用三维空间流匹配生成蛋白构象集合，更贴近真实动态行为。
P2DFlow: A Protein Ensemble Generative Model with SE(3) Flow Matching
- 基于SE(3)流匹配，引入额外维度建模构象分布
- 在ATLAS数据集上优于基线模型，还原晶体与模拟中的动态波动
- 适合研究蛋白功能、药物设计等需要动态结构的场景
生物过程、功能与性质与蛋白构象集合密切相关，而非仅由单一稳定构象决定。本文提出P2DFlow，一种基于SE(3)流匹配的蛋白构象集合生成模型。我们设计了适用于流过程的有效先验，并通过增加一维描述集合数据，增强模型对中间状态的区分能力，从而反映构象分布所遵循的物理规律，使先验知识有效引导生成过程。在ATLAS分子动力学数据集上训练与评估，P2DFlow在多项实验中表现优于其他基线模型，成功捕捉到晶体结构和分子模拟中可观测的动态波动。作为蛋白分子模拟的潜在代理，高质量生成的构象集合可显著促进对蛋白功能在多种场景下的理解。代码已开源。
原文摘要 · Abstract (English)
Biological processes, functions, and properties are intricately linked to the ensemble of protein conformations, rather than being solely determined by a single stable conformation. In this study, we have developed P2DFlow, a generative model based on SE(3) flow matching, to predict the structural ensembles of proteins. We specifically designed a valuable prior for the flow process and enhanced the model's ability to distinguish each intermediate state by incorporating an additional dimension to describe the ensemble data, which can reflect the physical laws governing the distribution of ensembles, so that the prior knowledge can effectively guide the generation process. When trained and evaluated on the MD datasets of ATLAS, P2DFlow outperforms other baseline models on extensive experiments, successfully capturing the observable dynamic fluctuations as evidenced in crystal structure and MD simulations. As a potential proxy agent for protein molecular simulation, the high-quality ensembles generated by P2DFlow could significantly aid in understanding protein functions across various scenarios. Code is available at https://github.com/BLEACH366/P2DFlow
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