南开大学郑伟等开发蛋白结构预测新模型：AI+物理模拟，超越AlphaFold2/3

Core Viewpoint - The emergence of D-I-TASSER, a new protein structure prediction tool, demonstrates significant advancements in protein folding prediction, outperforming existing models like AlphaFold2 and AlphaFold3 in accuracy and coverage [3][8]. Group 1: D-I-TASSER Development and Performance - D-I-TASSER was developed by a collaborative research team and has shown superior performance in the CASP15 competition, excelling in both single-domain and multi-domain protein structure predictions [3][8]. - The tool successfully predicted structures for 19,512 proteins from the human proteome, achieving 81% domain coverage and 73% full-length sequence coverage, which is a notable improvement over AlphaFold2 [3][12][14]. - D-I-TASSER integrates deep learning with physical simulations, utilizing multiple sources of information to enhance prediction accuracy [8][14]. Group 2: Technical Innovations - The core innovation of D-I-TASSER lies in its hybrid approach, combining deep learning with physical modeling to refine protein structure predictions [8][17]. - The tool employs an upgraded DeepMSA2 for multi-sequence alignment, increasing information retrieval from metagenomic databases by 6.75 times [11]. - D-I-TASSER's modeling process includes a unique workflow of automatic domain cutting, independent prediction, and dynamic assembly, resulting in improved accuracy and reduced orientation errors [8][11]. Group 3: Challenges and Future Directions - Despite its impressive performance, D-I-TASSER faces challenges such as reduced prediction accuracy for orphan proteins and higher computational time compared to pure deep learning models [20]. - The research indicates that the ultimate solution to protein folding may lie in the deep synergy between data-driven methods and physical simulations [17][20]. - The D-I-TASSER model and its human protein structure prediction database have been made open-source, promoting further research and collaboration in the field [17].