机器学习,对接或物理用于对接体诱导的三元复合体的结构预测
Riccardo Solazzo1, Shu-Yu Chen1, Sereina Riniker1
1Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland.
Current opinion in structural biology
|January 31, 2026
概括
向蛋白质解析的嵌合体 (PROTACs) 和分子剂使得向蛋白质降解成为可能. 模拟它们的三元复合体的计算方法正在进步,但预测能力和数据限制仍然存在挑战.
科学领域:
- 生物化学 生物化学
- 结构生物学 结构生物学
- 计算化学计算化学
背景情况:
- PROTACs和分子粘剂对于向蛋白质降解至关重要.
- 一个三元复杂结构 (E3结合酶,结合体,感兴趣的蛋白质) 对于合理的降解剂设计至关重要.
- 实验性结构确定面临着诸如形状灵活性和动态相互作用等挑战.
研究的目的:
- 审查三元复合体计算建模方面的最新进展.
- 批判性地讨论当前计算方法的预测能力和局限性.
- 为了确定基于结构的降解器设计的剩余挑战.
主要方法:
- 检查了多步计算方法 (例如对接).
- 检查了直接复杂预测的单步深度学习方法.
- 在没有实验结构的情况下,对基于结构的设计进行评估的方法.
主要成果:
- 多步骤方法面临采样,准确性和成本限制.
- 单步深度学习方法提供速度,但受训练数据稀缺性的限制.
- 这两种方法都有限制,影响了三元复杂建模的预测能力.
结论:
- 计算机建模对于设计PROTAC和分子粘剂至关重要.
- 需要进一步发展,以克服速度,准确性和数据可用性的限制.
- 应对这些挑战将推动针对蛋白质降解的基于结构的药物设计.
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