首页> 外文期刊>Perspectives in Drug Discovery and Design >Discovering high-affinity ligands from the computationally predicted structures and affinities of small molecules bound to a target: A virtual screening approach
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Discovering high-affinity ligands from the computationally predicted structures and affinities of small molecules bound to a target: A virtual screening approach

机译:从与目标结合的小分子的计算预测结构和亲和力中发现高亲和力配体:虚拟筛选方法

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We describe a 'virtual NMR screening' method to assist in the design of inhibitors that occupy different sites within a target. We dock small molecules into the active site of an enzyme and score them. Keeping the tightest-binding lead fixed in space, we dock and score other small molecules in its presence. Using this approach, linker groups are used to join the compounds together to form a high-affinity inhibitor. We present validation of our computational approach by reproducing experimental results for FKBP and stromelysin. Docking simulations are not subject to experimental problems such as proteolysis, protein or compound insolubility, or enzyme size. Because docking is fast and our scoring method can distinguish between high- and low-affinity inhibitors, this docking procedure shows promise as integral part of a drug-design strategy.
机译:我们描述了一种“虚拟NMR筛选”方法,以协助设计在靶标内占据不同位点的抑制剂。我们将小分子对接至酶的活性位点并对其评分。保持最紧密结合的导线固定在太空中,我们对接并在存在的其他小分子上刻痕。使用这种方法,使用连接基团将化合物连接在一起以形成高亲和力抑制剂。我们通过重现FKBP和溶血素的实验结果来验证我们的计算方法。对接模拟不受实验问题的影响,例如蛋白水解,蛋白质或化合物的不溶性或酶的大小。因为对接速度很快,并且我们的评分方法可以区分高亲和力和低亲和力抑制剂,所以这种对接程序显示出有望成为药物设计策略不可或缺的一部分。

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