DDMut: predicting effects of mutations on protein stability using deep learning

Understanding the effects of mutations on protein stability is crucial for variant interpretation and prioritisation, protein engineering, and biotechnology. Despite significant efforts, community assessments of predictive tools have highlighted ongoing limitations, including computational time, low predictive power, and biased predictions towards destabilising mutations. To fill this gap, we developed DDMut, a fast and accurate siamese network to predict changes in Gibbs Free Energy upon single and multiple point mutations, leveraging both forward and hypothetical reverse mutations to account for model anti-symmetry. Deep learning models were built by integrating graph-based representations of the localised…

• AG
  Australian Government
• SG
  State Government of Victoria
• MR
  Medical Research Council
• NH
  National Health and Medical Research Council

  Award: GNT1174405