Krokidis Marios G, Koumadorakis Dimitrios E, Lazaros Konstantinos, Ivantsik Ouliana, Exarchos Themis P, Vrahatis Aristidis G, Kotsiantis Sotiris, Vlamos Panagiotis
Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece.
Department of Mathematics, University of Patras, 26504 Rio, Greece.
Int J Mol Sci. 2025 Apr 13;26(8):3671. doi: 10.3390/ijms26083671.
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 to model not only single protein structures but also complex biomolecular interactions, including protein-protein interactions, protein-ligand docking, and protein-nucleic acid complexes. Herein, we provide a detailed examination of AlphaFold3's capabilities, emphasizing its applications across diverse biological fields and its effectiveness in complex biological systems. The strengths of the new AI model are also highlighted, including its ability to predict protein structures in dynamic systems, multi-chain assemblies, and complicated biomolecular complexes that were previously challenging to depict. We explore its role in advancing drug discovery, epitope prediction, and the study of disease-related mutations. Despite its significant improvements, the present review also addresses ongoing obstacles, particularly in modeling disordered regions, alternative protein folds, and multi-state conformations. The limitations and future directions of AlphaFold3 are discussed as well, with an emphasis on its potential integration with experimental techniques to further refine predictions. Lastly, the work underscores the transformative contribution of the new model to computational biology, providing new insights into molecular interactions and revolutionizing the fields of accelerated drug design and genomic research.
AlphaFold3是由谷歌DeepMind和Isomorphic Labs开发的AlphaFold的最新版本，旨在以极高的准确性预测蛋白质结构。AlphaFold3不仅增强了我们对单个蛋白质结构进行建模的能力，还提升了对复杂生物分子相互作用的建模能力，包括蛋白质-蛋白质相互作用、蛋白质-配体对接以及蛋白质-核酸复合物。在此，我们详细审视了AlphaFold3的功能，着重介绍了其在不同生物领域的应用以及在复杂生物系统中的有效性。新人工智能模型的优势也得到了凸显，包括其在动态系统、多链组装以及此前难以描绘的复杂生物分子复合物中预测蛋白质结构的能力。我们探讨了它在推动药物发现、表位预测以及疾病相关突变研究方面的作用。尽管有了显著改进，但本综述也讨论了当前存在的障碍，尤其是在对无序区域、蛋白质的替代折叠形式和多状态构象进行建模方面。还讨论了AlphaFold3的局限性和未来发展方向，重点是其与实验技术的潜在整合，以进一步优化预测。最后，这项工作强调了新模型对计算生物学的变革性贡献，为分子相互作用提供了新见解，并彻底改变了加速药物设计和基因组研究领域。
