利用物理反馈对齐蛋白质构象系综生成
Aligning Protein Conformation Ensemble Generation with Physical Feedback
AI总结：
提出基于能量的对齐（EBA）方法，利用物理模型反馈校准生成模型以平衡构象状态，在MD系综基准上实现高质量蛋白质系综生成的最先进性能。
摘要：
蛋白质动力学在蛋白质生物功能和性质中起着至关重要的作用，其传统研究通常依赖于在计算机中进行的耗时分子动力学（MD）模拟。生成建模的最新进展，特别是去噪扩散模型，通过学习晶体结构上的分布，实现了高效准确的蛋白质结构预测和构象采样。然而，将物理监督有效整合到这些数据驱动方法中仍然具有挑战性，因为基于能量的目标通常会导致难以处理的优化。在本文中，我们引入了基于能量的对齐（EBA）方法，该方法利用来自物理模型的反馈对齐生成模型，根据能量差异高效校准模型以适当平衡构象状态。在MD系综基准上的实验结果表明，EBA在生成高质量蛋白质系综方面达到了最先进的性能。通过提高生成结构的物理合理性，我们的方法增强了模型预测，并为结构生物学和药物发现领域的应用带来了希望。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
