Core Insights - The article highlights significant advancements in various fields of research published in the journal Cell, with a notable contribution from Chinese scholars, indicating a strong presence in cutting-edge scientific research [1]. Group 1: Measles Virus Research - A study by Zhang Heqiao and Roger Kornberg's team elucidated the structure of the measles virus polymerase complex and its interaction with non-nucleoside inhibitors, laying the groundwork for rational antiviral drug design [3][4]. Group 2: AI in Protein Engineering - The research team led by Gao Caixia developed a novel AI protein engineering simulation method called AiCE, which integrates structural and evolutionary constraints, enabling efficient protein evolution simulation and functional design without the need for specialized AI model training [7]. Group 3: Vertebrate Genomics - The team from Zhejiang University introduced a high-throughput, sensitive single-nucleus ATAC sequencing technology (UUATAC-seq) to create chromatin accessibility maps, and developed the Nvwa model for predicting cis-regulatory elements, revealing the conserved syntax of vertebrate regulatory sequences [10][11]. Group 4: Primate Brain Research - A study identified cell type-specific enhancers in the macaque brain, establishing tools for understanding primate brain structure and diseases, which could enhance insights into cognitive functions [15]. Group 5: Peripheral Nerve Imaging - Researchers from the University of Science and Technology of China pioneered a high-speed, subcellular resolution imaging technique for whole-mouse peripheral nerves, providing a detailed peripheral nerve atlas and new tools for studying nerve regulation and disease mechanisms [19]. Group 6: Primate Prefrontal Cortex Connectivity - A study reconstructed the whole-brain connectivity network of the macaque prefrontal cortex at the single-neuron level, revealing refined axon targeting and arborization, which is crucial for understanding complex cognitive functions in primates [23]. Group 7: Optogenetics in Drug Discovery - The research led by Felix Wong developed an optogenetics platform for discovering selective modulators of the integrated stress response, identifying compounds that enhance cell death without toxicity, and demonstrating antiviral activity in a herpes simplex virus mouse model [27][28]. Group 8: Engineering Yeast Behavior - A study from Imperial College London established engineering principles for yeast, enabling programmable multicellular behaviors, transforming yeast from a "single-cell factory" to a "multicellular system chassis" [33][34].

Core Viewpoint - The article discusses the development of a novel artificial intelligence-based protein engineering computational simulation method called AiCE, which integrates structural and evolutionary constraints to enhance protein evolution and function design without the need for specialized AI model training [4][12]. Group 1: Protein Engineering Overview - Protein engineering involves modifying amino acid sequences to alter protein structure and function, offering significant potential in both basic research and industrial applications, with market size expected to exceed hundreds of billions [2]. - Current strategies in protein engineering, such as rational design and directed evolution, face challenges including high costs and long experimental cycles, limiting their scalability [2]. Group 2: AI in Protein Engineering - The rapid advancement of artificial intelligence has led to its application in life sciences, particularly in simulating mutations and functional modifications of proteins [3]. - Existing AI models struggle with generalizability across various proteins and require substantial computational and experimental resources, necessitating the development of more efficient and universal protein engineering strategies [3]. Group 3: AiCE Method Development - The AiCE method allows for efficient protein evolution simulation and function design without the need for training dedicated AI models, significantly reducing computational costs [4][12]. - AiCE utilizes existing universal inverse folding models to predict amino acid sequences based on given protein structures, enhancing the accuracy of predictions [5][6]. Group 4: Performance and Applications - AiCE single module achieved a 16% prediction accuracy using 60 deep mutational scanning datasets, with a 37% performance improvement over unrestricted methods [6]. - AiCE multi module predicts mutation combinations effectively while maintaining low computational costs, demonstrating comparable predictive capabilities to larger models [7]. Group 5: Experimental Validation - The research team validated AiCE's functionality across eight diverse proteins, including deaminases and nucleases, confirming its simplicity, efficiency, and versatility [9][10]. - The development of new base editors with enhanced precision and activity, such as enABE8e and enDdd1-DdCBE, showcases AiCE's practical applications in precision medicine and molecular breeding [9][10]. Group 6: Significance and Future Directions - The study highlights the importance of developing efficient bioinformatics tools to reduce computational burdens, making AI-driven protein engineering accessible to more researchers [12]. - The advancements presented in this research mark a significant step forward in the field of protein evolution, elevating AI-based approaches to a new level [12].