大规模的化学蛋白质学加速了连接体的发现,并预测了细胞中的连接体行为
Fabian Offensperger1, Gary Tin1, Miquel Duran-Frigola1,2
1CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria.
概括
研究人员为407个小分子碎片绘制了蛋白质相互作用图,为以前未被准的蛋白质发现药物创造了资源. 这促进了化学蛋白质组学和无毒蛋白质组的药物发现.
科学领域:
- 化学生物学
- 蛋白质组学
- 药物发现
背景情况:
- 蛋白质的化学调节是理解生物学和开发治疗方法的关键.
- 尽管进行了广泛的研究,但80%的人类蛋白质组仍然没有结合.
- 目前的化学蛋白质组学方法面临着可扩展的片段蛋白相互作用发现的吞吐量限制.
研究的目的:
- 创建各种小分子碎片的蛋白质结合倾向的全蛋白质图.
- 在开发化学探测器中证明已识别的碎片蛋白相互作用的实用性.
- 整合机器学习以预测细胞环境中的碎片行为.
主要方法:
- 选407个结构多样化的小分子碎片以寻找蛋白质结合.
- 通过将它们推进到活性化学探针以验证已识别的相互作用,以检测E3无素连接酶,转运体和激酶.
- 使用机器学习二元分类器来预测碎片蛋白相互作用和细胞行为.
主要成果:
- 生成了407个小分子碎片的蛋白质结合倾向的全蛋白质图.
- 成功验证了已识别的片段蛋白相互作用,从而开发了活性化学探针.
- 开发可解释的机器学习模型,用于预测细胞系统中的碎片行为.
结论:
- 开发的碎片蛋白相互作用和预测模型资源有助于研究分子识别.
- 这项工作加速了先前未经药物处理的蛋白质的配体发现,解决了人类蛋白质组中的一个主要缺口.
- 这些发现为推进化学蛋白质组学和开发新疗法提供了基础.
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