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使用药物向相互作用预测模型发现了一种新型DCAF1干:将机器学习推广到新的药物向

Serah W Kimani^1,2, Julie Owen³, Stuart R Green¹

¹Structural Genomics Consortium, University of Toronto, Toronto, Ontario M5G 1L7, Canada.

Journal of chemical information and modeling

|June 23, 2023

概括

研究人员确定了DCAF1的新药候选者,DCAF1是细胞降解途径中的关键蛋白质. 这项研究展示了人工智能驱动的虚拟查在没有先前知识的情况下发现配体的力量,推进了针对性蛋白质降解策略.

科学领域:

生物化学生物化学
分子生物学分子生物学
药物发现药物发现药物发现

背景情况:

DCAF1作为CRL4和EDVP E3泛素合酶的关键基质招募子单元.
DCAF1 WDR 域结合了基质,并且是lentiviruses 针对宿主因子降解的目标.
对于开发针对蛋白质降解的化学抑制剂和PROTACs来说,DCAF1是一个有前途的目标.

研究的目的:

用人工智能驱动的虚拟选来识别DCAF1 WDR域的选择性配体.
通过生物物理和晶体学方法验证预测的连接体.
展示AI在对以前未结合的标的联体发现中的有效性.

主要方法:

使用MatchMaker模型进行蛋白质基因尺度药物向相互作用预测.
化学信息学过和分子对接用于配体识别.
生物物理查 (例如SPR,ITC) 和X射线晶体学用于验证.

主要成果:

对 DCAF1 WDR 域的选择性配体的识别.
预测的结合剂的实验证实.
X射线结晶学揭示了中部WDR域腔内的联体结合.
证明了人工智能虚拟选的成功应用,没有先前的带数据.

结论:

人工智能支持的虚拟选对于发现新型目标的配体,如DCAF1.1,是有效的.
这种方法加速了化学抑制剂和PROTACs的发展.
已识别的配体为针对DCAF1.1的进一步治疗开发提供了基础.

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