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• W4327566741 startingPage "e0282689" @default.
• W4327566741 abstract "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." @default.
• W4327566741 created "2023-03-17" @default.
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• W4327566741 date "2023-03-16" @default.
• W4327566741 modified "2023-10-14" @default.
• W4327566741 title "Using AlphaFold to predict the impact of single mutations on protein stability and function" @default.
• W4327566741 cites W2021928977 @default.
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• W4327566741 doi "https://doi.org/10.1371/journal.pone.0282689" @default.
• W4327566741 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36928239" @default.
• W4327566741 hasPublicationYear "2023" @default.
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