Wang Shunzhi, Favor Andrew, Kibler Ryan D, Lubner Joshua M, Borst Andrew J, Coudray Nicolas, Redler Rachel L, Chiang Huat Thart, Sheffler William, Hsia Yang, Bethel Neville P, Li Zhe, Ekiert Damian C, Bhabha Gira, Pozzo Lilo D, Baker David
Department of Biochemistry, University of Washington, Seattle, WA, USA.
Institute for Protein Design, University of Washington, Seattle, WA, USA.
Nat Mater. 2025 Jul 31. doi: 10.1038/s41563-025-02297-5.
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.
产生规则配位几何结构的定向相互作用是指导分子和胶体自组装的有力手段，但由于蛋白质形状复杂且界面性质错综复杂，要实现与蛋白质的这种高级相互作用仍然具有挑战性。在此，我们描述了一种受少量原子价态可产生丰富化学多样性启发的蛋白质纳米材料模块化设计方法。我们使用基于深度学习的生成工具设计蛋白质构建模块，纳入规则配位几何结构和可定制的键合相互作用，从而能够依据简单几何原理组装出多样的封闭和开放结构。实验表征证实成功形成了20多种多组分多面体蛋白质笼、二维阵列和三维蛋白质晶格，成功率高（10%-50%），且电子显微镜数据与相应设计模型高度匹配。由于模块化，单个构建模块可与不同伙伴组装以生成不同的规则组件，从而实现部件精简，并为设计纳米材料构建可重构网络。
