用物理规律指导蛋白折叠，生成更真实的新蛋白结构。
Let Physics Guide Your Protein Flows: Topology-aware Unfolding and Generation
- 基于经典物理设计非线性去噪过程，保持拓扑完整
- 在无条件生成中达到当前最优，能精准折叠单体序列
- 适合蛋白质设计与生成任务，兼顾真实性和创新性
蛋白质结构预测与折叠是理解生物学的基础，深度学习的进展正重塑该领域。基于扩散的生成模型已革新蛋白质设计，可创造新蛋白。但这些方法常忽略蛋白的内在物理真实性，因去噪过程缺乏物理基础。为此，我们提出一种基于物理的非线性去噪流程，从经典物理出发，将蛋白展开为二级结构（如α螺旋、β链），同时保持键合关系与避免碰撞。接着将此过程与SE(3)上的流匹配框架结合，高保真建模蛋白骨架的不变分布，并融合序列信息，实现序列条件下的折叠与生成。实验表明，该方法在无条件蛋白生成上达到当前最优，生成更具可设计性与新颖性的蛋白结构，且能准确将单体序列折叠为精确构象。
原文摘要 · Abstract (English)
Protein structure prediction and folding are fundamental to understanding biology, with recent deep learning advances reshaping the field. Diffusion-based generative models have revolutionized protein design, enabling the creation of novel proteins. However, these methods often neglect the intrinsic physical realism of proteins, driven by noising dynamics that lack grounding in physical principles. To address this, we first introduce a physically motivated non-linear noising process, grounded in classical physics, that unfolds proteins into secondary structures (e.g., alpha helices, linear beta sheets) while preserving topological integrity--maintaining bonds, and preventing collisions. We then integrate this process with the flow-matching paradigm on SE(3) to model the invariant distribution of protein backbones with high fidelity, incorporating sequence information to enable sequence-conditioned folding and expand the generative capabilities of our model. Experimental results demonstrate that the proposed method achieves state-of-the-art performance in unconditional protein generation, producing more designable and novel protein structures while accurately folding monomer sequences into precise protein conformations.
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