Core Insights - The presentation by Associate Professor Zhang Shugang from Ocean University of China focuses on the construction and application of an intelligent protein computing system, highlighting breakthroughs in traditional protein research challenges [1][3][4]. Group 1: Traditional Challenges in Protein Research - Proteins play a crucial role in biological functions but face challenges such as high structural analysis costs, delayed functional annotation, and low efficiency in novel protein design [1][3]. - The demand for understanding complex protein characteristics has increased, necessitating innovative approaches to overcome these challenges [1][3]. Group 2: AI-Driven Innovations - The introduction of AI technologies has revolutionized protein research, exemplified by the awarding of the 2024 Nobel Prize in Chemistry for breakthroughs in AI-driven protein structure prediction and design [3][4]. - The intelligent protein computing system enables significant advancements in large-scale functional annotation, interaction prediction, and 3D structure modeling, providing new technical pathways for drug discovery and life system simulation [1][3]. Group 3: Key Breakthroughs in Protein Computing - The core tasks of intelligent protein computing include: 1. **Protein Structure Prediction**: AlphaFold's models have achieved unprecedented accuracy in predicting protein structures, with AlphaFold2 providing atomic-level precision and AlphaFold3 extending capabilities to predict interactions with various biomolecules [4][5]. 2. **Functional Annotation**: The team has developed methods to automate protein function annotation using deep learning, significantly increasing the scale of data processed and improving prediction accuracy [6][7]. 3. **Interaction Prediction**: A self-developed model has been created to enhance the prediction of protein interactions, achieving over 95% accuracy in specific applications [16][20]. 4. **Protein Design**: The potential for designing new proteins has been demonstrated, with innovative approaches being explored for applications in vaccine development and cancer treatment [22]. Group 4: Multiscale Modeling in Life Systems - The research emphasizes the importance of multiscale modeling in understanding complex life systems, integrating various biological scales from molecular to cellular levels [23]. - The team has proposed a comprehensive modeling framework that encompasses multiple research points, aiming for a holistic simulation of life systems [23].

Group 1 - The core idea of the article emphasizes the transformative impact of AI on protein research, which is crucial for understanding diseases and extending human lifespan [1][15][47] - AI is accelerating the exploration of protein structures, with predictions that human lifespan could exceed 100 years, and potentially reach 150 years [1][15] - The development of advanced AI models, such as OpenComplex2, allows for the prediction of dynamic protein structures, enhancing the understanding of protein interactions and their implications for drug development [6][8][14] Group 2 - The article highlights the importance of protein research in modern medicine, as it aids in identifying disease causes and designing precise treatment plans [3][15] - AI's role in drug development is underscored, with the potential to reduce the time and cost associated with bringing new drugs to market, breaking the "double ten law" [22][24] - The integration of AI in protein research is leading to innovative diagnostic methods, such as predicting disease risks through blood plasma protein analysis [17][19] Group 3 - The article discusses the creation of a comprehensive protein database, which supports AI models in predicting protein sequences and structures, significantly improving accuracy and efficiency [12][35] - The Venus model developed by Shanghai Jiao Tong University exemplifies the application of AI in designing functional proteins for various industrial needs [36][41] - The ongoing advancements in AI-driven protein research are expected to lead to breakthroughs in treating rare and complex diseases, enhancing overall health outcomes [47]