AF2Complex predicts direct physical interactions in multimeric proteins with deep learning

Accurate descriptions of protein-protein interactions are essential for understanding biological systems. Remarkably accurate atomic structures have been recently computed for individual proteins by AlphaFold2 (AF2). Here, we demonstrate that the same neural network models from AF2 developed for single protein sequences can be adapted to predict the structures of multimeric protein complexes without retraining. In contrast to common approaches, our method, AF2Complex, does not require paired multiple sequence alignments. It achieves higher accuracy than some complex protein-protein docking strategies and provides a significant improvement over AF-Multimer, a development of AlphaFold for multimeric proteins.…

• UD
  U.S. Department of Energy

  Award: DE-SC0021303
• GI
  Georgia Institute of Technology
• NI
  National Institutes of Health

  Award: R35GM118039
• OO
  Office of Science
• NI
  National Institute of General Medical Sciences

  Awards: R35GM118039, NIH R35GM118039
• AS
  Advanced Scientific Computing Research
• BA
  Biological and Environmental Research

  Award: DOE DE-SC0021303