Information in proteins flows from sequence to structure to function, with each step causally driven by the preceding one. Protein design is founded on inverting this process: specify a desired function, design a structure executing this function, and find a sequence that folds into this structure. This ‘central dogma’ underlies nearly all de novo protein-design efforts. Our ability to accomplish these tasks depends on our understanding of protein folding and function and our ability to capture this understanding in computational methods. In recent years, deep learning-derived approaches for efficient and accurate structure modeling and enrichment of successful designs have enabled progression beyond the design of protein structures and towards the design of functional proteins. We examine these advances in the broader context of classical de novo protein design and consider implications for future challenges to come, including fundamental capabilities such as sequence and structure co-design and conformational control considering flexibility, and functional objectives such as antibody and enzyme design.

蛋白质中的信息从序列流动到结构再到功能，每一步都由前一步因果驱动。蛋白质设计建立在反转这个过程的基础上：指定所需的功能，设计执行该功能的结构，并找到折叠成该结构的序列。这个“中心法则”几乎是所有从头蛋白质设计工作的基础。我们完成这些任务的能力取决于我们对蛋白质折叠和功能的理解以及我们在计算方法中捕捉这种理解的能力。近年来，基于深度学习的高效、准确的结构建模方法和丰富的成功设计已经使蛋白质结构设计向功能蛋白质设计迈进。我们在经典从头蛋白质设计的更广泛背景下研究这些进展，并考虑对未来挑战的影响，包括序列和结构协同设计等基本能力以及考虑灵活性的构象控制，以及抗体和酶设计等功能目标。