Department of Chemistry and Biochemistry, University of California, Los Angeles, CA 90095, USA.
UCLA-DOE Institute for Genomics and Proteomics, Los Angeles, CA 90095, USA.
Structure. 2024 Jun 6;32(6):751-765.e11. doi: 10.1016/j.str.2024.02.017. Epub 2024 Mar 20.
Designed protein cages and related materials provide unique opportunities for applications in biotechnology and medicine, but their creation remains challenging. Here, we apply computational approaches to design a suite of tetrahedrally symmetric, self-assembling protein cages. For the generation of docked conformations, we emphasize a protein fragment-based approach, while for sequence design of the de novo interface, a comparison of knowledge-based and machine learning protocols highlights the power and increased experimental success achieved using ProteinMPNN. An analysis of design outcomes provides insights for improving interface design protocols, including prioritizing fragment-based motifs, balancing interface hydrophobicity and polarity, and identifying preferred polar contact patterns. In all, we report five structures for seven protein cages, along with two structures of intermediate assemblies, with the highest resolution reaching 2.0 Å using cryo-EM. This set of designed cages adds substantially to the body of available protein nanoparticles, and to methodologies for their creation.
设计的蛋白质笼和相关材料为生物技术和医学中的应用提供了独特的机会，但它们的创造仍然具有挑战性。在这里，我们应用计算方法来设计一系列具有四面体对称性的自组装蛋白质笼。为了生成对接构象，我们强调基于蛋白质片段的方法，而对于从头开始的界面序列设计，基于知识和机器学习协议的比较突出了使用 ProteinMPNN 可实现的强大功能和增加的实验成功率。对设计结果的分析为改进接口设计协议提供了思路，包括优先考虑基于片段的基序、平衡界面疏水性和极性以及识别首选的极性接触模式。总之，我们报告了七个蛋白质笼的五个结构，以及两个中间组装体的结构，使用 cryo-EM 的最高分辨率达到 2.0 Å。这组设计的笼子大大增加了可用蛋白质纳米颗粒的数量，以及它们的创建方法。
