编译丨王聪 编辑丨王多鱼 排版丨水成文 蛋白质设计 （ protein design ） 正在经历一场由 人工智能 （AI） 驱动的革命，彻底改变了我们为药物发现、生物技术和合成生物学应用而设计蛋白质的方式。 通过驾驭蛋白质序列空间的巨大复杂性，并克服结构和功能数据的局限性，AI 能够以前所未有的精准度和速度设计具有定制功能的新型蛋白质。 2025 年 9 月 8 日，哈佛大学医学院 乔治·丘奇 （George Church） 、 Li Li ， 格里菲斯大学 潘世瑞 等人在 Nature 旗下综述期刊 Nature Reviews Bioengineering 上发表了题为： AI-driven protein design 的综述论文。 该综述的核心是 提供一个全面且可操作的蛋白质设计路线图 ，逐步指导如何将最先进的 AI 工具整合到蛋白质设计工作流程中，包括结构与功能预测工具以及用 于从头设计的生成式模型。为了在实践中说明这一路线图，作者展示了 AI 驱动蛋白质设计的案例研究，涵盖从工程化治疗性蛋白质到设计解锁酶功能及重编程生 物分子系统的新型蛋白质。展望未来，该综述勾勒出未来的发展方向，强调了 AI ...

Core Viewpoint - The article discusses the breakthrough technology BindCraft, which allows for the one-shot design of functional protein binders with a success rate of 10%-100%, significantly improving the efficiency of protein design compared to traditional methods [2][3][5]. Summary by Sections BindCraft Technology - BindCraft is an open-source, automated platform for de novo design of protein binders, achieving high-affinity binders without the need for high-throughput screening or experimental optimization [3][5]. - The technology leverages AlphaFold2's weights to generate protein binders with nanomolar affinity, even in the absence of known binding sites [3][5]. Applications and Results - The research team successfully designed binders targeting challenging targets such as cell surface receptors, common allergens, de novo proteins, and multi-domain nucleases like CRISPR-Cas9 [3][7]. - Specific applications include: 1. Designing antibody drugs targeting therapeutic cell surface receptors like PD-1 and PD-L1, achieving nanomolar affinity without extensive design and screening [7]. 2. Blocking allergens, with a designed binder for birch pollen allergen Bet v1 showing a 50% reduction in IgE binding in patient serum tests [7][8]. 3. Regulating CRISPR gene editing by designing a new inhibitory protein that significantly reduces Cas9's gene editing activity in HEK293 cells [8]. 4. Neutralizing deadly bacterial toxins, with a designed protein completely eliminating cell death caused by the toxin from Clostridium perfringens [8]. 5. Modifying AAV for targeted gene delivery by integrating mini-binders that specifically target HER2 and PD-L1 expressing cancer cells [8]. Impact and Future Potential - BindCraft addresses long-standing success rate bottlenecks in protein design and offers direct solutions for allergy treatment, gene editing safety, toxin neutralization, and targeted gene therapy [9]. - The open-source nature of the technology allows ordinary laboratories to design custom proteins, potentially reshaping drug development, disease diagnosis, and biotechnology [9].

编辑丨王多鱼 排版丨水成文 光响应性蛋白质 在生命的所有领域中都发挥着至关重要的作用，它们能够感知并响应环境中的光信号。然 而，从头设计 （ de novo design） 具有精确结构和可逆响应行为的光响应性蛋白质，一直是一项未解决 的挑战。 2025 年 8 月 28 日，西湖大学生命科学学院 曹龙兴 团队在 Nature Chemistry 期刊 发表了题为 ： De novo design of light-responsive protein–protein interactions enables reversible formation of protein assemblies 的研究论文。 该研究开发了一套可适用于非天然氨基酸的蛋白质对接程序，通过整合光响应非天然氨基酸 AzoF 与蛋白 质 密码子扩展 技术， 从头设计出了一系列可逆的光响应性蛋白质。 2024 年， 蛋白质设计 领域迎来高光时刻——蛋白质设计先驱 David Baker 教授获得 诺贝尔化学奖 。随 着 人工智能 （AI） 技术的加持，蛋白质设计领域的发展可谓突飞猛进。 尽管该领域近年来取得了许多实质性进展，但仍面临一个重 ...

Core Viewpoint - The article discusses the development of a new framework, RFpeptides, for the de novo design of high-affinity macrocyclic peptides targeting proteins, utilizing advancements in deep learning and artificial intelligence [2][3][10]. Group 1: Background and Challenges - Traditional methods for peptide drug development rely on natural product discovery or high-throughput screening of random peptides, which are resource-intensive and limited in scope [6][8]. - The challenges in natural product discovery include difficulties in synthesis, poor stability, and low tolerance to mutations [6]. - High-throughput screening methods, while powerful, are time-consuming and costly, covering only a small fraction of the chemical diversity available in macrocyclic compounds [6][9]. Group 2: Innovations in Design Methodology - The RFpeptides framework allows for precise de novo design of macrocyclic peptides with high affinity for target proteins, addressing the limitations of previous methods [3][12]. - The research team expanded existing structural prediction networks and protein backbone generation frameworks to incorporate cyclic relative position encoding, enhancing the design process [12]. Group 3: Experimental Results - The team tested up to 20 designed macrocyclic peptides against four different proteins (MCL1, MDM2, GABARAP, and RbtA), achieving medium to high affinity binders for all targets [13]. - Notably, a high-affinity binder for RbtA was designed with a dissociation constant (K_d) of less than 10 nM based solely on predicted target structure [13]. - Structural analysis of the designed macrocyclic peptide complexes with MCL1, GABARAP, and RbtA showed high agreement with computational models, with Cα RMSD values less than 1.5 Å [14]. Group 4: Implications and Future Applications - The RFpeptides framework provides a systematic approach for the rapid custom design of macrocyclic peptides for diagnostic and therapeutic applications, indicating significant potential in the pharmaceutical industry [16].