Designing Protein Structures From Natural Language 1 Prompteina is introduced as the first text-conditioned all-atom protein structure generation model that directly generates both sequence and all-atom 3D structure from natural-language prompts, aiming to replace property-by-property engineered conditioning inputs with a single language interface. 2 A key enabling contribution is Prompteina-Data: ~1.8M protein-chain structure entries (from PDB, AlphaFold DB, and synthetic dimers) paired with 52.4M LLM-written captions describing global and residue-specific properties (average ~29 captions per structure), spanning up to 59 annotation types per entry. 3 The captions are not just high-level function blurbs: they include fine-grained, residue-linked constraints (e.g., disulfide bonds, catalytic sites, protein–protein contacts). The dataset also includes phrase-to-residue correspondences when possible, enabling supervised alignment between text spans and residue subsets. 4 Methodologically, Prompteina adapts a pretrained all-atom generator (Complexa) with two text-conditioning pathways: pooled text embeddings broadcast for global properties, and token-level cross-attention for residue-specific control. Both are injected into the denoiser to steer generation throughout sampling (rather than only at inference-time guidance). 5 A supervised text–residue alignment objective is added during fine-tuning: alignment annotations define a sparse residue-by-token matrix, and the model learns which residues correspond to which text spans, improving control over features like disulfide and salt-bridge counts while preserving global fold control. 6 On text-guided structural control across 18 tasks in five property groups (fold/topology, shape, secondary, tertiary, and combined constraints), Prompteina leads baselines on 15/18 tasks and supports the broadest range of properties through one interface; it also shows robustness to paraphrasing and maintains designability/novelty metrics relative to baselines. 7 For functional prompts across 122 protein families, evaluation with ProTrek (sequence/structure/text similarity) shows Prompteina achieves the highest mean similarity, including when weighting by predicted fold confidence (pLDDT), compared with sequence-only text-to-protein methods that must be folded post hoc. 8 In binder design, Prompteina can directly steer interface size via natural language: across 20 targets, requested vs realized interface residue counts track nearly linearly (Spearman ρ ~0.994–1.000), enabling explicit tuning of binder–target interface extent. 9 In agentic binder design (same LLM agent writing specs tailored to each model’s interface), Prompteina yields the highest interface-confidence metric (expected top-k ipSAEmin) across 20 targets and multiple sequence conditions (native, full ProteinMPNN redesign, and fixed-interface redesign). Ablations suggest the largest gains come from being able to specify interface properties (size/geometry/chemistry) in the prompt. 10 Prospective wet-lab validation targets GM2A (a disease-relevant protein with no previously reported de novo binders): from 89 expressed and SPR-assayed designs, nine were classified as binders, including a strongest apparent KD of 107 nM (with sub-micromolar binders spanning diverse architectures). 💻Code: github.com/uw-ssec/promptein… 📜Paper: arxiv.org/abs/2610.13917 #ProteinDesign #GenerativeModels #DiffusionModels #ProteinStructure #TextToStructure #ComputationalBiology #Bioinformatics #MachineLearning