用物理能量反馈提升蛋白质构象生成的合理性
Aligning Protein Conformation Ensemble Generation with Physical Feedback
- 通过能量对齐机制将生成模型与物理模型联动
- 在分子动力学基准上达到当前最优的构象生成质量
- 适合结构生物学与药物设计领域研究者参考
蛋白质动态对其生物功能和性质至关重要，传统研究依赖耗时的分子动力学（MD）模拟。近年来，去噪扩散模型等生成方法通过学习晶体结构分布，实现了高效准确的蛋白质结构预测与构象采样。然而，如何有效融入物理监督仍具挑战，因标准能量目标常导致优化不可行。本文提出能量对齐（EBA）方法，通过物理模型反馈，高效校准生成模型，使其根据能量差异合理平衡不同构象状态。在MD构象集基准测试中，EBA表现出最先进的生成性能。该方法显著提升了生成结构的物理合理性，改善了模型预测效果，为结构生物学与药物发现应用提供了新可能。
原文摘要 · Abstract (English)
Protein dynamics play a crucial role in protein biological functions and properties, and their traditional study typically relies on time-consuming molecular dynamics (MD) simulations conducted in silico. Recent advances in generative modeling, particularly denoising diffusion models, have enabled efficient accurate protein structure prediction and conformation sampling by learning distributions over crystallographic structures. However, effectively integrating physical supervision into these data-driven approaches remains challenging, as standard energy-based objectives often lead to intractable optimization. In this paper, we introduce Energy-based Alignment (EBA), a method that aligns generative models with feedback from physical models, efficiently calibrating them to appropriately balance conformational states based on their energy differences. Experimental results on the MD ensemble benchmark demonstrate that EBA achieves state-of-the-art performance in generating high-quality protein ensembles. By improving the physical plausibility of generated structures, our approach enhances model predictions and holds promise for applications in structural biology and drug discovery.
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