美国西北大学范伯格医学院Gabriel J. Rocklin小组取得一项新突破。他们研制了大规模发现、分析和设计蛋白质能量景观。相关论文于2026年5月13日发表于国际顶尖学术期刊《自然》杂志上。

研究介绍了一种多重实验方法，在平行主题完整蛋白质氢-氘交换质谱法中分析数百个蛋白质结构域的构象波动能量。研究小组分析了5778个结构域，长度为28-64个氨基酸，揭示了构象波动的隐藏变化，甚至在具有相同折叠和全局折叠稳定性的序列之间也是如此。13个结构域的位分辨氢交换核磁共振分析表明，这些波动通常涉及整个二级结构单元，其稳定性低于整体褶皱。其结构域的计算模型确定了与实验观察到的波动相关的结构特征，使他们能够设计稳定低稳定性结构段的突变。他们的数据集使新的基于机器学习的蛋白质能量格局分析成为可能，他们的实验方法有望在相当大的范围内描绘这些格局。

研究人员表示，所有折叠的蛋白质都在低能的天然结构和部分或完全展开的高能构象之间不断波动。这些罕见的状态影响蛋白质的功能、相互作用、聚集和免疫原性，但它们仍然远远少于蛋白质的天然状态。虽然天然蛋白质结构现在通常可以以令人印象深刻的精度预测，但构象波动及其能量在很大程度上仍然是不可见的和不可预测的，并且实验挑战阻碍了可以改进机器学习和基于物理的建模的大规模测量。

附：英文原文

Title: Large-scale discovery, analysis and design of protein energy landscapes

Author: Ferrari, llan J. R., Dixit, Sugyan M., Thibeault, Jane, Garcia, Mario, Houliston, Scott, Ludwig, Robert W., Notin, Pascal, Phoumyvong, Claire M., Martell, Cydney M., Jung, Michelle D., Tsuboyama, Kotaro, Carter, Lauren, Arrowsmith, Cheryl H., Guttman, Miklos, Rocklin, Gabriel J.

Issue&Volume: 2026-05-13

Abstract: All folded proteins continuously fluctuate between their low-energy native structures and higher-energy conformations that can be partially or fully unfolded. These rare states influence protein function1,2, interactions3, aggregation4,5,6,7 and immunogenicity8,9, yet they remain far less understood than protein native states. Although native protein structures are now often predictable with impressive accuracy, conformational fluctuations and their energies remain largely invisible10 and unpredictable11,12,13,14, and experimental challenges have prevented large-scale measurements that could improve machine learning and physics-based modelling. Here we introduce a multiplexed experimental approach to analyse the energies of conformational fluctuations for hundreds of protein domains in parallel using intact protein hydrogen–deuterium exchange mass spectrometry. We analysed 5,778 domains 28–64 amino acids in length, revealing hidden variation in conformational fluctuations, even between sequences sharing the same fold and global folding stability. Site-resolved hydrogen exchange nuclear magnetic resonance analysis of 13 domains showed that these fluctuations often involve entire secondary structural elements with lower stability than the overall fold. Computational modelling of our domains identified structural features that correlated with the experimentally observed fluctuations, enabling us to design mutations that stabilized low-stability structural segments. Our dataset enables new machine-learning-based analysis of protein energy landscapes, and our experimental approach promises to profile these landscapes at considerable scale.

DOI: 10.1038/s41586-026-10465-z

Source: https://www.nature.com/articles/s41586-026-10465-z