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].