ArticleScience (New York, N.Y.)2024
Scalable protein design using optimization in a relaxed sequence space.
Article in Science (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- ModelCIF Update: Supporting Emerging Classes of Computational Macromolecular Models.Journal of molecular biology · 2026Article
- FoldDoF: Utilizing the Primary Degrees of Freedom of Protein Backbone for Geometric Modeling and Generation.Journal of chemical information and modeling · 2026Article
- Artificial intelligence in biologic drug discovery: A review of methodological evolution and therapeutic applications.Acta pharmaceutica Sinica. B · 2026Review
- OpenGerminal: an open-source implementation of the Germinal antibody design pipeline.bioRxiv : the preprint server for biology · 2026Article
- Efficient generation of epitope-targeted antibodies with Germinal.Nature biotechnology · 2026Article
- Zero-shot design of abioRxiv : the preprint server for biology · 2026Article
- Physics-guided design of intrinsically disordered proteins.bioRxiv : the preprint server for biology · 2026Article
- Protein design and RNA design: Perspectives.Quantitative biology (Beijing, China) · 2026Article
- Limitations of the refolding pipeline for de novo protein design.Protein science : a publication of the Protein Society · 2026Article
- I53-50: Engineered icosahedral protein cage for modular vaccine nanoplatform.Journal of microbiology (Seoul, Korea) · 2026Review
- Scaffold-Lab: Critical evaluation and ranking of protein backbone generation methods in a unified framework.PLoS computational biology · 2026Article
- Generative Protein Design: From Deep Learning Algorithms to Translational Applications.International journal of molecular sciences · 2026Review
- AlphaFold as a prior: experimental structure determination conditioned on a pretrained neural network.Nature methods · 2026Article
- Review
- De novo design of GPCR exoframe modulators.Nature · 2026Article
- Mapping targetable sites on the human surfaceome for the design of novel binders.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Evaluating zero-shot prediction of monomeric protein design success by AlphaFold, ESMFold, and ProteinMPNN.Protein science : a publication of the Protein Society · 2026Article
- ProteinDJ: A high-performance and modular protein design pipeline.Protein science : a publication of the Protein Society · 2026Article
- The Five-Decade Journey of Small Cell Lung Cancer.Cancer communications (London, England) · 2026Review
- Mass spectrometry integrates protein design into structural biology method development.QRB discovery · 2026Review
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Machine learning (ML)-based design approaches have advanced the field of de novo protein design, with diffusion-based generative methods increasingly dominating protein design pipelines. Here, we report a "hallucination"-based protein design approach that functions in relaxed sequence space, enabling the efficient design of high-quality protein backbones over multiple scales and with broad scope of application without the need for any form of retraining. We experimentally produced and characterized more than 100 proteins. Three high-resolution crystal structures and two cryo-electron microscopy density maps of designed single-chain proteins comprising up to 1000 amino acids validate the accuracy of the method. Our pipeline can also be used to design synthetic protein-protein interactions, as validated experimentally by a set of protein heterodimers. Relaxed sequence optimization offers attractive performance with respect to designability, scope of applicability for different design problems, and scalability across protein sizes.
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Registered trials
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.