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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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-07-01
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| Series: | Frontiers in Molecular Biosciences |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmolb.2023.1155629/full |
| _version_ | 1797786121174777856 |
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| author | Oliviero Carugo Oliviero Carugo Kristina Djinović-Carugo Kristina Djinović-Carugo Kristina Djinović-Carugo |
| author_facet | Oliviero Carugo Oliviero Carugo Kristina Djinović-Carugo Kristina Djinović-Carugo Kristina Djinović-Carugo |
| author_sort | Oliviero Carugo |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-13T01:03:32Z |
| format | Article |
| id | doaj.art-fc8236601e3d4221978b779ace9231dc |
| institution | Directory Open Access Journal |
| issn | 2296-889X |
| language | English |
| last_indexed | 2024-03-13T01:03:32Z |
| publishDate | 2023-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Molecular Biosciences |
| spelling | doaj.art-fc8236601e3d4221978b779ace9231dc2023-07-06T09:51:48ZengFrontiers Media S.A.Frontiers in Molecular Biosciences2296-889X2023-07-011010.3389/fmolb.2023.11556291155629Automated identification of chalcogen bonds in AlphaFold protein structure database files: is it possible?Oliviero Carugo0Oliviero Carugo1Kristina Djinović-Carugo2Kristina Djinović-Carugo3Kristina Djinović-Carugo4Department of Chemistry, University of Pavia, Pavia, ItalyMax Perutz Labs, Department of Structural and Computational Biology, University of Vienna, Vienna, AustriaMax Perutz Labs, Department of Structural and Computational Biology, University of Vienna, Vienna, AustriaEuropean Molecular Biology Laboratory (EMBL) Grenoble, Grenoble, FranceDepartment of Biochemistry, Faculty of Chemistry and Chemical Technology, University of Ljubljana, Ljubljana, SloveniaProtein 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.https://www.frontiersin.org/articles/10.3389/fmolb.2023.1155629/fullAlphaFoldchalcogen bond3D structure predictionstereochemical criteriaexperimental 3D structure |
| spellingShingle | Oliviero Carugo Oliviero Carugo Kristina Djinović-Carugo Kristina Djinović-Carugo Kristina Djinović-Carugo Automated identification of chalcogen bonds in AlphaFold protein structure database files: is it possible? Frontiers in Molecular Biosciences AlphaFold chalcogen bond 3D structure prediction stereochemical criteria experimental 3D structure |
| 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 | AlphaFold chalcogen bond 3D structure prediction stereochemical criteria experimental 3D structure |
| url | https://www.frontiersin.org/articles/10.3389/fmolb.2023.1155629/full |
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