Core Insights - The article discusses the significance of AlphaFold2, an AI tool developed by DeepMind, in predicting protein structures, particularly the giant protein titin, which has eluded complete structural analysis for over 70 years [1][3][4] Group 1: AlphaFold2 and Protein Structure Prediction - AlphaFold2 has revolutionized the field of protein structure prediction, achieving over 90% accuracy in predicting protein structures from amino acid sequences during the global protein structure prediction competition (CASP) in 2020 [6][4] - The database created by AlphaFold now contains over 200 million predicted protein structures, covering 98.5% of the human proteome, enabling researchers worldwide to explore protein functions more efficiently [6][4] - AlphaFold2 was utilized during the early stages of the COVID-19 pandemic to predict the structures of viral proteins, aiding in understanding the virus's mechanisms and potential treatments [8][10] Group 2: Applications in Disease Research - Researchers are using AlphaFold to study the impact of genetic mutations on diseases, such as osteoporosis, by comparing the structures of normal and mutated proteins [11][13] - The introduction of AlphaMissense allows scientists to assess the pathogenic potential of missense mutations, successfully categorizing 89% of human missense mutations and creating a directory for further research [13][11] Group 3: Environmental and Pharmaceutical Innovations - AlphaFold2 is also being applied to address environmental issues, such as plastic pollution, by helping scientists design enzymes that can efficiently degrade single-use plastics [14][17] - The integration of AlphaFold2 into drug discovery platforms, like Insilico Medicine's Pharma.AI, has led to the identification of a candidate drug for idiopathic pulmonary fibrosis, Rentosertib, which is currently in Phase II clinical trials [18][20] Group 4: Future Developments - The article highlights ongoing advancements in protein research, including the discovery of a new protein larger than titin and the release of AlphaFold3 and AlphaProteo, which enhance predictions of protein interactions and custom protein design [23][21] - Other AI models, such as RoseTTAFold and I-TASSER, are also contributing to solving long-standing challenges in protein folding, indicating a collaborative effort in the field [23]

Core Viewpoint - The 2025 Nobel Prize in Chemistry has been awarded to Susumu Kitagawa, Richard Robson, and Omar M. Yaghi for their contributions to the development of metal-organic frameworks [1] Group 1: Awardees Background - Susumu Kitagawa, born in 1951, is affiliated with Kyoto University and focuses on the fundamental research and application development of metal-organic framework materials [3] - Richard Robson, born in 1937, works at the University of Melbourne and has made significant contributions to the theoretical foundations of metal-organic frameworks [6] - Omar M. Yaghi is a professor at the University of California, Berkeley, known for major breakthroughs in the synthesis methods and practical applications of metal-organic frameworks [9] Group 2: Nobel Prize History - As of October 2024, the Nobel Prize in Chemistry has been awarded 116 times to 197 recipients, with 63 awards given to individuals, 25 shared by two, and 28 shared by three [12] - Notable statistics include 8 years where the award was not given, 9 years of delayed awards, and the recognition of 8 female laureates [12] - The youngest laureate was Jean Frédéric Joliot-Curie, who won at age 35 in 1935, while the oldest was John Goodenough, awarded at age 97 for his work on lithium batteries [12] Group 3: Recent Nobel Prize Winners - In 2024, half of the prize was awarded to David Baker, with the other half shared by Demis Hassabis and John Jumper for their contributions to protein design and structure prediction [13] - The 2023 prize was awarded to Mogi Bawendi, Louis Brus, and Alexei Ekimov for their discovery and synthesis of quantum dots [14] - In 2022, the award went to Carolyn Bertozzi, Morten Meldal, and Carolyn Bertozzi for their work in click chemistry and bioorthogonal chemistry [15]

Group 1 - The 2025 Nobel Prize in Chemistry was awarded to Susumu Kitagawa, Richard Robson, and Omar M. Yaghi for their contributions to the development of metal-organic frameworks, with a total prize amount of 11 million Swedish Krona (approximately 8.36 million RMB) [1] Group 2 - The Nobel Prize in Chemistry has been awarded 116 times since 1901, highlighting the importance of interdisciplinary research, with many laureates coming from fields such as biology and physics [2] - The 2024 Nobel Prize in Chemistry was awarded to David Baker, Demis Hassabis, and John Jumper for their groundbreaking work in protein structure prediction using artificial intelligence [5][12]

Core Points - The 2025 Nobel Prize in Chemistry has been awarded to Susumu Kitagawa, Richard Robson, and Omar M. Yaghi for their contributions to the development of metal-organic frameworks [1] Group 1 - As of October 2024, the Nobel Prize in Chemistry has been awarded 116 times to 197 recipients, with 63 awards given to individuals, 25 shared by two, and 28 shared by three [2] - Notable statistics include 8 years where the award was not given, 9 years of delayed awards, and the fact that 2 individuals have won the prize twice [2] - The youngest laureate was Jean Frédéric Joliot-Curie, who won at age 35 in 1935, while the oldest was John Goodenough, who won at age 97 and passed away in June 2023 at the age of 100 [2] Group 2 - Recent Nobel Prize winners in Chemistry include David Baker, Demis Hassabis, and John Jumper in 2024 for their work in protein design and structure prediction [3] - In 2023, the prize was awarded to Mogi Bawendi, Louis Bruce, and Alexei Ekimov for their discovery and synthesis of quantum dots [4] - The 2022 award went to Carolyn Bertozzi, Morten Meldal, and Carolyn Bertozzi for their contributions to click chemistry and bioorthogonal chemistry [4]

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].