ArticleNature methods2024
De novo protein design with a denoising diffusion network independent of pretrained structure prediction models.
Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- What will be the future of computational biology for macromolecules in the era of AI?PLoS computational biology · 2026Article
- Integrating Single-Cell Profiling with Generative AI forMolecules (Basel, Switzerland) · 2026Article
- Generalist biological artificial intelligence in modeling the language of life.Nature biotechnology · 2026Review
- Protein design and RNA design: Perspectives.Quantitative biology (Beijing, China) · 2026Article
- A Novel Dual-Target Compound Designed With Potent Herbicidal and Fungicidal Activity Inspired by Conserved Phytoene Synthase Domains.Plant biotechnology journal · 2026Article
- Review
- BoltzGen: Toward Universal Binder Design.bioRxiv : the preprint server for biology · 2025Article
- Enhancing functional proteins through multimodal inverse folding with ABACUS-T.Nature communications · 2025Article
- Designing flexible protein structures and sampling protein conformations with a unified model using vector quantization and diffusion.National science review · 2025Article
- Article
- GeoEvoBuilder: A deep learning framework for efficient functional and thermostable protein design.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Efficient protein structure generation with sparse denoising models.Nature machine intelligence · 2025Article
- The Role of AI-Driven De Novo Protein Design in the Exploration of the Protein Functional Universe.Biology · 2025Review
- Modification and applications of glucose oxidase: optimization strategies and high-throughput screening technologies.World journal of microbiology & biotechnology · 2025Review
- ProDualNet: dual-target protein sequence design method based on protein language model and structure model.Briefings in bioinformatics · 2025Article
- FoldMark: Safeguarding Protein Structure Generative Models with Distributional and Evolutionary Watermarking.bioRxiv : the preprint server for biology · 2025Article
- Machine learning in molecular biophysics: Protein allostery, multi-level free energy simulations, and lipid phase transitions.Biophysics reviews · 2025Review
- De Novo Design of Large Polypeptides Using a Lightweight Diffusion Model Integrating LSTM and Attention Mechanism Under Per-Residue Secondary Structure Constraints.Molecules (Basel, Switzerland) · 2025Article
- Characterization of PRDM9 Multifunctionality in Yak Testes Through Protein Interaction Mapping.International journal of molecular sciences · 2025Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
The recent success of RFdiffusion, a method for protein structure design with a denoising diffusion probabilistic model, has relied on fine-tuning the RoseTTAFold structure prediction network for protein backbone denoising. Here, we introduce SCUBA-diffusion (SCUBA-D), a protein backbone denoising diffusion probabilistic model freshly trained by considering co-diffusion of sequence representation to enhance model regularization and adversarial losses to minimize data-out-of-distribution errors. While matching the performance of the pretrained RoseTTAFold-based RFdiffusion in generating experimentally realizable protein structures, SCUBA-D readily generates protein structures with not-yet-observed overall folds that are different from those predictable with RoseTTAFold. The accuracy of SCUBA-D was confirmed by the X-ray structures of 16 designed proteins and a protein complex, and by experiments validating designed heme-binding proteins and Ras-binding proteins. Our work shows that deep generative models of images or texts can be fruitfully extended to complex physical objects like protein structures by addressing outstanding issues such as the data-out-of-distribution errors.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.