Accurate structure prediction of biomolecular interactions with AlphaFold 3.
Abramson Josh, Adler Jonas, Dunger Jack, Evans Richard, Green Tim, Pritzel Alexander, Ronneberger Olaf, Willmore Lindsay, Ballard Andrew J, Bambrick Joshua, Bodenstein Sebastian W, Evans David A, Hung Chia-Chun, O'Neill Michael, Reiman David, Tunyasuvunakool Kathryn, Wu Zachary, Žemgulytė Akvilė, Arvaniti Eirini, Beattie Charles, Bertolli Ottavia, Bridgland Alex, Cherepanov Alexey, Congreve Miles, Cowen-Rivers Alexander I, Cowie Andrew, Figurnov Michael, Fuchs Fabian B, Gladman Hannah, Jain Rishub, Khan Yousuf A, Low Caroline M R, Perlin Kuba, Potapenko Anna, Savy Pascal, Singh Sukhdeep, Stecula Adrian, Thillaisundaram Ashok, Tong Catherine, Yakneen Sergei, Zhong Ellen D, Zielinski Michal, Žídek Augustin, Bapst Victor, Kohli Pushmeet, Jaderberg Max, Hassabis Demis, Jumper John M
Nature · 2024 · PMID 38718835 · 인용 14.3k
The introduction of AlphaFold 21 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design2-6. Here we describe our AlphaFold 3 model with a substantially updated diffusion-based architecture that is capable of predicting the joint structure of complexes including proteins, nucleic acids, small molecules, ions and modified residues. The new AlphaFold model demonstrates substantially improved accuracy over many previous specialized tools: far greater accuracy for protein-ligand interactions compared with state-of-the-art docking tools, much higher accuracy for protein-nucleic acid interactions compared with nucleic-acid-specific predictors and substantially higher antibody-antigen prediction accuracy compared with AlphaFold-Multimer v.2.37,8.
Together, these results show that high-accuracy modelling across biomolecular space is possible within a single unified deep-learning framework.