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Prompteina, an all-atom protein structure generation model, leverages natural language prompts to specify diverse design objectives, overcoming limitations of property-specific input representations. Trained on a novel 1.8M structure dataset with 50M+ captions, it adapts a pretrained structure generator via text-conditioned pretraining and finetuning. Prompteina demonstrates superior performance across numerous structural and functional properties, achieving significant improvements in binder design and experimental validation with a strongest binder at 107 nM $K_{\mathrm{D}}$.
Natural language prompts now unlock de novo protein design, enabling generation of novel structures with specified functions and outperforming existing methods in binder design and experimental success.
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.