AlphaFold3: An Overview of Applications and Performance Insights DOI Open Access
Marios G. Krokidis, Dimitrios E. Koumadorakis, Konstantinos Lazaros

и другие.

International Journal of Molecular Sciences, Год журнала: 2025, Номер 26(8), С. 3671 - 3671

Опубликована: Апрель 13, 2025

AlphaFold3, the latest release of AlphaFold developed by Google DeepMind and Isomorphic Labs, was designed to predict protein structures with remarkable accuracy. AlphaFold3 enhances our ability model not only single but also complex biomolecular interactions, including protein–protein protein–ligand docking, protein-nucleic acid complexes. Herein, we provide a detailed examination AlphaFold3’s capabilities, emphasizing its applications across diverse biological fields effectiveness in systems. The strengths new AI are highlighted, dynamic systems, multi-chain assemblies, complicated complexes that were previously challenging depict. We explore role advancing drug discovery, epitope prediction, study disease-related mutations. Despite significant improvements, present review addresses ongoing obstacles, particularly modeling disordered regions, alternative folds, multi-state conformations. limitations future directions discussed as well, an emphasis on potential integration experimental techniques further refine predictions. Lastly, work underscores transformative contribution computational biology, providing insights into molecular interactions revolutionizing accelerated design genomic research.

Язык: Английский

AlphaFold3: An Overview of Applications and Performance Insights DOI Open Access
Marios G. Krokidis, Dimitrios E. Koumadorakis, Konstantinos Lazaros

и другие.

International Journal of Molecular Sciences, Год журнала: 2025, Номер 26(8), С. 3671 - 3671

Опубликована: Апрель 13, 2025

AlphaFold3, the latest release of AlphaFold developed by Google DeepMind and Isomorphic Labs, was designed to predict protein structures with remarkable accuracy. AlphaFold3 enhances our ability model not only single but also complex biomolecular interactions, including protein–protein protein–ligand docking, protein-nucleic acid complexes. Herein, we provide a detailed examination AlphaFold3’s capabilities, emphasizing its applications across diverse biological fields effectiveness in systems. The strengths new AI are highlighted, dynamic systems, multi-chain assemblies, complicated complexes that were previously challenging depict. We explore role advancing drug discovery, epitope prediction, study disease-related mutations. Despite significant improvements, present review addresses ongoing obstacles, particularly modeling disordered regions, alternative folds, multi-state conformations. limitations future directions discussed as well, an emphasis on potential integration experimental techniques further refine predictions. Lastly, work underscores transformative contribution computational biology, providing insights into molecular interactions revolutionizing accelerated design genomic research.

Язык: Английский

Процитировано

0