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The impact of AlphaFold2 on experimental structure solution
AlphaFold2 is a machine-learning based program that predicts a protein structure based on the amino acid sequence. In this article, we report on the current usages of this new tool and give examples from our work in the Coronavirus Structural Task Force. With its unprecedented accuracy, it can be ut...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society of Chemistry
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10231047/ https://www.ncbi.nlm.nih.gov/pubmed/35943157 http://dx.doi.org/10.1039/d2fd00072e |
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author | Edich, Maximilian Briggs, David C. Kippes, Oliver Gao, Yunyun Thorn, Andrea |
author_facet | Edich, Maximilian Briggs, David C. Kippes, Oliver Gao, Yunyun Thorn, Andrea |
author_sort | Edich, Maximilian |
collection | PubMed |
description | AlphaFold2 is a machine-learning based program that predicts a protein structure based on the amino acid sequence. In this article, we report on the current usages of this new tool and give examples from our work in the Coronavirus Structural Task Force. With its unprecedented accuracy, it can be utilized for the design of expression constructs, de novo protein design and the interpretation of Cryo-EM data with an atomic model. However, these methods are limited by their training data and are of limited use to predict conformational variability and fold flexibility; they also lack co-factors, post-translational modifications and multimeric complexes with oligonucleotides. They also are not always perfect in terms of chemical geometry. Nevertheless, machine learning-based fold prediction is a game changer for structural bioinformatics and experimentalists alike, with exciting developments ahead. |
format | Online Article Text |
id | pubmed-10231047 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Royal Society of Chemistry |
record_format | MEDLINE/PubMed |
spelling | pubmed-102310472023-06-01 The impact of AlphaFold2 on experimental structure solution Edich, Maximilian Briggs, David C. Kippes, Oliver Gao, Yunyun Thorn, Andrea Faraday Discuss Chemistry AlphaFold2 is a machine-learning based program that predicts a protein structure based on the amino acid sequence. In this article, we report on the current usages of this new tool and give examples from our work in the Coronavirus Structural Task Force. With its unprecedented accuracy, it can be utilized for the design of expression constructs, de novo protein design and the interpretation of Cryo-EM data with an atomic model. However, these methods are limited by their training data and are of limited use to predict conformational variability and fold flexibility; they also lack co-factors, post-translational modifications and multimeric complexes with oligonucleotides. They also are not always perfect in terms of chemical geometry. Nevertheless, machine learning-based fold prediction is a game changer for structural bioinformatics and experimentalists alike, with exciting developments ahead. The Royal Society of Chemistry 2022-05-24 /pmc/articles/PMC10231047/ /pubmed/35943157 http://dx.doi.org/10.1039/d2fd00072e Text en This journal is © The Royal Society of Chemistry https://creativecommons.org/licenses/by-nc/3.0/ |
spellingShingle | Chemistry Edich, Maximilian Briggs, David C. Kippes, Oliver Gao, Yunyun Thorn, Andrea The impact of AlphaFold2 on experimental structure solution |
title | The impact of AlphaFold2 on experimental structure solution |
title_full | The impact of AlphaFold2 on experimental structure solution |
title_fullStr | The impact of AlphaFold2 on experimental structure solution |
title_full_unstemmed | The impact of AlphaFold2 on experimental structure solution |
title_short | The impact of AlphaFold2 on experimental structure solution |
title_sort | impact of alphafold2 on experimental structure solution |
topic | Chemistry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10231047/ https://www.ncbi.nlm.nih.gov/pubmed/35943157 http://dx.doi.org/10.1039/d2fd00072e |
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