互作用引发、激活或抑制的信号模式。我们开发了一种化学信息学管道，可以从大量化学结构（bbb240,000）中预测受体-气味相互作用，这些受体已经被测试到较小的气味面板（类似于100）。使用计算方法，我们首先从已知的单个受体配体中识别出共同的结构特征。然后，我们利用这些特征从果蝇天线中几种气味受体（Ors）的bbbb240000种潜在挥发物中筛选新的候选配体。来自9个or的功能实验支持筛选的高成功率（类似于71%），从而鉴定出许多新的活化剂和抑制剂。这种对受体-气味相互作用的计算预测有可能使生物体中嗅觉受体的系统水平分析成为可能。
Coding of information in the peripheral olfactory system depends on two fundamental factors: interaction of individual odors with subsets of the odorant receptor repertoire and mode of signaling that an individual receptor-odor interaction elicits, activation or inhibition. We develop a cheminformatics pipeline that predicts receptor-odorant interactions from a large collection of chemical structures (>240,000) for receptors that have been tested to a smaller panel of odorants (similar to 100). Using a computational approach, we first identify shared structural features from known ligands of individual receptors. We then use these features to screen in silico new candidate ligands from >240,000 potential volatiles for several Odorant receptors (Ors) in the Drosophila antenna. Functional experiments from 9 Ors support a high success rate (similar to 71%) for the screen, resulting in identification of numerous new activators and inhibitors. Such computational prediction of receptor-odor interactions has the potential to enable systems level analysis of olfactory receptor repertoires in organisms.