Since the first revelation of proteins functioning as macromolecular machines through their three dimensional structures, researchers have been intrigued by the marvelous ways the biochemical processes are carried out by proteins. The aspiration to understand protein structures has fueled extensive efforts across different scientific disciplines. In recent years, it has been demonstrated that proteins with new functionality or shapes can be designed via structure-based modeling methods, and the design strategies have combined all available information — but largely piece-by-piece — from sequence derived statistics to the detailed atomic-level modeling of chemical interactions. Despite the significant progress, incorporating data-derived approaches through the use of deep learning methods can be a game changer. In this review, we summarize current progress, compare the arc of developing the deep learning approaches with the conventional methods, and describe the motivation and concepts behind current strategies that may lead to potential future opportunities.

自从首次揭示蛋白质通过其三维结构发挥大分子机器的功能以来，研究人员一直对蛋白质执行生化过程的奇妙方式很感兴趣。了解蛋白质结构的愿望推动了不同科学学科的广泛努力。近年来，已经证明可以通过基于结构的建模方法设计具有新功能或形状的蛋白质，并且设计策略结合了所有可用信息——但主要是逐条——从序列衍生统计数据到详细信息化学相互作用的原子级建模。尽管取得了重大进展，但通过使用深度学习方法整合数据衍生方法可能会改变游戏规则。在这篇评论中，