撰文丨王聪 编辑丨王多鱼 酶的从头设计，旨在构建含有理想活性位点的蛋白质，这些位点周围的催化氨基酸残基能够稳定目标化学 反应的过渡态。此前已有研究利用蛋白质从头设计来生成新的 金属水解酶 （ Metallohydrolase ） ， 但这 些酶的活性和效率相对较低， 需要经过大量的定向进化才能达到天然酶的活性和效率水平。 排版丨水成文 David Baker 教授 David Baker 团队之前开发的用于蛋白质从头设计的生成式 AI 工具—— RFdiffusion，可以解决上述难 题，但其需要为每个催化氨基酸残基指定序列位置和主链坐标，这限制了 设计空间范围。 2025 年 12 月 3 日，诺奖得主、蛋白质设计先驱 David Baker 教授团队在国际顶尖学术期刊 Nature 上发 表了题为 ： Computational design of metallohydrolases 的研究论文。 该研究利用新一代 AI 蛋白质设计工具—— RFdiffusion2 ， 成功设计了活性极高的锌金属水解酶，其催化 效率比之前设计的金属水解酶高出上千倍 。更令人惊叹的是，这些高性能酶完全"从头开始"设计，且无 ...

Core Viewpoint - The article discusses the advancements in protein design using generative artificial intelligence, particularly focusing on the breakthrough of RFdiffusion3, which allows for atomic-level precision in designing proteins that can interact with specific small molecules, DNA, and other biomolecules [9][24]. Group 1: RFdiffusion3 Overview - RFdiffusion3 represents a significant advancement in protein design, enabling the design of proteins with atomic-level precision, including interactions with non-protein components [9][10]. - The model is built on previous versions, RFdiffusion and RFdiffusion2, and offers improvements in accuracy, efficiency, and versatility [10][28]. - RFdiffusion3 can handle complex atomic constraints, such as hydrogen bonds and solvent accessibility, and is capable of designing various interactions, including protein-protein, protein-small molecule, and protein-nucleic acid interactions [10][28]. Group 2: Performance and Applications - In benchmark tests, RFdiffusion3 demonstrated superior performance with a computational cost only one-tenth of previous methods, making it significantly more efficient [3][10]. - The model has shown excellent results in designing DNA-binding proteins and enzymes, achieving a binding activity of 5.89±2.15 μM for a designed DNA-binding protein and a Kcat/Km value of 3557 for a designed cysteine hydrolase [21][28]. - RFdiffusion3 has outperformed its predecessor in multiple target designs, producing an average of 8.2 unique successful clusters compared to 1.4 from RFdiffusion [15]. Group 3: Technical Innovations - The core innovation of RFdiffusion3 lies in its all-atom diffusion model, which allows for simultaneous simulation of protein backbone and side chains, as well as interactions with non-protein components [9][10]. - The model employs a unified representation of amino acids, standardizing them to 14 atoms, which facilitates the handling of varying side chain atom counts [13][14]. - The architecture is based on a Transformer U-Net, which includes downsampling, sparse transformer modules, and upsampling to predict coordinate updates [14]. Group 4: Future Implications - The introduction of RFdiffusion3 marks a paradigm shift in protein design, enabling unprecedented control over complex functionalities, such as specifying enzyme active sites and controlling hydrogen bond states [24][25]. - As the technology continues to evolve, it is expected to lead to innovative therapies, new types of proteases, and biomaterials, fulfilling the vision of "designing life molecules" [25].