Using AlphaFold to predict the impact of single mutations on protein stability and function

Marina A Pak, Karina A Markhieva, Mariia S Novikova, Dmitry S Petrov, Ilya S Vorobyev, Ekaterina S Maksimova, Fyodor A Kondrashov and Dmitry N Ivankov

PLOS ONE, 2023, vol. 18, issue 3, 1-9

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 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.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0282689

DOI: 10.1371/journal.pone.0282689

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