从单独使用AlphaFold2的序列来快速估计蛋白质折叠途径.
Liwei Chang1,2,3, Alberto Perez4,5
1Department of Chemistry, University of Florida, Gainesville, FL, USA. liwei.chang@schrodinger.com.
Nature communications
|December 1, 2025
概括
AlphaFold2 (AF2) 隐式学习了蛋白质折叠原理,使其能够快速发现折叠路径. 在没有MSA的情况下运行AF2显示了折叠机制和中间结构.
科学领域:
- 计算生物学 计算生物学
- 结构生物学 结构生物学
- 生物物理学的生物物理.
背景情况:
- AlphaFold2 (AF2) 显著提升了蛋白质结构预测,但其与蛋白质折叠问题的关系仍然不清楚.
- 蛋白质结构预测侧重于静态构造,而蛋白质折叠则关注实现这些结构的动态过程.
研究的目的:
- 调查AlphaFold2是否已暗中学习了蛋白质折叠的原理.
- 探索AF2的学习能量功能的实用性,以发现折叠路径和中间体.
主要方法:
- 运行AlphaFold2在类似ab initio模式下,不包括多重序列对齐 (MSA) 和模板,以采样整个能源景观.
- 分析了超过7000种蛋白质,仅从序列中就能识别出那些折叠的蛋白质.
- 代和回收预测以发现中间结构,并分析具有优化局部相互作用的设计蛋白质.
主要成果:
- 尽管有缺陷,AlphaFold2的学习能量功能能够快速 (几分钟) 发现蛋白质折叠路径.
- 预测有一部分蛋白质仅使用AF2就会从序列折叠,这表明了光滑的学习能量格局.
- 回收的AF2预测揭示了与实验数据一致的中间结构,表明"本地先,全球后"的折叠机制.
结论:
- AlphaFold2已经暗中学习了蛋白质折叠的基本方面,超出了静态结构预测的范围.
- AF2的能源景观有助于探索折叠动态和识别过渡性蛋白质结构.
- 这些发现为AF2的学习能力提供了洞察力,并为研究蛋白质折叠机制提供了新的方法.
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