ArticleProceedings of the National Academy of Sciences of the United States of America2024
Rapid and automated design of two-component protein nanomaterials using ProteinMPNN.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- A Generative Neuro-Symbolic AI for Protein Sequence Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Beyond native sequence recovery: Improved modeling of the sequence-energy landscape of protein structures.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- New Frontiers in AI-Nano Converged Platforms for Intelligent Diagnostics, Therapeutics, and Safety Evaluation.Chem & bio engineering · 2026Review
- Application and Research Progress of Self-Assembling Protein Nanoparticles in Vaccine Development.International journal of molecular sciences · 2026Review
- Engineering complexity into protein-based biomaterials for biomedical applications.Nature reviews. Materials · 2026Article
- Self-assembling protein cages: from coiled-coil module to machine learning-drivenMaterials advances · 2026Review
- Beyond native sequence recovery: Improved modeling of the sequence-energy landscape of protein structures.bioRxiv : the preprint server for biology · 2026Article
- Protein engineering: status report.Protein engineering, design & selection : PEDS · 2026Review
- De novo design of protein nanoparticles with integrated functional motifs.bioRxiv : the preprint server for biology · 2026Article
- From sequence to scaffold: Computational design of protein nanoparticle vaccines from AlphaFold2-predicted building blocks.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- AI-Driven De Novo Design of Ultra Long-Acting GLP-1 Receptor Agonists.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Computational design of bifaceted protein nanomaterials.Nature materials · 2025Article
- Self-supervised machine learning methods for protein design improve sampling but not the identification of high-fitness variants.Science advances · 2025Article
- Article
- Leveraging large language models for peptide antibiotic design.Cell reports. Physical science · 2025Article
- Nanocarriers for intracellular delivery of proteins in biomedical applications: strategies and recent advances.Journal of nanobiotechnology · 2024Review
- Assessing the functional roles of coevolving PHD finger residues.Protein science : a publication of the Protein Society · 2024Article
- A suite of designed protein cages using machine learning and protein fragment-based protocols.Structure (London, England : 1993) · 2024Article
- Reengineering of a flavin-binding fluorescent protein using ProteinMPNN.Protein science : a publication of the Protein Society · 2024Article
- Design of a symmetry-broken tetrahedral protein cage by a method of internal steric occlusion.Protein science : a publication of the Protein Society · 2024Article
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Abstract
The design of protein-protein interfaces using physics-based design methods such as Rosetta requires substantial computational resources and manual refinement by expert structural biologists. Deep learning methods promise to simplify protein-protein interface design and enable its application to a wide variety of problems by researchers from various scientific disciplines. Here, we test the ability of a deep learning method for protein sequence design, ProteinMPNN, to design two-component tetrahedral protein nanomaterials and benchmark its performance against Rosetta. ProteinMPNN had a similar success rate to Rosetta, yielding 13 new experimentally confirmed assemblies, but required orders of magnitude less computation and no manual refinement. The interfaces designed by ProteinMPNN were substantially more polar than those designed by Rosetta, which facilitated in vitro assembly of the designed nanomaterials from independently purified components. Crystal structures of several of the assemblies confirmed the accuracy of the design method at high resolution. Our results showcase the potential of deep learning-based methods to unlock the widespread application of designed protein-protein interfaces and self-assembling protein nanomaterials in biotechnology.
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