设计能动态切换构象的蛋白质，提升多状态蛋白设计成功率。
Multi-state Protein Design with DynamicMPNN
- 联合学习多个构象，直接生成适配多状态的序列。
- 在多状态基准上，构象误差降低25%，序列恢复率提升12%。
- 适合研究动态蛋白功能或开发新型生物分子器件的研究者。
结构生物学长期遵循‘一序列、一结构、一功能’范式，但许多关键生物过程——如酶催化和膜运输——依赖于可采用多种构象状态的蛋白质。现有方法依赖对单状态预测结果的后处理聚合，实验成功率远低于单状态设计。我们提出DynamicMPNN，一种通过联合学习构象集合中多个状态而显式训练生成多构象兼容序列的逆折叠模型。该模型在涵盖75% CATH超家族的46,033个构象对上训练，并使用Alphafold 3进行评估。在挑战性的多状态蛋白基准测试中，DynamicMPNN在去噪均方根偏差（decoy-normalized RMSD）上比ProteinMPNN最高提升25%，在序列恢复率上提升12%。
原文摘要 · Abstract (English)
Structural biology has long been dominated by the one sequence, one structure, one function paradigm, yet many critical biological processes - from enzyme catalysis to membrane transport - depend on proteins that adopt multiple conformational states. Existing multi-state design approaches rely on post-hoc aggregation of single-state predictions, achieving poor experimental success rates compared to single-state design. We introduce DynamicMPNN, an inverse folding model explicitly trained to generate sequences compatible with multiple conformations through joint learning across conformational ensembles. Trained on 46,033 conformational pairs covering 75% of CATH superfamilies and evaluated using Alphafold 3, DynamicMPNN outperforms ProteinMPNN by up to 25% on decoy-normalized RMSD and by 12% on sequence recovery across our challenging multi-state protein benchmark.
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