机器学习设计了新的GCGR/GLP-1R双抗体,具有增强的生物效能
Anna M Puszkarska1,2, Bruck Taddese3,4, Jefferson Revell3
1Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK.
Nature chemistry
|May 16, 2024
概括
研究人员开发了人工智能模型来设计强大的双激动剂,针对糖尿病和肥胖的葡萄糖受体 (GCGR) 和葡萄糖样-1受体 (GLP-1R). 模型设计的体显示出卓越的生物活性,同时提高了两种受体的效能.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 内分泌学 在内分泌学.
背景情况:
- 针对人类葡萄糖受体 (GCGR) 和葡萄糖类-1受体 (GLP-1R) 的双激动剂对治疗2型糖尿病和肥胖有前途.
- 开发有效的双重激动剂需要在两个受体上具有高强度,这是由于预测新变异活性的实验数据有限而面临的挑战.
研究的目的:
- 评估现有序数据是否可以训练模型准确预测双受体活性.
- 通过使用计算建模来设计具有优化双重激励力的新变体.
主要方法:
- 训练了各种预测模型,包括一个深度的多任务神经网络与多次损失优化,使用人体GCGR和GLP-1R的体外功效数据.
- 采用模型引导的序列优化来设计具有预测双重活性范围的变体.
主要成果:
- 三种模型设计的序列显示出强烈的双重激进作用与增强的生物活性.
- 与最佳训练组双重激动剂相比,在GCGR和GLP-1R两种药物中实现了多达7倍的同时强度提高.
结论:
- 计算模型,特别是深度神经网络,可以有效地预测设计的双受体活性.
- 以模型为指导的优化是开发用于代谢疾病治疗的优质双重激动剂的可行策略.
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