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Improved prediction of protein-protein interactions using AlphaFold2

Author

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  • Patrick Bryant

    (Science for Life Laboratory
    Stockholm University)

  • Gabriele Pozzati

    (Science for Life Laboratory
    Stockholm University)

  • Arne Elofsson

    (Science for Life Laboratory
    Stockholm University)

Abstract

Predicting the structure of interacting protein chains is a fundamental step towards understanding protein function. Unfortunately, no computational method can produce accurate structures of protein complexes. AlphaFold2, has shown unprecedented levels of accuracy in modelling single chain protein structures. Here, we apply AlphaFold2 for the prediction of heterodimeric protein complexes. We find that the AlphaFold2 protocol together with optimised multiple sequence alignments, generate models with acceptable quality (DockQ ≥ 0.23) for 63% of the dimers. From the predicted interfaces we create a simple function to predict the DockQ score which distinguishes acceptable from incorrect models as well as interacting from non-interacting proteins with state-of-art accuracy. We find that, using the predicted DockQ scores, we can identify 51% of all interacting pairs at 1% FPR.

Suggested Citation

  • Patrick Bryant & Gabriele Pozzati & Arne Elofsson, 2022. "Improved prediction of protein-protein interactions using AlphaFold2," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28865-w
    DOI: 10.1038/s41467-022-28865-w
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