闻乐 发自 凹非寺 量子位 | 公众号 QbitAI 借助AI， 新型蛋白质 合成周期大幅降低！ 这些蛋白质还能有效抵抗像大肠杆菌这类抗生素耐药细菌。 一项来自澳大利亚的研究发现，像大肠杆菌这类致病菌会通过 ChuA蛋白 （细菌中的一种外膜蛋白） 从宿主血红蛋白中"偷"血红素获取生长 所需的 铁 。 研究人员借助AI工具，成功设计出能与ChuA结合的蛋白质，这些蛋白质就像"门卫"一样，阻止ChuA与血红蛋白接触，从而抑制细菌生长。 并且，部分AI设计的蛋白质在 低纳摩尔浓度下 就能发挥作用。 该研究现已发表于 Nature Communications 。 这项研究由Gavin Knott教授和Rhys Grinter博士共同领衔，他们研发出的AI蛋白质设计平台是澳大利亚首个模拟 诺奖得主David Baker 工 作的平台，采用 端到端 的方式创建了多种蛋白质。 并且，平台使用的是全球科学家均可 免费 使用的AI驱动蛋白质设计工具，能够让更多科研人员能参与其中，推动该领域发展。 研究原理：用算法打造抗菌 "分子锁" 对于包括大肠杆菌和志贺氏菌在内的大多数细菌， 铁是其生长和导致感染所必需的关键营养物质 。 ...

#SynBio团队 | 以色列魏茨曼科学研究院 【SynBioCon】 获 悉 ， 以色列魏茨曼科学研究院科学家在新一期《自然》杂志发表文章称：他们利用基于酶工 作原理的计算机新算法设计出高效人工合成酶。这种新型酶不仅能催化天然蛋白质无法完成的化学反应， 其效率 更达到人工智能（AI）设计酶的100倍 ，标志着"按需定制"高效酶的新阶段即将来临。 传统计算机算法辅助设计的酶往往效率低下，需要耗费大量时间进行实验室优化。为突破这一瓶颈，研究团队独 辟蹊径，选择"肯普消除"（一种涉及从特定底物碳原子上移除质子的非天然化学反应）作为验证案例。 过去十年，AI蛋白质设计大行其道，但其工作机制主要模仿现有酶。相比之下，新算法的工作机制是基于物理原 理来构建酶。研究团队表示，AI在处理某些蛋白质设计时确实无可替代，但对于复杂催化反应仍力不从心。未来 需要将两种方法优势互补，才能设计出更完美的酶。 通过算法设计的一种酶（白色）将底物（红色、黄色和蓝色）包裹于其活性位点内。图片来源：《自然》网站 ▌ 参考信息： 本文部分 素 材 来 自全网 。由 作 者重新 编写，系作者个人观点，本平台发布仅为了传达一种不同观点，不代表对该 ...

Group 1 - The article discusses the first real theater-based neuroaesthetic experiment in China, conducted at Tsinghua University, where eight volunteers wore portable EEG devices while watching a dance performance to capture their neural activity in real-time [3][6]. - The NeuroHUB platform, developed by Boruikang, is highlighted for its ability to achieve millisecond-level synchronization of EEG and physiological signals in a real-world setting, marking a significant advancement in neuroaesthetic research [5][6]. - The experiment demonstrated that audience engagement significantly increases brain activity, revealing the neural connections between emotional responses and artistic experiences [6][7]. Group 2 - NeuroHUB showcases three core advantages: wireless freedom allowing natural seating in the theater, group super-scanning enabling real-time dialogue among multiple brains, and robust interference resistance against complex electromagnetic environments [7][9]. - The platform's modular design and wireless data transmission ensure a seamless experience for participants, maintaining the purity of the artistic experience during the performance [7]. - NeuroHUB's capability to synchronize data from over ten participants simultaneously represents a breakthrough in overcoming the limitations of traditional laboratory settings [8]. Group 3 - The article also mentions advancements in medical technology, including a new integrated solution for coronary function and imaging assessments approved for market release, which combines multiple evaluation metrics for enhanced surgical decision-making [11][12]. - The development of a fully magnetic levitation artificial heart by Xinxin Medical has been recognized as one of the top ten technological advancements in Jiangsu Province, showcasing significant progress in heart failure treatment [13][15]. - The iPSC-derived CAR-NK cell therapy developed by Qihan Biotech has achieved notable clinical results in treating refractory systemic sclerosis, marking a significant milestone in autoimmune disease treatment [16][17]. Group 4 - The article highlights the introduction of two first-in-class drugs by Dige Pharmaceutical, which will be presented at major international conferences, indicating ongoing innovation in the hematology sector [21]. - The J-VALVE TF system's 12-month clinical follow-up results demonstrate superior performance compared to similar products, emphasizing the potential of Chinese-developed medical devices on the international stage [22][24]. - The article concludes with advancements in AI-driven enzyme design and biodegradable medical devices, showcasing the ongoing evolution and innovation within the healthcare and biotechnology industries [25][35].

Core Insights - The recent breakthrough in AI enzyme design by MoleculeMind and Hong Kong Polytechnic University has been recognized at the ICML 2025 conference, marking a significant advancement in the field of AI enzyme design [1] - The traditional methods of enzyme discovery and optimization are time-consuming and costly, with a success rate of less than 1%, highlighting the urgent need for innovative solutions in the biomanufacturing sector [2] - The introduction of the SENZ method, which utilizes substrate structure similarity for enzyme design, represents a novel approach that could revolutionize enzyme generation [3][5] Industry Overview - Enzymes are crucial for the development of the trillion-dollar bio-economy, impacting sectors such as biomedicine, green chemistry, and environmental degradation [1] - The lack of ideal biocatalysts is a major barrier to scaling production in the biomanufacturing industry, leading to annual capacity losses exceeding $100 billion in pharmaceuticals, chemicals, and agriculture [2] - AI protein design has emerged as a promising solution to generate precise catalysts by learning from existing enzyme structure-function relationships, although it faces challenges with novel synthetic molecules due to limited training data [2] Company Developments - MoleculeMind has developed the SENZ method, which integrates biological data retrieval and generative AI to create enzymes without direct catalytic data, thus addressing a critical challenge in enzyme generation [3][5] - The SENZ method has demonstrated superior performance compared to traditional enzyme design methods, potentially providing tailored solutions for complex drug synthesis and environmental remediation [6] - MoleculeMind is expanding its capabilities in "on-demand design" across various fields, including antibodies, vaccines, and industrial enzymes, aiming to provide innovative biological solutions for health, environmental, and sustainability challenges [7]