Applying and improving AlphaFold at CASP14

We describe the operation and improvement of AlphaFold, the system that was entered by the team AlphaFold2 to the “human” category in the 14th Critical Assessment of Protein Structure Prediction (CASP14). The AlphaFold system entered in CASP14 is entirely different to the one entered in CASP13. It u...

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Published in:Proteins, structure, function, and bioinformatics Vol. 89; no. 12; pp. 1711 - 1721
Main Authors: Jumper, John, Evans, Richard, Pritzel, Alexander, Green, Tim, Figurnov, Michael, Ronneberger, Olaf, Tunyasuvunakool, Kathryn, Bates, Russ, Žídek, Augustin, Potapenko, Anna, Bridgland, Alex, Meyer, Clemens, Kohl, Simon A. A., Ballard, Andrew J., Cowie, Andrew, Romera‐Paredes, Bernardino, Nikolov, Stanislav, Jain, Rishub, Adler, Jonas, Back, Trevor, Petersen, Stig, Reiman, David, Clancy, Ellen, Zielinski, Michal, Steinegger, Martin, Pacholska, Michalina, Berghammer, Tamas, Silver, David, Vinyals, Oriol, Senior, Andrew W., Kavukcuoglu, Koray, Kohli, Pushmeet, Hassabis, Demis
Format: Journal Article
Language:English
Published: Hoboken, USA John Wiley & Sons, Inc 01.12.2021
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Subjects:
AlphaFold
Amino Acid Sequence
Amino acids
Artificial neural networks
CASP
Computational Biology
Deep Learning
Homology
machine learning
Models, Molecular
Neural networks
Neural Networks, Computer
Predictions
Protein Conformation
Protein Folding
Protein structure
protein structure prediction
Proteins
Proteins - chemistry
Proteins - metabolism
Sequence Analysis, Protein
Software
deep learning
CASP
machine learning
protein structure prediction
AlphaFold
ISSN:0887-3585, 1097-0134, 1097-0134
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Summary:We describe the operation and improvement of AlphaFold, the system that was entered by the team AlphaFold2 to the “human” category in the 14th Critical Assessment of Protein Structure Prediction (CASP14). The AlphaFold system entered in CASP14 is entirely different to the one entered in CASP13. It used a novel end‐to‐end deep neural network trained to produce protein structures from amino acid sequence, multiple sequence alignments, and homologous proteins. In the assessors' ranking by summed z scores (>2.0), AlphaFold scored 244.0 compared to 90.8 by the next best group. The predictions made by AlphaFold had a median domain GDT_TS of 92.4; this is the first time that this level of average accuracy has been achieved during CASP, especially on the more difficult Free Modeling targets, and represents a significant improvement in the state of the art in protein structure prediction. We reported how AlphaFold was run as a human team during CASP14 and improved such that it now achieves an equivalent level of performance without intervention, opening the door to highly accurate large‐scale structure prediction.
Bibliography:Funding information
National Research Foundation of Korea, Grant/Award Numbers: 2019R1A6A1A10073437, 2020M3A9G7103933; Seoul National University, Grant/Award Numbers: Creative‐Pioneering Researchers Program, New Faculty Startup Fund
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, and Demis Hassabis contributed equally.
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Funding information National Research Foundation of Korea, Grant/Award Numbers: 2019R1A6A1A10073437, 2020M3A9G7103933; Seoul National University, Grant/Award Numbers: Creative‐Pioneering Researchers Program, New Faculty Startup Fund
ISSN:0887-3585
1097-0134
1097-0134
DOI:10.1002/prot.26257