约束脱的隐性扩散用于蛋白质的背面映射
Xu Han1,2,3, Yuancheng Sun1,2,3, Kai Chen3
1University of Chinese Academy of Sciences, Beijing 100190, China.
Journal of chemical theory and computation
|December 27, 2025
概括
我们开发了CODLAD,一种用于蛋白质结构重建的新方法. 它从粗的模拟中高效地生成准确和多样化的全原子模型,克服了现有的后映射技术的局限性.
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:
- 粗粒度 (CG) 模拟提供了高效的蛋白质构造探索.
- 从CG模型 (后映射) 中重建所有原子细节至关重要,但具有挑战性.
- 现有的逆向映射方法难以平衡准确性和形状多样性.
研究的目的:
- 引入一种新的两阶段框架,CODLAD (COnstraint 脱 LAtent 扩散),用于改进蛋白质结构的逆向映射.
- 为了提高效率和准确性,将约束处理与生成过程分开.
- 从CG模型中产生结构有效和多样化的全原子蛋白质构造.
主要方法:
- 开发了一个两阶段的框架:隐性压缩和隐性扩散.
- 将压缩的原子结构压缩成离散的潜在表示,嵌入约束.
- 用于在潜空间中消除扩散,以有效生成全原子结构.
主要成果:
- 在原子精度方面,CODLAD实现了最先进的性能.
- 与现有方法相比,该方法显示出优越的形状多样性.
- 评估显示了高计算效率和在各种蛋白质数据集中强大的概括性.
结论:
- CODLAD有效地解决了当前后映射技术中的权衡问题.
- 该框架能够准确多样化地重建蛋白质结构.
- CODLAD代表了计算蛋白质建模的重大进步.
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