Improved protein structure prediction using potentials from deep learning

Andrew W. Senior (), Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin Žídek, Alexander W. R. Nelson, Alex Bridgland, Hugo Penedones, Stig Petersen, Karen Simonyan, Steve Crossan, Pushmeet Kohli, David T. Jones, David Silver, Koray Kavukcuoglu and Demis Hassabis
Additional contact information
Andrew W. Senior: DeepMind
Richard Evans: DeepMind
John Jumper: DeepMind
James Kirkpatrick: DeepMind
Laurent Sifre: DeepMind
Tim Green: DeepMind
Chongli Qin: DeepMind
Augustin Žídek: DeepMind
Alexander W. R. Nelson: DeepMind
Alex Bridgland: DeepMind
Hugo Penedones: DeepMind
Stig Petersen: DeepMind
Karen Simonyan: DeepMind
Steve Crossan: DeepMind
Pushmeet Kohli: DeepMind
David T. Jones: The Francis Crick Institute
David Silver: DeepMind
Koray Kavukcuoglu: DeepMind
Demis Hassabis: DeepMind

Nature, 2020, vol. 577, issue 7792, 706-710

Abstract: Abstract Protein structure prediction can be used to determine the three-dimensional shape of a protein from its amino acid sequence1. This problem is of fundamental importance as the structure of a protein largely determines its function2; however, protein structures can be difficult to determine experimentally. Considerable progress has recently been made by leveraging genetic information. It is possible to infer which amino acid residues are in contact by analysing covariation in homologous sequences, which aids in the prediction of protein structures3. Here we show that we can train a neural network to make accurate predictions of the distances between pairs of residues, which convey more information about the structure than contact predictions. Using this information, we construct a potential of mean force4 that can accurately describe the shape of a protein. We find that the resulting potential can be optimized by a simple gradient descent algorithm to generate structures without complex sampling procedures. The resulting system, named AlphaFold, achieves high accuracy, even for sequences with fewer homologous sequences. In the recent Critical Assessment of Protein Structure Prediction5 (CASP13)—a blind assessment of the state of the field—AlphaFold created high-accuracy structures (with template modelling (TM) scores6 of 0.7 or higher) for 24 out of 43 free modelling domains, whereas the next best method, which used sampling and contact information, achieved such accuracy for only 14 out of 43 domains. AlphaFold represents a considerable advance in protein-structure prediction. We expect this increased accuracy to enable insights into the function and malfunction of proteins, especially in cases for which no structures for homologous proteins have been experimentally determined7.

Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (35)

Downloads: (external link)
https://www.nature.com/articles/s41586-019-1923-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:nature:v:577:y:2020:i:7792:d:10.1038_s41586-019-1923-7

Ordering information: This journal article can be ordered from
https://www.nature.com/

DOI: 10.1038/s41586-019-1923-7

Access Statistics for this article

Nature is currently edited by Magdalena Skipper

More articles in Nature from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().