ArticleCell systems2025
Assessing generative model coverage of protein structures with SHAPES.
Article in Cell systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Efficient generation of epitope-targeted antibodies with Germinal.Nature biotechnology · 2026Article
- Zero-shot design of abioRxiv : the preprint server for biology · 2026Article
- Steering generative models for protein design: Aligning and conditioning strategies.Current opinion in structural biology · 2026Review
- One-site polarity switch enhances catalytic efficiency and stability of D-allulose 3-epimerase via flexibility-rigidity rebalancing.Scientific reports · 2026Article
- Protein FID: improved evaluation of protein structure generative models.Bioinformatics (Oxford, England) · 2026Article
- Ensemble-conditioned protein sequence design with Caliby.bioRxiv : the preprint server for biology · 2025Article
- SLAE: Strictly Local All-atom Environment for Protein Representation.bioRxiv : the preprint server for biology · 2025Article
- Efficient generation of epitope-targetedbioRxiv : the preprint server for biology · 2025Article
- Conditional Protein Structure Generation with Protpardelle-1c.bioRxiv : the preprint server for biology · 2025Article
- Article
- An improved model for prediction of de novo designed proteins with diverse geometries.bioRxiv : the preprint server for biology · 2025Article
- ProT-GFDM: A generative fractional diffusion model for protein generation.Computational and structural biotechnology journal · 2025Article
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Abstract
Recent advances in generative modeling enable efficient sampling of protein structures, but their tendency to optimize for designability imposes a bias toward idealized structures at the expense of loops and other complex structural motifs that are critical for function. We introduce SHAPES (structural and hierarchical assessment of proteins with embedding similarity) to evaluate five state-of-the-art generative models of protein structures. Using structural embeddings across multiple structural hierarchies, ranging from local geometries to global protein architectures, we reveal substantial undersampling of the observed protein structure space by these models. We use Fréchet protein distance (FPD) to quantify distributional coverage. Different models are distinct in their coverage behavior across different sampling noise scales and temperatures. The frequency of tertiary motifs (TERMs) further supports the observations. More robust sequence design and structure prediction methods are likely crucial in guiding the development of models with improved coverage of the designable protein space. A record of this paper's transparent peer review process is included in the supplemental information.
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