基于AlphaFold2的序列关联对替代蛋白质构造的大规模预测
Myeongsang Lee1, Joseph W Schafer1, Jeshuwin Prabakaran1
1National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA.
Nature communications
|July 2, 2025
概括
一种名为CF-random的新方法预测了多种蛋白质构造,其性能优于现有的折叠交换蛋白质工具,并揭示了5%的大肠杆菌蛋白质潜在的折叠交换.
科学领域:
- 计算生物学是一种计算生物学.
- 结构生物学是结构生物学.
- 蛋白质动力学 蛋白质动力学
背景情况:
- 阿尔法2 (AF2) 的成功启发了多重构造预测方法.
- 目前的方法通常依赖于进化合,并与折叠切换蛋白质作斗争.
- 折叠切换蛋白质在响应刺激时重塑结构和调节功能.
研究的目的:
- 开发一种新的方法,利用AF2在进化合中的结构学习.
- 改进对替代蛋白质构造的预测,特别是对于折叠切换蛋白质.
- 探索埃斯切里希亚大肠杆菌中折叠切换的流行情况.
主要方法:
- 开发了CF-random,一种专注于AF2结构学习的方法.
- 评估CF随机与预测蛋白质构造的现有方法对比.
- 利用序列关联将同源序列模式与结构景观联系起来.
- 在成千上万的大肠杆菌蛋白中进行了盲目搜索.
主要成果:
- 在预测折叠交换机的替代形状方面,CF-random优于其他方法.
- 该方法准确地预测了具有刚性身体运动和局部重新排列的蛋白质构造.
- CF-random 预测了 AlphaFold3.3 错过的折叠交换组件.
- 有证据表明CF-random使用序列关联进行预测.
结论:
- CF-random为预测各种蛋白质构造提供了一种优越的方法.
- 该方法促进了对折叠切换蛋白及其动态的理解.
- CF-random表明,高达5%的大肠杆菌蛋白可能表现出折叠交换行为.
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