Login / Signup

Discovery of Novel Gain-of-Function Mutations Guided by Structure-Based Deep Learning.

Raghav ShroffAustin W ColeDaniel J DiazBarrett R MorrowIsaac DonnellAnkur AnnapareddyJimmy GolliharAndrew D EllingtonRoss Thyer
Published in: ACS synthetic biology (2020)
Despite the promise of deep learning accelerated protein engineering, examples of such improved proteins are scarce. Here we report that a 3D convolutional neural network trained to associate amino acids with neighboring chemical microenvironments can guide identification of novel gain-of-function mutations that are not predicted by energetics-based approaches. Amalgamation of these mutations improved protein function in vivo across three diverse proteins by at least 5-fold. Furthermore, this model provides a means to interrogate the chemical space within protein microenvironments and identify specific chemical interactions that contribute to the gain-of-function phenotypes resulting from individual mutations.
Keyphrases
  • deep learning
  • convolutional neural network
  • amino acid
  • protein protein
  • small molecule
  • artificial intelligence
  • resistance training
  • single cell