ArticleNature materials2025
Bond-centric modular design of protein assemblies.
Article in Nature materials, 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
12 citing papers in PubMed.
- Immunofunctions of Protein and Carbohydrate Assemblies Modulated by Structures Acrossing Scales as Biomaterials.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Article
- Modeling Reveals How Direct-Acting Antivirals Redirect HBV Capsid Assembly Pathways to Noninfectious Products.bioRxiv : the preprint server for biology · 2026Article
- Review
- Reverse-engineering amyloid strains with generative protein design.bioRxiv : the preprint server for biology · 2026Article
- Protein foundation models: a comprehensive survey.Science China. Life sciences · 2026Review
- Generative Protein Design: From Deep Learning Algorithms to Translational Applications.International journal of molecular sciences · 2026Review
- Review
- 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
- The dynamic frontier of artificial intelligence.Nature materials · 2026Article
- De novo design of protein nanoparticles with integrated functional motifs.bioRxiv : the preprint server for biology · 2026Article
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16 authors.
Funding
Abstract
Directional interactions that generate regular coordination geometries are a powerful means of guiding molecular and colloidal self-assembly, but implementing such high-level interactions with proteins remains challenging due to their complex shapes and intricate interface properties. Here we describe a modular approach to protein nanomaterial design inspired by the rich chemical diversity that can be generated from the small number of atomic valencies. We design protein building blocks using deep learning-based generative tools, incorporating regular coordination geometries and tailorable bonding interactions that enable the assembly of diverse closed and open architectures guided by simple geometric principles. Experimental characterization confirms the successful formation of more than 20 multicomponent polyhedral protein cages, two-dimensional arrays and three-dimensional protein lattices, with a high (10%-50%) success rate and electron microscopy data closely matching the corresponding design models. Due to modularity, individual building blocks can assemble with different partners to generate distinct regular assemblies, resulting in an economy of parts and enabling the construction of reconfigurable networks for designer nanomaterials.
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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.