Highly accurate protein structure prediction with AlphaFold

John Jumper (), Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A. A. Kohl, Andrew J. Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew W. Senior, Koray Kavukcuoglu, Pushmeet Kohli and Demis Hassabis ()
Additional contact information
John Jumper: DeepMind
Richard Evans: DeepMind
Alexander Pritzel: DeepMind
Tim Green: DeepMind
Michael Figurnov: DeepMind
Olaf Ronneberger: DeepMind
Kathryn Tunyasuvunakool: DeepMind
Russ Bates: DeepMind
Augustin Žídek: DeepMind
Anna Potapenko: DeepMind
Alex Bridgland: DeepMind
Clemens Meyer: DeepMind
Simon A. A. Kohl: DeepMind
Andrew J. Ballard: DeepMind
Andrew Cowie: DeepMind
Bernardino Romera-Paredes: DeepMind
Stanislav Nikolov: DeepMind
Rishub Jain: DeepMind
Jonas Adler: DeepMind
Trevor Back: DeepMind
Stig Petersen: DeepMind
David Reiman: DeepMind
Ellen Clancy: DeepMind
Michal Zielinski: DeepMind
Martin Steinegger: Seoul National University
Michalina Pacholska: DeepMind
Tamas Berghammer: DeepMind
Sebastian Bodenstein: DeepMind
David Silver: DeepMind
Oriol Vinyals: DeepMind
Andrew W. Senior: DeepMind
Koray Kavukcuoglu: DeepMind
Pushmeet Kohli: DeepMind
Demis Hassabis: DeepMind

Nature, 2021, vol. 596, issue 7873, 583-589

Abstract: Abstract Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1–4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’8—has been an important open research problem for more than 50 years9. Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm.

Date: 2021
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DOI: 10.1038/s41586-021-03819-2

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