通过机器学习模型快速预测可折叠性的关键残留物,可以设计具有超稳定的受约束基架的高功能库
Fei Cai1, Yuehua Wei1, Daniel Kirchhofer1
1Departments of Biological Chemistry, Genentech, Inc., South San Francisco, California, United States of America.
PLoS computational biology
|November 18, 2024
概括
本研究介绍了一种机器学习模型,用于设计稳定的疗法. 通过保留关键序列,新图书馆显示了药物开发的折叠效率提高.
科学领域:
- 生物化学 生化学
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 类是有前途的治疗方法,但面临着稳定性挑战.
- 双硫化物受约束 (DCP) 和高稳定性受约束 (HCP) 提供稳定的支架.
- 通过考虑脚手架折叠性,可以改进以前的酸发现平台.
研究的目的:
- 开发一种设计多样化的DCP库的方法,以保持支架可折叠性.
- 改进用于治疗开发的类库的设计.
- 提高基于的治疗发现的效率.
主要方法:
- 使用酵母表面显示 (YSD) 和枪支氨酸扫描生成了一个大型数据集.
- 训练了一种机器学习 (ML) 模型,应用自然语言理解技术.
- 验证了ML模型在预测折叠性和识别关键残留物的准确性.
主要成果:
- ML模型准确地预测了在各种序列中的折叠性.
- 确定了维护体支架稳定性的关键残留物.
- 设计了一个基于de novo设计的HCP,具有优化折叠效率的新型类库.
- 新图书馆的板式试验产生了具有良好的折叠性质的有希望的治疗候选者.
结论:
- 这项工作推进了和小蛋白质域库的设计原则.
- 开发的方法增强了稳定和可折叠的基架的创建.
- 这些发现支持未来基于的治疗方法的高效开发.
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