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化学蛋白质相互作用组及其在脱靶识别中的应用。

Chemical-protein interactome and its application in off-target identification.

机构信息

Bio-X Center, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030, China.

出版信息

Interdiscip Sci. 2011 Mar;3(1):22-30. doi: 10.1007/s12539-011-0051-8. Epub 2011 Mar 3.

Abstract

Drugs exert their therapeutic and adverse effects by interacting with molecular targets. Although designed to interact with specific targets in a desirable manner, drug molecules often bind to unexpected proteins (off-targets). By activating or inhibiting off-targets and the associated biological processes and pathways, the resulting chemical-protein interactions can influence drug reaction directly or indirectly. Exploring the relationship between drug and off-targets and the downstream drug reaction can help understand the polypharmacology of the drug, hence significantly advance the drug repositioning pipeline and the application of personalized medicine in understanding and preventing adverse drug reaction. This review summarizes works on predicting off-targets via chemical-protein interactome (CPI), an interaction strength matrix of drugs across multiple human proteins aiming at exploring the unexpected drug-protein interactions, with a variety of computational strategies, including docking, chemical structure comparison and text-mining etc. Effective recall on previous knowledge, de novo prediction and subsequent experimental validation conferred us strong confidence in these methods. Such studies present prospect of large scale in silico methodologies for off-target discovery with low cost and high efficiency.

摘要

药物通过与分子靶标相互作用发挥治疗作用和不良反应。尽管药物分子旨在以理想的方式与特定靶标相互作用,但它们经常与意想不到的蛋白质(非靶标)结合。通过激活或抑制非靶标以及相关的生物过程和途径,由此产生的化学-蛋白质相互作用可以直接或间接地影响药物反应。探索药物与非靶标以及下游药物反应之间的关系有助于了解药物的多效性,从而显著推进药物重定位管道和个性化医学在理解和预防药物不良反应方面的应用。本综述总结了通过化学-蛋白质互作组(CPI)预测非靶标的工作,CPI 是一个针对探索意外药物-蛋白质相互作用的药物在多种人类蛋白质上的相互作用强度矩阵,采用了多种计算策略,包括对接、化学结构比较和文本挖掘等。对先前知识的有效回忆、从头预测和随后的实验验证使我们对这些方法充满信心。这些研究为低成本、高效率的大规模计算方法提供了非靶标发现的前景。

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