Improved prediction of protein-protein interactions using AlphaFold2

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…

• SE
  Swedish e-Science Research Centre
• KO
  Knut och Alice Wallenbergs Stiftelse

  Award: COVID 19
• V
  Vetenskapsrådet

  Awards: 2016-06301, Berzelius-2021-29
• SU
  Stockholms Universitet