Core Viewpoint - Watson Bio (300142.SZ) has announced the establishment of the Yunnan Chuangwo Biological Industry Investment Fund in collaboration with several partners, aiming for a target size of 1 billion yuan, with the company contributing 450 million yuan as a limited partner [1] Group 1: Investment Fund Details - The fund's target size is set at 1 billion yuan [1] - Watson Bio will invest 450 million yuan of its own funds as a limited partner [1] - The fund will focus on various investment areas including synthetic biology, life and health, biological agriculture, bioenergy, biomaterials, technology platform enterprises, and high-throughput equipment [1] Group 2: Investment Areas - Investment areas include synthetic biology such as enzyme preparations, amino acids, and probiotics [1] - Life and health investments will cover plant extracts, recombinant collagen, sweeteners, and protein drugs [1] - Biological agriculture will focus on feed protein, micro-ecological preparations, animal vaccines, and biological nitrogen fixation [1] - Bioenergy investments will include bioethanol and biodiesel [1] - Biomaterials will focus on PLA, PHA, and PDO [1] - The fund will also target technology platform enterprises like strain development and AI protein design [1] - High-throughput equipment investments will include microfluidic devices and gene sequencing equipment [1]

Core Insights - The article highlights a significant breakthrough in protein design using conditional RFdiffusion to create high-affinity binding proteins for hydrophilic targets, led by Nobel laureate David Baker [4][7]. Design Strategy - The design strategy involves generating extended beta-sheet structures that geometrically match the edges of the target protein's beta strands through conditional RFdiffusion [5]. - Specially designed hydrogen bond groups are created to complement the polar groups on the target protein [6]. Experimental Validation - This technology overcomes traditional limitations in computational protein design, significantly expanding the range of target proteins for designed binding agents, particularly addressing challenges related to hydrophilic interactions. This advancement holds substantial value for drug development and protein function research [7]. - The designed protein binding agents exhibit high specificity and affinity, achieving picomolar to nanomolar levels of binding affinity for important protein targets such as KIT and PDGFRα [9]. Training and Courses - A series of online courses are offered, including AI protein design, antimicrobial peptide design, and computer-aided drug design, aimed at equipping participants with cutting-edge knowledge and practical skills in protein design [8]. - Various promotional offers are available for course registrations, including discounts for early sign-ups and bundled course registrations [8]. Future Trends - The article emphasizes the importance of AI protein design as a key technology to watch in 2026, with a growing demand for training and resources in this field, as evidenced by the high attendance and positive feedback from previous training sessions [7].

Core Insights - Levinthal Biotech, a leading company in AI protein design, successfully completed a multi-million RMB Pre-A financing round on December 9 [1] - The financing round was led by Jinyumaowu, with Junke Danmu participating, and Zhoudu Capital serving as the exclusive financial advisor [1] - The funds will primarily be used to accelerate the technical iteration of its all-atom model protein design platform, Pallatom, expand its commercial product pipeline, and attract global talent [1] - Pallatom has uniquely addressed the challenge of designing mixed cyclic peptides, paving the way for the development of a new generation of cyclic peptide drugs [1]

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 Insights - AlphaFold2, developed by Google DeepMind, has revolutionized scientific research by enabling accurate predictions of protein structures based solely on amino acid sequences since its launch in November 2020 [1][4][7]. Group 1: Impact on Scientific Research - Over the past five years, AlphaFold2 has assisted researchers worldwide in predicting millions of protein structures, marking a second renaissance in structural biology [7]. - The tool has significantly accelerated discovery processes, with researchers like Andrea Pauli stating that every project now utilizes AlphaFold [12]. - The Nature paper describing AlphaFold2 has garnered nearly 40,000 citations, indicating sustained interest from the scientific community [12]. Group 2: Applications and Discoveries - AlphaFold-Multimer, an extension of AlphaFold2, has enabled the discovery of three critical proteins involved in fertilization, challenging previous assumptions about the simplicity of sperm-egg interactions [8][10]. - The TMEM81-IZUMO1-SPACA6 protein complex plays a vital role in mediating sperm-egg binding, highlighting the complexity of fertilization mechanisms [10]. Group 3: User Engagement and Accessibility - AlphaFold has been accessed by approximately 3.3 million users across over 190 countries, with more than 1 million users from low- and middle-income countries, showcasing its global reach and accessibility [15]. - The AlphaFold database (AFDB) contains over 240 million predicted protein structures, covering nearly all known proteins on Earth [15]. Group 4: Influence on Structural Biology and Computational Biology - Researchers using AlphaFold have submitted about 50% more protein structures to the Protein Data Bank (PDB) compared to those who did not use the tool [18]. - AlphaFold has opened new research directions in computational biology, including AI-assisted drug discovery and protein design, leading to increased funding and interest in these areas [21]. Group 5: Future Prospects - AlphaFold2 is expected to aid in understanding disease mechanisms and potentially lead to new therapies, with AlphaFold3 anticipated to enhance drug discovery capabilities [24].

Core Viewpoint - The article discusses a groundbreaking research on AI-designed insulin receptor agonists that offer improved efficacy and safety for diabetes treatment compared to traditional insulin therapies [3][10]. Group 1: Research Background - Insulin has been a cornerstone in diabetes treatment since its discovery, but it has limitations such as complex production, strict storage conditions, and potential cancer risks [2]. - The insulin receptor acts as a "lock" that insulin "unlocks," initiating two main signaling pathways: one for metabolic regulation (AKT pathway) and another for cell growth (MAPK pathway) [2]. Group 2: Research Findings - Researchers from Washington University and Texas Southwestern Medical Center developed AI-designed insulin receptor agonists that outperform traditional insulin in lowering blood sugar and can precisely regulate signaling pathways, avoiding cancer cell proliferation [3][5]. - The new insulin receptor agonists exhibit remarkable properties, including enhanced thermal stability, remaining stable at 95°C, and precise signal regulation through adjustable linkers [7]. Group 3: Experimental Results - In mouse models, the AI-designed agonist RF-409 demonstrated superior blood sugar-lowering effects, requiring only half the dosage of insulin for the same effect and maintaining low blood sugar levels for up to 6 hours [7][8]. - These agonists can activate mutated insulin receptors in insulin-resistant patients, providing new treatment hope for rare genetic diabetes [8]. Group 4: Implications and Future Directions - The specificity of these agonists allows them to activate normal and mutated insulin receptors while avoiding activation of cancer cell receptors, significantly reducing potential cancer risks associated with traditional insulin therapy [8][10]. - The research lays the groundwork for developing safer and more effective next-generation diabetes treatments, with the founding of Lila Biologics aimed at utilizing AI protein design for breakthrough therapies [10][11].

Core Viewpoint - David Baker's team has developed a groundbreaking AI protein design model, RFdiffusion, which allows for the precise control of protein-protein interactions, potentially revolutionizing fields such as cancer treatment and immune regulation [2][3]. Group 1: Research Breakthroughs - The new design method enables precise timing of cytokine signaling, allowing for "remote control" of protein interactions with second-level accuracy [3]. - The research focuses on designing the "excited state" of proteins, which influences the kinetics of protein-protein interactions, rather than just their stable states [7]. - A special "hinge protein" was designed to change conformation in response to external signaling molecules, facilitating rapid dissociation of protein complexes [10]. Group 2: Performance Metrics - The new design method achieves up to a 5700-fold increase in dissociation rates, allowing protein complexes that previously took hours to dissociate to do so in seconds [12]. - Structural analysis confirmed that the designed proteins closely matched theoretical predictions, with a maximum deviation of only 1.3Å [12]. Group 3: Applications - The technology has potential applications in developing rapid biosensors, such as a SARS-CoV-2 sensor with a response time of just 30 seconds, which is 70 times faster than previous sensors [14]. - It can create dynamic control circuits at the protein level, enabling efficient signal transmission and amplification [15]. - The method allows for the rapid shutdown of highly active splitting enzyme systems, providing new tools for metabolic engineering [16]. Group 4: Immunology Insights - The research has significant implications for controlling the interleukin-2 (IL-2) signaling pathway, which is crucial for immune response, allowing for rapid on/off switching of IL-2 analogs [18]. - Different durations of IL-2 stimulation were found to have distinct biological effects, with short stimulation providing anti-apoptotic protection while prolonged stimulation activated metabolic changes and cell division [19][20]. Group 5: Paradigm Shift in Protein Design - This research represents a paradigm shift in protein design, moving from static structure design to dynamic kinetic control, with broad applicability across various protein interactions [22]. - The technology not only serves as a powerful tool for basic biological research but also opens new avenues for therapeutic applications, potentially leading to more precise and controllable biotherapies [22].

Company Overview - Wuxi Tushen Zhihuo Artificial Intelligence Technology Co., Ltd. (Tushen Zhihuo) completed a multi-million RMB angel round financing, with participation from Shanghai Angel Association and continued investment from existing shareholder Chengmei Capital [1][3] - Founded in December 2023, Tushen Zhihuo focuses on AI-driven protein design in the biotechnology sector, leveraging advanced AI technology for high-value protein and related product development [2][3] - The company is led by Wang Yuguang, an associate professor at Shanghai Jiao Tong University, with a strong background in AI, applied mathematics, and synthetic biology [2][3] Technology and Innovation - Tushen Zhihuo has developed a scientific intelligence platform for the biopharmaceutical field, enabling automated antibody design and protein expression testing [2][3] - The company has achieved significant advancements in industrial applications, such as improving the affinity of rabbit monoclonal antibodies beyond that of leading European pharmaceutical companies and enhancing enzyme activity by 380% and 800% for specific enzymes [3] Funding and Investment - Tushen Zhihuo has completed four rounds of financing within two years, indicating strong investor confidence and interest in the AI protein design sector [3][5] - Chengmei Capital has been a consistent investor, recognizing the potential of AI technology across various industries and the team's solid technical background [3][5] Industry Landscape - The global protein design market is rapidly evolving, with a focus on the application of AI in drug development, industrial enzyme catalysis, and biomanufacturing [4][5] - Approximately 30 AI protein-related companies are based in China, while 25 are located overseas, with most technologies originating from academic institutions [5][6] - Chinese companies emphasize practical applications and industrial efficiency in AI protein design, contrasting with overseas firms that focus on foundational breakthroughs [6] Challenges and Future Outlook - The AI protein design sector faces challenges such as data quality, model interpretability, and high costs of wet lab validation, which are critical for transitioning from research to commercial applications [6]

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].

Core Insights - The article discusses a groundbreaking study that utilizes AI to design proteins that can effectively inhibit the growth of antibiotic-resistant bacteria like E. coli by blocking their access to essential nutrients [1][29]. Group 1: AI-Driven Protein Design - Researchers have successfully designed proteins that bind to the ChuA protein in E. coli, acting as "gatekeepers" to prevent the bacteria from extracting heme from hemoglobin [3][22]. - The AI-driven protein design platform is the first of its kind in Australia, modeled after Nobel laureate David Baker's work, and is accessible for free to global scientists [7][8]. - The study highlights a rapid design process using AI algorithms, completing tasks that traditionally took months or years in a significantly shorter timeframe [19][24]. Group 2: Mechanism of Action - The study reveals that iron is a critical nutrient for the growth of bacteria, and during infections, the host employs a mechanism called "nutritional immunity" to sequester free iron [10][11]. - E. coli and Shigella utilize the ChuA protein to "steal" heme from hemoglobin, which is essential for their growth [12][15]. - The designed proteins specifically inhibit the interaction between ChuA and hemoglobin, effectively starving the bacteria of the necessary nutrients [22][28]. Group 3: Efficacy and Specificity - Among the AI-designed proteins, one inhibitor (G7) demonstrated an IC50 value of 42.5 nM, indicating its potency comparable to traditional antibiotics [21]. - The designed proteins exhibit exceptional specificity, only inhibiting the extraction of heme from hemoglobin without affecting the transport of free heme [25][26]. - This innovative approach represents a new paradigm in antibacterial strategies, focusing on nutrient deprivation rather than direct bacterial killing [27][28]. Group 4: Broader Implications - The advancements in AI-driven protein synthesis are expected to reshape the drug development landscape, enabling customized treatment solutions [37]. - The study emphasizes the potential of AI to revolutionize the field of antibiotic research by significantly reducing the risk of antibiotic resistance through novel mechanisms [29][31].