绑定器2030:一个定量膜蛋白质组结合数据集,使人工智能驱动的药物发现成为可能
Naoki Tarui1, Masaharu Nakayama1, Thuy Duong Nguyen1
1SEEDSUPPLY INC., 26-1, Muraoka-Higashi 2-Chome, Fujisawa, Kanagawa, 251-0012, Japan.
SLAS discovery : advancing life sciences R & D
|February 25, 2026
概括
研究人员创建了Binder2030,这是一个小型分子联体和跨膜蛋白质的大数据集. 这个资源提供了标准化的测量,以推进药物发现的目标,如GPCRs和离子通道.
科学领域:
- 生物化学 生化学
- 药理学 药理学是指药理学的学科.
- 计算生物学 计算生物学
背景情况:
- 膜蛋白是关键的治疗点,但缺乏全面的定量带结合数据.
- 现有的数据集往往缺乏标准化,阻碍了对比分析和药物发现工作.
研究的目的:
- 为了建立Binder2030,一个精选的小分子配体和跨膜蛋白相互作用的数据集.
- 为各种目标类提供标准化解离常数 (Kd) 测量,用于对不同目标类进行比较分析.
- 为了促进药物发现和基于结构的建模下游应用.
主要方法:
- 在膜部分上使用绑定器选择技术 (BST) 的亲和选择质谱 (ASMS).
- 标准化Kd测量,化学标识符策划和目标注释.
- 开发一个PubChem重叠子集,并为外部验证提供活动注释.
主要成果:
- 绑定器2030数据集包括3,384个小分子配体,针对大约400个跨膜蛋白 (GPCR,SLC,离子通道).
- 标准化的Kd值使得能够对目标类的亲和分布和化学空间进行分析.
- 证明了与基于结构的建模的成功整合,将预测的功率与实验上的亲和力进行比较.
结论:
- 绑定器2030显著扩展了膜蛋白质的定量联结数据.
- 数据集和相关工具支持比较分析,加速新疗法的发现.
- 通过将实验亲和关系与计算预测联系起来,促进基于结构的药物设计.
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