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通过亚胺配体在 3D 构象动态网络中的液/液界面的图案生成和信息传递,以响应金属阳离子效应物。

Pattern Generation and Information Transfer through a Liquid/Liquid Interface in 3D Constitutional Dynamic Networks of Imine Ligands in Response to Metal Cation Effectors.

机构信息

Laboratoire de Chimie Supramoléculaire, Institut de Science et d'Ingénierie Supramoléculaires (ISIS) , Université de Strasbourg , 8 allée Gaspard Monge , 67000 Strasbourg , France.

出版信息

J Am Chem Soc. 2019 Aug 14;141(32):12724-12737. doi: 10.1021/jacs.9b05438. Epub 2019 Jul 31.

Abstract

The immense discriminative capacity of the human olfactory chemosensory systems relies on the generation of a in response to the interaction of a particular odorant molecule with many different olfactory receptors. In this work, we report the generation of distributional signals by the action of particular effectors, here metal cations, on dynamic covalent libraries (DCLs) of receptor molecules, here ligands for metal cations. Different effectors are discriminated by the formation of different constitutional distributions, which result from the adaptation of the DCL to the action of a particular cation effector through the selection and exchange of components. Compartmentalization by operation in a system of immiscible solvents (here water and chloroform) results in a 3D constitutional dynamic network (CDN), effecting and information transfer between two domains, through the interface from the "writing" input phase (the IN-phase) and the "reading" output phase (the OUT-phase). Here, it is not the selectivity of a specific recognition process between a particular DCL member and a given effector that is key to the information processing, but the change in the distribution of the components and constituents, a or , induced in one phase in response to interaction with a given effector binding and transmitted to the other phase by component and constituent exchange across the phase boundary. Finally, the pattern recognition techniques such as hierarchical cluster analysis (HCA) and principal component analysis (PCA) were successfully applied to analyze the output generated by the action of different effectors on the higher order [5 × 5] DCL. Discrimination between different effectors was characterized by specific domains. Such data processing also opens the way toward extension to much larger DCLs.

摘要

人类嗅觉化学感觉系统的巨大辨别能力依赖于对特定气味分子与许多不同嗅觉受体相互作用的反应生成分布信号。在这项工作中,我们报告了特定效应物(这里是金属阳离子)对受体分子(这里是金属阳离子的配体)的动态共价文库(DCL)的作用产生分布信号。不同的效应物通过形成不同的组成分布来区分,这是由于 DCL 通过选择和交换组件适应特定阳离子效应物的作用而产生的。通过在不混溶溶剂(这里是水和氯仿)系统中的操作进行分隔,导致形成 3D 组成动态网络(CDN),通过从“写入”输入相(IN 相)到“读取”输出相(OUT 相)的界面在两个域之间进行信息传递和信息传递。在这里,关键不是特定识别过程的选择性,即特定 DCL 成员和给定效应物之间的选择性,而是在响应于与给定效应物结合的相互作用而在一个相中诱导的组件和成分的分布变化,即构象或拓扑变化,通过组件和成分交换跨相界传递到另一个相。最后,成功地应用了层次聚类分析(HCA)和主成分分析(PCA)等模式识别技术来分析不同效应物对高阶[5×5]DCL 的作用产生的输出。不同效应物的区分特征在于特定的域。这种数据处理也为扩展到更大的 DCL 开辟了道路。

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