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

View ORCID ProfileMarina A. Pak, Karina A. Markhieva, Mariia S. Novikova, Dmitry S. Petrov, Ilya S. Vorobyev, Ekaterina S. Maksimova, View ORCID ProfileFyodor A. Kondrashov, View ORCID ProfileDmitry N. Ivankov
doi: https://doi.org/10.1101/2021.09.19.460937
Marina A. Pak
1Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia
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Karina A. Markhieva
2Peoples’ Friendship University of Russia (RUDN University), Moscow, Russia
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Mariia S. Novikova
3Armand Hammer United World College of the American West, New Mexico, USA
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Dmitry S. Petrov
4Specialized Educational and Scientific Center of UrFU (SUNC UrFU), Ekaterinburg, Russia
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Ilya S. Vorobyev
1Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia
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Ekaterina S. Maksimova
5Institute of Science and Technology Austria, Maria Gugging, Austria
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Fyodor A. Kondrashov
5Institute of Science and Technology Austria, Maria Gugging, Austria
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Dmitry N. Ivankov
1Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia
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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 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 AlphaFold predictions on the impact of a single mutation on structure with 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 cannot be immediately applied to other problems or applications in protein folding.

Competing Interest Statement

The authors have declared no competing interest.

  • Abbreviations
    3D
    three-dimensional
    CASP
    critical assessment of protein structure prediction
    PDB
    protein data bank
    NMR
    nuclear magnetic resonance
    DNA
    deoxyribonucleic acid
    RNA
    ribonucleic acid
    PCC
    Pearson correlation coefficient
    LDDT
    local distance difference test
    pLDDT
    per-residue local distance difference test
    GFP
    green fluorescence protein
    BLAST
    basic local alignment search tool
  • Copyright 
    The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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    Posted September 20, 2021.
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    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, Dmitry N. Ivankov
    bioRxiv 2021.09.19.460937; doi: https://doi.org/10.1101/2021.09.19.460937
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    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, Dmitry N. Ivankov
    bioRxiv 2021.09.19.460937; doi: https://doi.org/10.1101/2021.09.19.460937

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