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Automated identification of chalcogen bonds in AlphaFold protein structure database files: is it possible?

Protein structure prediction and structural biology have entered a new era with an artificial intelligence-based approach encoded in the AlphaFold2 and the analogous RoseTTAfold methods. More than 200 million structures have been predicted by AlphaFold2 from their primary sequences and the models as...

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Autores principales: Carugo, Oliviero, Djinović-Carugo, Kristina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10359982/
https://www.ncbi.nlm.nih.gov/pubmed/37484534
http://dx.doi.org/10.3389/fmolb.2023.1155629
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author Carugo, Oliviero
Djinović-Carugo, Kristina
author_facet Carugo, Oliviero
Djinović-Carugo, Kristina
author_sort Carugo, Oliviero
collection PubMed
description Protein structure prediction and structural biology have entered a new era with an artificial intelligence-based approach encoded in the AlphaFold2 and the analogous RoseTTAfold methods. More than 200 million structures have been predicted by AlphaFold2 from their primary sequences and the models as well as the approach itself have naturally been examined from different points of view by experimentalists and bioinformaticians. Here, we assessed the degree to which these computational models can provide information on subtle structural details with potential implications for diverse applications in protein engineering and chemical biology and focused the attention on chalcogen bonds formed by disulphide bridges. We found that only 43% of the chalcogen bonds observed in the experimental structures are present in the computational models, suggesting that the accuracy of the computational models is, in the majority of the cases, insufficient to allow the detection of chalcogen bonds, according to the usual stereochemical criteria. High-resolution experimentally derived structures are therefore still necessary when the structure must be investigated in depth based on fine structural aspects.
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spelling pubmed-103599822023-07-22 Automated identification of chalcogen bonds in AlphaFold protein structure database files: is it possible? Carugo, Oliviero Djinović-Carugo, Kristina Front Mol Biosci Molecular Biosciences Protein structure prediction and structural biology have entered a new era with an artificial intelligence-based approach encoded in the AlphaFold2 and the analogous RoseTTAfold methods. More than 200 million structures have been predicted by AlphaFold2 from their primary sequences and the models as well as the approach itself have naturally been examined from different points of view by experimentalists and bioinformaticians. Here, we assessed the degree to which these computational models can provide information on subtle structural details with potential implications for diverse applications in protein engineering and chemical biology and focused the attention on chalcogen bonds formed by disulphide bridges. We found that only 43% of the chalcogen bonds observed in the experimental structures are present in the computational models, suggesting that the accuracy of the computational models is, in the majority of the cases, insufficient to allow the detection of chalcogen bonds, according to the usual stereochemical criteria. High-resolution experimentally derived structures are therefore still necessary when the structure must be investigated in depth based on fine structural aspects. Frontiers Media S.A. 2023-07-06 /pmc/articles/PMC10359982/ /pubmed/37484534 http://dx.doi.org/10.3389/fmolb.2023.1155629 Text en Copyright © 2023 Carugo and Djinović-Carugo. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Molecular Biosciences
Carugo, Oliviero
Djinović-Carugo, Kristina
Automated identification of chalcogen bonds in AlphaFold protein structure database files: is it possible?
title Automated identification of chalcogen bonds in AlphaFold protein structure database files: is it possible?
title_full Automated identification of chalcogen bonds in AlphaFold protein structure database files: is it possible?
title_fullStr Automated identification of chalcogen bonds in AlphaFold protein structure database files: is it possible?
title_full_unstemmed Automated identification of chalcogen bonds in AlphaFold protein structure database files: is it possible?
title_short Automated identification of chalcogen bonds in AlphaFold protein structure database files: is it possible?
title_sort automated identification of chalcogen bonds in alphafold protein structure database files: is it possible?
topic Molecular Biosciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10359982/
https://www.ncbi.nlm.nih.gov/pubmed/37484534
http://dx.doi.org/10.3389/fmolb.2023.1155629
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