使用基于激素的虚拟选管道发现高亲和性氨基酸
Timothy S Chisholm1, Mark Mackey2, Christopher A Hunter1
1Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
Journal of the American Chemical Society
|July 13, 2023
概括
研究人员开发了一种计算方法来发现神经退行性疾病中常见的粉样β (Aβ1-42) 纤维的新连接物. 确定了五种新的纳米亲和结合剂,展示了该方法的有效性.
科学领域:
- 神经科学
- 医学化学
- 计算生物学
背景情况:
- 纤维蛋白聚合物,如粉样β (Aβ1-42) 是神经退行性疾病的标志.
- 由于结合点的结构信息有限,为这些聚合物设计配体具有挑战性.
研究的目的:
- 开发和验证基于联体的计算虚拟选方法,用于识别Aβ{1-42}纤维的新型高亲和联体.
- 证明这种方法在发现结构独特的粉样配体中的实用性.
主要方法:
- 组建了已知的Aβ{1-42}配体的数据库,以训练基于化学结构的解离常数的预测模型.
- 实施了三步虚拟选管道:分子性质过,使用化学描述器的机器学习和3D场点分析.
- 从ZINC15数据库中选了6.98亿种化合物,并使用硫黄素T光位移测定试验测试了最佳候选物.
主要成果:
- 鉴定了5种新的Aβ1-42) 配体,其解离常数在20-600nM之间.
- 新型连接体具有独特的结构,验证了计算方法.
- 从虚拟选活动中获得了10.9%的实验成功率.
结论:
- 开发的基于配体的虚拟选管道对于发现新型,高亲和度的粉样配体是有效的.
- 这种计算策略为针对神经退行性疾病中难以实现的蛋白质聚合结构提供了强大的工具.
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