Core Insights - On October 8, 2025, 15 papers were published in the prestigious journal Nature, with 4 authored by Chinese scholars [2][4][6][8] Group 1: Research Contributions - Professor Zhao Huimin from the University of Illinois Urbana-Champaign published a paper titled "Enzyme specificity prediction using cross attention graph neural networks," introducing an innovative AI model called EZSpecificity that predicts enzyme substrate specificity with high accuracy, aiding in enzyme engineering and synthetic biology [2] - Professor Zhou Peng from Fudan University published a paper titled "A full-featured 2D flash chip enabled by system integration I," focusing on advancements in 2D flash memory technology through system integration [4] - MIT's Liu Qi co-authored a paper titled "Quantum-amplified global-phase spectroscopy on an optical clock transition," contributing to the field of quantum spectroscopy [6] - Professors Lou Zhenkun and Huang Jinzhu from Mayo Clinic published a paper titled "KCTD10 is a sensor for co-directional transcription–replication conflicts," revealing a ubiquitin ligase complex that detects transcription-replication conflicts and regulates replication body progression to prevent DNA damage [8]

Core Viewpoint - The article discusses a groundbreaking AI model named EZSpecificity, developed by a research team led by Professor Zhao Huimin from the University of Illinois at Urbana-Champaign, which predicts enzyme substrate specificity with high accuracy, aiding in enzyme engineering and synthetic biology applications [2][3]. Group 1: Enzyme Specificity and Challenges - Enzymes are large protein molecules that catalyze reactions, and their specificity refers to the degree of fit between an enzyme and its substrate, often likened to a "lock and key" mechanism [6]. - Identifying the optimal enzyme-substrate combinations is challenging due to the dynamic nature of enzyme conformations during interactions and the existence of enzyme polymorphism, which complicates predictions [6][8]. Group 2: Development of EZSpecificity Model - The EZSpecificity model combines cross-attention mechanisms with SE(3)-equivariant graph neural networks to accurately predict enzyme substrate specificity, thus providing a new methodological foundation for AI applications in enzyme engineering and green manufacturing [3][8]. - The research team collaborated to create a comprehensive database that includes enzyme sequences, structural information, and conformational data around various substrates, enhancing the model's predictive capabilities [8]. Group 3: Performance and Validation - In tests across four practical scenarios, EZSpecificity outperformed the leading model ESP, achieving a success rate of 91.7% in accurately identifying unique reaction substrates, compared to ESP's 58.3% [11]. - The model is designed to be user-friendly, allowing researchers to input substrate and protein sequences to predict compatibility, marking a shift from "intelligent annotation" to "intelligent recognition" in enzyme research [11]. Group 4: Future Directions - Future plans include expanding the model to analyze enzyme selectivity regarding specific substrate sites, which will help eliminate off-target effects, and continuing to refine EZSpecificity with additional experimental data [11].