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大数据和人工智能发现针对无 3D 结构蛋白质的新型药物,克服了不可成药的靶点。

Big data and artificial intelligence discover novel drugs targeting proteins without 3D structure and overcome the undruggable targets.

机构信息

Jiangsu Key Lab of Drug Screening, China Pharmaceutical University, Nanjing, China.

Institute of Pharmacologic Science, China Pharmaceutical University, Nanjing, China

出版信息

Stroke Vasc Neurol. 2020 Dec;5(4):381-387. doi: 10.1136/svn-2019-000323. Epub 2020 Mar 29.

Abstract

The discovery of targeted drugs heavily relies on three-dimensional (3D) structures of target proteins. When the 3D structure of a protein target is unknown, it is very difficult to design its corresponding targeted drugs. Although the 3D structures of some proteins (the so-called undruggable targets) are known, their targeted drugs are still absent. As increasing crystal/cryogenic electron microscopy structures are deposited in Protein Data Bank, it is much more possible to discover the targeted drugs. Moreover, it is also highly probable to turn previous undruggable targets into druggable ones when we identify their hidden allosteric sites. In this review, we focus on the currently available advanced methods for the discovery of novel compounds targeting proteins without 3D structure and how to turn undruggable targets into druggable ones.

摘要

靶向药物的发现严重依赖于靶蛋白的三维(3D)结构。当靶蛋白的 3D 结构未知时,设计其相应的靶向药物非常困难。尽管一些蛋白质(所谓的不可成药靶点)的 3D 结构已知,但仍缺乏其靶向药物。随着越来越多的晶体/冷冻电子显微镜结构被存入蛋白质数据库,发现靶向药物的可能性更大。此外,当我们确定其隐藏的变构位点时,也极有可能将以前不可成药的靶标转化为可成药的靶标。在这篇综述中,我们重点介绍了目前用于发现无 3D 结构的新型蛋白靶向化合物的先进方法,以及如何将不可成药的靶标转化为可成药的靶标。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f40/7804061/78cb1ce65337/svn-2019-000323f01.jpg

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