用迭代生成方法从粗粒度蛋白重建原子级结构，提升精度与效率。
An Iterative Framework for Generative Backmapping of Coarse Grained Proteins
- 分步迭代式生成，结合条件变分自编码器与图神经网络
- 在不同结构蛋白上实现高精度重建，超粗粒度下仍有效
- 适合需要高保真分子模拟的生物物理研究者
基于数据驱动的粗粒度（CG）到细粒度（FG）回映技术常面临精度不足、训练不稳和物理真实性差的问题，尤其在复杂系统如蛋白质中表现明显。本文提出一种新型迭代生成框架，采用条件变分自编码器与图基神经网络，专门应对大尺度生物分子的挑战。该方法实现从粗粒度粒子到全原子细节的逐步精细化重构。我们阐述了迭代生成回映的理论，并通过数值实验验证多步策略的优势，应用于结构差异极大的蛋白质且使用极粗粒度表示时仍表现良好。该多步方法不仅提升重建精度，还使超粗粒度表示下的训练过程更高效。
原文摘要 · Abstract (English)
The techniques of data-driven backmapping from coarse-grained (CG) to fine-grained (FG) representation often struggle with accuracy, unstable training, and physical realism, especially when applied to complex systems such as proteins. In this work, we introduce a novel iterative framework by using conditional Variational Autoencoders and graph-based neural networks, specifically designed to tackle the challenges associated with such large-scale biomolecules. Our method enables stepwise refinement from CG beads to full atomistic details. We outline the theory of iterative generative backmapping and demonstrate via numerical experiments the advantages of multistep schemes by applying them to proteins of vastly different structures with very coarse representations. This multistep approach not only improves the accuracy of reconstructions but also makes the training process more computationally efficient for proteins with ultra-CG representations.
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