Transdisciplinary Research Area, "Building Blocks of Matter and Fundamental Interactions (TRA Matter)", University of Bonn, 53121, Bonn, Germany.
Life and Medical Sciences Institute, University of Bonn, 53115, Bonn, Germany.
Chembiochem. 2023 Aug 1;24(15):e202300117. doi: 10.1002/cbic.202300117. Epub 2023 Jul 12.
Self-assembling polyhedral protein biomaterials have gained attention as engineering targets owing to their naturally evolved sophisticated functions, ranging from protecting macromolecules from the environment to spatially controlling biochemical reactions. Precise computational design of de novo protein polyhedra is possible through two main types of approaches: methods from first principles, using physical and geometrical rules, and more recent data-driven methods based on artificial intelligence (AI), including deep learning (DL). Here, we retrospect first principle- and AI-based approaches for designing finite polyhedral protein assemblies, as well as advances in the structure prediction of such assemblies. We further highlight the possible applications of these materials and explore how the presented approaches can be combined to overcome current challenges and to advance the design of functional protein-based biomaterials.
自组装多面体蛋白生物材料因其自然进化出的复杂功能而备受关注，这些功能范围从保护大分子免受环境影响到空间控制生化反应。通过两种主要类型的方法可以实现从头设计新的蛋白质多面体：基于物理和几何规则的第一性原理方法，以及基于人工智能（AI）的最近的数据驱动方法，包括深度学习（DL）。在这里，我们回顾了基于第一性原理和基于 AI 的设计有限多面体蛋白组装的方法，以及这些组装结构预测的进展。我们进一步强调了这些材料的可能应用，并探讨了如何结合使用这些方法来克服当前的挑战，推进功能性蛋白基生物材料的设计。
