Accurate structure prediction of biomolecular interactions with AlphaFold 3
The introduction of AlphaFold 2 1 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design 2 , 3 , 4 , 5 – 6 . Here we describe our AlphaFold 3 model with a substantially updated diffusion-based arch...
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| Published in | Nature (London) Vol. 630; no. 8016; pp. 493 - 500 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
13.06.2024
Nature Publishing Group |
| Subjects | |
| Online Access | Get full text |
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| Abstract | The introduction of AlphaFold 2
1
has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design
2
,
3
,
4
,
5
–
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.2.3
7
,
8
. Together, these results show that high-accuracy modelling across biomolecular space is possible within a single unified deep-learning framework.
AlphaFold 3 has a substantially updated architecture that is capable of predicting the joint structure of complexes including proteins, nucleic acids, small molecules, ions and modified residues with greatly improved accuracy over many previous specialized tools. |
|---|---|
| AbstractList | The introduction of AlphaFold 2
1
has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design
2
–
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.2.3
7
,
8
. Together, these results show that high-accuracy modelling across biomolecular space is possible within a single unified deep-learning framework.
AlphaFold 3 has a substantially updated architecture that is capable of predicting the joint structure of complexes including proteins, nucleic acids, small molecules, ions and modified residues with greatly improved accuracy over many previous specialized tools. The introduction of AlphaFold 2 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design . 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.2.3 . Together, these results show that high-accuracy modelling across biomolecular space is possible within a single unified deep-learning framework. The introduction of AlphaFold 2 1 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design 2–6 . 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.2.3 7,8 . Together, these results show that high-accuracy modelling across biomolecular space is possible within a single unified deep-learning framework. The introduction of AlphaFold 2 1 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design 2 , 3 , 4 , 5 – 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.2.3 7 , 8 . Together, these results show that high-accuracy modelling across biomolecular space is possible within a single unified deep-learning framework. AlphaFold 3 has a substantially updated architecture that is capable of predicting the joint structure of complexes including proteins, nucleic acids, small molecules, ions and modified residues with greatly improved accuracy over many previous specialized tools. 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.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. 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. |
| Author | O’Neill, Michael Low, Caroline M. R. Zielinski, Michal Jumper, John M. Yakneen, Sergei Wu, Zachary Arvaniti, Eirini Jain, Rishub Thillaisundaram, Ashok Figurnov, Michael Bodenstein, Sebastian W. Pritzel, Alexander Kohli, Pushmeet Congreve, Miles Savy, Pascal Žídek, Augustin Žemgulytė, Akvilė Cherepanov, Alexey Reiman, David Bridgland, Alex Gladman, Hannah Stecula, Adrian Khan, Yousuf A. Adler, Jonas Zhong, Ellen D. Jaderberg, Max Bambrick, Joshua Hassabis, Demis Ronneberger, Olaf Evans, David A. Willmore, Lindsay Perlin, Kuba Potapenko, Anna Abramson, Josh Bertolli, Ottavia Singh, Sukhdeep Cowie, Andrew Tong, Catherine Dunger, Jack Cowen-Rivers, Alexander I. Green, Tim Evans, Richard Ballard, Andrew J. Tunyasuvunakool, Kathryn Bapst, Victor Hung, Chia-Chun Beattie, Charles Fuchs, Fabian B. |
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R. surname: Low fullname: Low, Caroline M. R. organization: Isomorphic Labs – sequence: 33 givenname: Kuba surname: Perlin fullname: Perlin, Kuba organization: Google DeepMind – sequence: 34 givenname: Anna surname: Potapenko fullname: Potapenko, Anna organization: Google DeepMind – sequence: 35 givenname: Pascal surname: Savy fullname: Savy, Pascal organization: Isomorphic Labs – sequence: 36 givenname: Sukhdeep surname: Singh fullname: Singh, Sukhdeep organization: Google DeepMind – sequence: 37 givenname: Adrian orcidid: 0000-0001-6914-6743 surname: Stecula fullname: Stecula, Adrian organization: Isomorphic Labs – sequence: 38 givenname: Ashok surname: Thillaisundaram fullname: Thillaisundaram, Ashok organization: Google DeepMind – sequence: 39 givenname: Catherine orcidid: 0000-0001-7570-4801 surname: Tong fullname: Tong, Catherine organization: Isomorphic Labs – sequence: 40 givenname: Sergei orcidid: 0000-0001-7827-9839 surname: Yakneen fullname: Yakneen, Sergei organization: Isomorphic Labs – sequence: 41 givenname: Ellen D. orcidid: 0000-0001-6345-1907 surname: Zhong fullname: Zhong, Ellen D. organization: Google DeepMind, Department of Computer Science, Princeton University – sequence: 42 givenname: Michal surname: Zielinski fullname: Zielinski, Michal organization: Google DeepMind – sequence: 43 givenname: Augustin orcidid: 0000-0002-0748-9684 surname: Žídek fullname: Žídek, Augustin organization: Google DeepMind – sequence: 44 givenname: Victor surname: Bapst fullname: Bapst, Victor organization: Core Contributor, Google DeepMind – sequence: 45 givenname: Pushmeet orcidid: 0000-0002-7466-7997 surname: Kohli fullname: Kohli, Pushmeet organization: Core Contributor, Google DeepMind – sequence: 46 givenname: Max orcidid: 0000-0002-9033-2695 surname: Jaderberg fullname: Jaderberg, Max email: jaderberg@isomorphiclabs.com organization: Core Contributor, Isomorphic Labs – sequence: 47 givenname: Demis orcidid: 0000-0003-2812-9917 surname: Hassabis fullname: Hassabis, Demis email: dhcontact@google.com organization: Core Contributor, Google DeepMind, Core Contributor, Isomorphic Labs – sequence: 48 givenname: John M. orcidid: 0000-0001-6169-6580 surname: Jumper fullname: Jumper, John M. email: jumper@google.com organization: Core Contributor, Google DeepMind |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38718835$$D View this record in MEDLINE/PubMed |
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| Snippet | The introduction of AlphaFold 2
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has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications... The introduction of AlphaFold 2 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications... The introduction of AlphaFold 21 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications... The introduction of AlphaFold 21 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications... |
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| Title | Accurate structure prediction of biomolecular interactions with AlphaFold 3 |
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