Improving de novo protein binder design with deep learning

Recently it has become possible to de novo design high affinity protein binding proteins from target structural information alone. There is, however, considerable room for improvement as the overall design success rate is low. Here, we explore the augmentation of energy-based protein binder design using deep learning. We find that using AlphaFold2 or RoseTTAFold to assess the probability that a designed sequence adopts the designed monomer structure, and the probability that this structure binds the target as designed, increases design success rates nearly 10-fold. We find further that sequence design using ProteinMPNN rather than Rosetta considerably increases computational efficiency.

• HH
  Howard Hughes Medical Institute
• M
  Microsoft
• UO
  University of Texas at Austin
• FW
  Fonds Wetenschappelijk Onderzoek
• VI
  Vlaams Instituut voor Biotechnologie
• VR
  Vlaamse regering
• DA
  Defense Advanced Research Projects Agency

  Awards: HR0011-21-2-0012, HR001120S0052