用分阶段扩散模型实现蛋白原子结构重建，兼顾精度与多样性。
Constraint Decoupled Latent Diffusion for Protein Backmapping
- 先将原子结构压缩为带约束的离散潜在表示，再在潜空间生成
- 在多个蛋白数据集上达到最高精度和最广泛构象多样性
- 无需复杂约束处理，适合蛋白构象采样与结构建模研究
粗粒度（CG）分子动力学模拟可高效探索蛋白构象集合。但从粗粒度结构重建原子细节（即回溯建模）仍是难题。现有方法在保持原子精度与探索多样化构象间存在固有权衡，常需复杂的约束处理或大量优化步骤。为此，我们提出一种名为CODLAD（COnstraint Decoupled LAtent Diffusion）的两阶段框架。该框架首先将原子结构压缩为显式嵌入结构约束的离散潜在表示，从而将约束处理与生成过程解耦；随后在该潜在空间中进行高效的去噪扩散，生成结构合理且多样的全原子构象。在多个蛋白数据集上的综合评估表明，CODLAD在原子精度、构象多样性及计算效率方面均达到当前最优水平，并在不同蛋白系统间展现出强泛化能力。代码已开源：https://github.com/xiaoxiaokuye/CODLAD。
原文摘要 · Abstract (English)
Coarse-grained (CG) molecular dynamics simulations enable efficient exploration of protein conformational ensembles. However, reconstructing atomic details from CG structures (backmapping) remains a challenging problem. Current approaches face an inherent trade-off between maintaining atomistic accuracy and exploring diverse conformations, often necessitating complex constraint handling or extensive refinement steps. To address these challenges, we introduce a novel two-stage framework, named CODLAD (COnstraint Decoupled LAtent Diffusion). This framework first compresses atomic structures into discrete latent representations, explicitly embedding structural constraints, thereby decoupling constraint handling from generation. Subsequently, it performs efficient denoising diffusion in this latent space to produce structurally valid and diverse all-atom conformations. Comprehensive evaluations on diverse protein datasets demonstrate that CODLAD achieves state-of-the-art performance in atomistic accuracy, conformational diversity, and computational efficiency while exhibiting strong generalization across different protein systems. Code is available at https://github.com/xiaoxiaokuye/CODLAD.
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