AlphaFold prediction of structural ensembles of disordered proteins

Deep learning methods of predicting protein structures have reached an accuracy comparable to that of high-resolution experimental methods. It is thus possible to generate accurate models of the native states of hundreds of millions of proteins. An open question, however, concerns whether these advances can be translated to disordered proteins, which should be represented as structural ensembles because of their heterogeneous and dynamical nature. To address this problem, we introduce the AlphaFold-Metainference method to use AlphaFold-derived distances as structural restraints in molecular dynamics simulations to construct structural ensembles of ordered and disordered proteins. The results obtained using…

• UR
  UK Research and Innovation

  Awards: 10061100, 10059436
• BF
  Bodossaki Foundation
• HE
  HORIZON EUROPE Framework Programme