一致的合成序列解锁全原子脱氧蛋白设计中的结构多样性
Consistent Synthetic Sequences Unlock Structural Diversity in Fully Atomistic De Novo Protein Design
AI总结：
本研究通过创建对齐的序列-结构数据集，提升了全原子脱氧蛋白设计模型的结构多样性和共设计性。
摘要：
高质量的训练数据集对于开发有效的蛋白质设计模型至关重要，但现有的合成数据集往往包含不利的序列-结构对，损害生成模型的性能。我们利用ProteinMPNN，其序列在实验上是有利的，且易于折叠，结合结构预测模型，将高质量的合成结构与可恢复的合成序列对齐。通过这种方式，我们创建了一个专门设计用于训练表达性强、全原子蛋白质生成器的新型数据集。通过在该数据集上重新训练La-Proteina，该模型在连续潜在空间中建模离散残基类型和侧链结构，我们实现了新的最先进的结果，结构多样性提高了54%，共设计性提高了27%。为了验证我们方法的广泛实用性，我们进一步引入了Proteina Atomistica，一个统一的流基于框架，该框架在没有潜在变量的情况下联合学习蛋白质主链结构、离散序列和原子侧链的分布。我们再次发现，使用我们的新序列-结构数据训练可以显著提升基准性能，使方法的结构多样性提高73%，共设计性提高5%。我们的工作强调了对齐的序列-结构数据对于训练高性能脱氧蛋白设计模型的重要性。我们的新数据集https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/resources/proteina-atomistica_data/files?version=release，即一致性过滤的合成蛋白质数据库，作为开源资源提供。
英文摘要：
High-quality training datasets are crucial for the development of effective protein design models, but existing synthetic datasets often include unfavorable sequence-structure pairs, impairing generative model performance. We leverage ProteinMPNN, whose sequences are experimentally favorable as well as amenable to folding, together with structure prediction models to align high-quality synthetic structures with recoverable synthetic sequences. In that way, we create a new dataset designed specifically for training expressive, fully atomistic protein generators. By retraining La-Proteina, which models discrete residue type and side chain structure in a continuous latent space, on this dataset, we achieve new state-of-the-art results, with improvements of +54% in structural diversity and +27% in co-designability. To validate the broad utility of our approach, we further introduce Proteina Atomistica, a unified flow-based framework that jointly learns the distribution of protein backbone structure, discrete sequences, and atomistic side chains without latent variables. We again find that training on our new sequence-structure data dramatically boosts benchmark performance, improving \method's structural diversity by +73% and co-designability by +5%. Our work highlights the critical importance of aligned sequence-structure data for training high-performance de novo protein design models. Our new dataset https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/resources/proteina-atomistica_data/files?version=release , the Consistency Distilled Synthetic Protein Database, is made available as an open-source resource.
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