AlphaFold加速了针对微氨基关联受体1的精神药物激活剂的发现,这些激活剂准了微氨基关联受体1
Alejandro Díaz-Holguín1, Marcus Saarinen2, Duc Duy Vo1
1Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Box 596, SE-751 24 Uppsala, Sweden.
Science advances
|August 7, 2024
概括
使用AlphaFold的人工智能,通过增强对像微氨基关联受体1 (TAAR1) 等标的蛋白质结构预测,显著改善药物发现. 这加快了针对神经精神疾病的强效药物候选者的识别.
科学领域:
- 计算生物学是一种计算生物学.
- 结构生物学是结构生物学.
- 药理学 药理学是指药理学的学科.
背景情况:
- 人工智能 (AI) 正在改变蛋白质结构的预测.
- 精确的蛋白质结构对于药物设计和带发现至关重要.
- 微氨基关联受体1 (TAAR1) 是神经精神疾病的关键点,但其结构是未知的.
研究的目的:
- 为了比较AlphaFold生成的蛋白质结构与传统同类模型在药物发现虚拟查中的有效性.
- 评估人工智能驱动的结构预测对识别TAAR1.1新型配体的影响.
主要方法:
- 通过AlphaFold和同质模型生成的TAAR1模型对超过1600万种化合物的虚拟选.
- 从两种选方法对排名最高的化合物的实验评估.
- 在野生类型和淘汰赛小鼠中对一个有前途的TAAR1激动剂进行了体内测试.
主要成果:
- AlphaFold屏幕识别了25个TAAR1激动剂,达到60%的成功率,是同类模型的两倍多.
- 由AlphaFold衍生的模型导致发现了最强大的TAAR1激动剂.
- 一种选择的TAAR1激动剂在小鼠中表现出生理和抗精神病类效应.
结论:
- 与传统方法相比,AlphaFold产生的蛋白质结构显著加速了药物发现.
- 人工智能驱动的蛋白质结构预测为识别神经精神疾病的新疗法提供了强大的方法.
- 这项研究验证了AlphaFold在推进配体发现和药物开发管道中的实用性.
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