Using AlphaFold to predict the impact of single mutations on protein stability and function.
AlphaFold changed the field of structural biology by achieving three-dimensional (3D) structure prediction from protein sequence at experimental quality. The astounding success even led to claims that the protein folding problem is "solved". However, protein folding problem is more than ju...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0282689 |
| _version_ | 1797842478422818816 |
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| author | Marina A Pak Karina A Markhieva Mariia S Novikova Dmitry S Petrov Ilya S Vorobyev Ekaterina S Maksimova Fyodor A Kondrashov Dmitry N Ivankov |
| author_facet | Marina A Pak Karina A Markhieva Mariia S Novikova Dmitry S Petrov Ilya S Vorobyev Ekaterina S Maksimova Fyodor A Kondrashov Dmitry N Ivankov |
| author_sort | Marina A Pak |
| collection | DOAJ |
| description | AlphaFold changed the field of structural biology by achieving three-dimensional (3D) structure prediction from protein sequence at experimental quality. The astounding success even led to claims that the protein folding problem is "solved". However, protein folding problem is more than just structure prediction from sequence. Presently, it is unknown if the AlphaFold-triggered revolution could help to solve other problems related to protein folding. Here we assay the ability of AlphaFold to predict the impact of single mutations on protein stability (ΔΔG) and function. To study the question we extracted the pLDDT and <pLDDT> metrics from AlphaFold predictions before and after single mutation in a protein and correlated the predicted change with the experimentally known ΔΔG values. Additionally, we correlated the same AlphaFold pLDDT metrics with the impact of a single mutation on structure using a large scale dataset of single mutations in GFP with the experimentally assayed levels of fluorescence. We found a very weak or no correlation between AlphaFold output metrics and change of protein stability or fluorescence. Our results imply that AlphaFold may not be immediately applied to other problems or applications in protein folding. |
| first_indexed | 2024-04-09T16:48:35Z |
| format | Article |
| id | doaj.art-f93d385516134f79a976bef3989c0315 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-09T16:48:35Z |
| publishDate | 2023-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-f93d385516134f79a976bef3989c03152023-04-22T05:31:54ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-01183e028268910.1371/journal.pone.0282689Using AlphaFold to predict the impact of single mutations on protein stability and function.Marina A PakKarina A MarkhievaMariia S NovikovaDmitry S PetrovIlya S VorobyevEkaterina S MaksimovaFyodor A KondrashovDmitry N IvankovAlphaFold changed the field of structural biology by achieving three-dimensional (3D) structure prediction from protein sequence at experimental quality. The astounding success even led to claims that the protein folding problem is "solved". However, protein folding problem is more than just structure prediction from sequence. Presently, it is unknown if the AlphaFold-triggered revolution could help to solve other problems related to protein folding. Here we assay the ability of AlphaFold to predict the impact of single mutations on protein stability (ΔΔG) and function. To study the question we extracted the pLDDT and <pLDDT> metrics from AlphaFold predictions before and after single mutation in a protein and correlated the predicted change with the experimentally known ΔΔG values. Additionally, we correlated the same AlphaFold pLDDT metrics with the impact of a single mutation on structure using a large scale dataset of single mutations in GFP with the experimentally assayed levels of fluorescence. We found a very weak or no correlation between AlphaFold output metrics and change of protein stability or fluorescence. Our results imply that AlphaFold may not be immediately applied to other problems or applications in protein folding.https://doi.org/10.1371/journal.pone.0282689 |
| spellingShingle | Marina A Pak Karina A Markhieva Mariia S Novikova Dmitry S Petrov Ilya S Vorobyev Ekaterina S Maksimova Fyodor A Kondrashov Dmitry N Ivankov Using AlphaFold to predict the impact of single mutations on protein stability and function. PLoS ONE |
| title | Using AlphaFold to predict the impact of single mutations on protein stability and function. |
| title_full | Using AlphaFold to predict the impact of single mutations on protein stability and function. |
| title_fullStr | Using AlphaFold to predict the impact of single mutations on protein stability and function. |
| title_full_unstemmed | Using AlphaFold to predict the impact of single mutations on protein stability and function. |
| title_short | Using AlphaFold to predict the impact of single mutations on protein stability and function. |
| title_sort | using alphafold to predict the impact of single mutations on protein stability and function |
| url | https://doi.org/10.1371/journal.pone.0282689 |
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