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      Highly accurate protein structure prediction with AlphaFold

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      Nature
      Nature Publishing Group UK
      Computational biophysics, Machine learning, Protein structure predictions, Structural biology

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          Abstract

          Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 14 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6, 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 years 9 . Despite recent progress 1014 , 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.

          Abstract

          AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.

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          Author and article information

          Contributors
          jumper@deepmind.com
          dhcontact@deepmind.com
          Journal
          Nature
          Nature
          Nature
          Nature Publishing Group UK (London )
          0028-0836
          1476-4687
          15 July 2021
          15 July 2021
          : 1-7
          Affiliations
          [1 ]GRID grid.498210.6, ISNI 0000 0004 5999 1726, DeepMind, ; London, UK
          [2 ]GRID grid.31501.36, ISNI 0000 0004 0470 5905, School of Biological Sciences, , Seoul National University, ; Seoul, South Korea
          [3 ]GRID grid.31501.36, ISNI 0000 0004 0470 5905, Artificial Intelligence Institute, , Seoul National University, ; Seoul, South Korea
          Author information
          http://orcid.org/0000-0001-6169-6580
          http://orcid.org/0000-0002-3227-1505
          http://orcid.org/0000-0003-4271-4418
          http://orcid.org/0000-0001-9928-3407
          http://orcid.org/0000-0001-8781-9753
          http://orcid.org/0000-0002-2160-6226
          http://orcid.org/0000-0002-5197-2892
          http://orcid.org/0000-0002-2401-5691
          http://orcid.org/0000-0003-2812-9917
          Article
          3819
          10.1038/s41586-021-03819-2
          8371605
          34265844
          b1f93136-f0f0-4c78-8e45-27bd037e9bb9
          © The Author(s) 2021

          Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

          History
          : 11 May 2021
          : 12 July 2021
          Categories
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          Uncategorized
          computational biophysics,machine learning,protein structure predictions,structural biology

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