Prompteina：用自然语言生成蛋白质结构的全原子模型
Designing Protein Structures from Natural Language
把文生图那套搬到了蛋白质设计上，用文字描述想要的蛋白结构就能生成，还做出了针对 GM2A 的实验验证结合剂。
Prompteina 是首个支持自然语言指定设计目标的全原子蛋白质结构生成模型，思路类似文本生成图像。训练所用的 Prompteina-Data 包含 180 万个结构和 5000 万条以上文本描述。模型在 18 项结构属性中的 15 项上超过基线，结合剂设计在 20 个靶点中至少 13 个领先，界面置信度比最强基线高 14%。实验验证中，团队针对此前没有全新结合剂的 GM2A 靶点找到 9 个结合剂，最强的表观 K_D 为 107 nM。
Designing Protein Structures from Natural Language
A central challenge in de novo protein design is generating proteins with desired structural and functional properties. Existing design models typically require a dedicated input representation for each property, limiting the design objectives they support. We develop Prompteina, the first all-atom structure generation model that supports new and broader design objectives specified in natural language, analogous to text-to-image generation. To train Prompteina, we construct Prompteina-Data, a paired text--protein structure dataset of 1.8M structures and 50M+ captions describing global and residue-specific structural and functional properties. We adapt Proteina-Complexa, a pretrained structure generation model, through text-conditioned pretraining and finetuning that aligns residues with their corresponding text. Prompteina outperforms baselines on 15 of 18 structural properties and generates proteins that more closely match descriptions of their intended functions. In binder design, it leads baselines on at least 13 of 20 targets and improves interface confidence by 14\% over the strongest baseline. With iterative in silico feedback, Prompteina's gain over non-iterative generation is 57\% larger than the next-best baseline on the hardest targets. Finally, we experimentally validate Prompteina designs against GM2A, a target with no reported de novo binders. We identify nine binders, with the strongest at an apparent $K_{\mathrm{D}}$ of 107 nM. These results establish natural language as an effective and powerful conditioning interface for protein structure generation.
