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The breakthrough in protein structure prediction
Proteins are the essential agents of all living systems. Even though they are synthesized as linear chains of amino acids, they must assume specific three-dimensional structures in order to manifest their biological activity. These structures are fully specified in their amino acid sequences — and t...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Portland Press Ltd.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8166336/ https://www.ncbi.nlm.nih.gov/pubmed/34029366 http://dx.doi.org/10.1042/BCJ20200963 |
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author | Lupas, Andrei N. Pereira, Joana Alva, Vikram Merino, Felipe Coles, Murray Hartmann, Marcus D. |
author_facet | Lupas, Andrei N. Pereira, Joana Alva, Vikram Merino, Felipe Coles, Murray Hartmann, Marcus D. |
author_sort | Lupas, Andrei N. |
collection | PubMed |
description | Proteins are the essential agents of all living systems. Even though they are synthesized as linear chains of amino acids, they must assume specific three-dimensional structures in order to manifest their biological activity. These structures are fully specified in their amino acid sequences — and therefore in the nucleotide sequences of their genes. However, the relationship between sequence and structure, known as the protein folding problem, has remained elusive for half a century, despite sustained efforts. To measure progress on this problem, a series of doubly blind, biennial experiments called CASP (critical assessment of structure prediction) were established in 1994. We were part of the assessment team for the most recent CASP experiment, CASP14, where we witnessed an astonishing breakthrough by DeepMind, the leading artificial intelligence laboratory of Alphabet Inc. The models filed by DeepMind's structure prediction team using the program AlphaFold2 were often essentially indistinguishable from experimental structures, leading to a consensus in the community that the structure prediction problem for single protein chains has been solved. Here, we will review the path to CASP14, outline the method employed by AlphaFold2 to the extent revealed, and discuss the implications of this breakthrough for the life sciences. |
format | Online Article Text |
id | pubmed-8166336 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Portland Press Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-81663362021-06-11 The breakthrough in protein structure prediction Lupas, Andrei N. Pereira, Joana Alva, Vikram Merino, Felipe Coles, Murray Hartmann, Marcus D. Biochem J Bioinformatics Proteins are the essential agents of all living systems. Even though they are synthesized as linear chains of amino acids, they must assume specific three-dimensional structures in order to manifest their biological activity. These structures are fully specified in their amino acid sequences — and therefore in the nucleotide sequences of their genes. However, the relationship between sequence and structure, known as the protein folding problem, has remained elusive for half a century, despite sustained efforts. To measure progress on this problem, a series of doubly blind, biennial experiments called CASP (critical assessment of structure prediction) were established in 1994. We were part of the assessment team for the most recent CASP experiment, CASP14, where we witnessed an astonishing breakthrough by DeepMind, the leading artificial intelligence laboratory of Alphabet Inc. The models filed by DeepMind's structure prediction team using the program AlphaFold2 were often essentially indistinguishable from experimental structures, leading to a consensus in the community that the structure prediction problem for single protein chains has been solved. Here, we will review the path to CASP14, outline the method employed by AlphaFold2 to the extent revealed, and discuss the implications of this breakthrough for the life sciences. Portland Press Ltd. 2021-05-28 2021-05-24 /pmc/articles/PMC8166336/ /pubmed/34029366 http://dx.doi.org/10.1042/BCJ20200963 Text en © 2021 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY) (https://creativecommons.org/licenses/by/4.0/) . Open access for this article was enabled by the participation of Max Planck Institute for Developmental Biology in an all-inclusive Read and Publish pilot with Portland Press and the Biochemical Society under a transformative agreement with Max Planck Digital Library. |
spellingShingle | Bioinformatics Lupas, Andrei N. Pereira, Joana Alva, Vikram Merino, Felipe Coles, Murray Hartmann, Marcus D. The breakthrough in protein structure prediction |
title | The breakthrough in protein structure prediction |
title_full | The breakthrough in protein structure prediction |
title_fullStr | The breakthrough in protein structure prediction |
title_full_unstemmed | The breakthrough in protein structure prediction |
title_short | The breakthrough in protein structure prediction |
title_sort | breakthrough in protein structure prediction |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8166336/ https://www.ncbi.nlm.nih.gov/pubmed/34029366 http://dx.doi.org/10.1042/BCJ20200963 |
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