Protein interface prediction using graph convolutional networks

Proteins play a critical role in processes both within and between cells, through their interactions with each other and other molecules. Proteins interact via an interface forming a protein complex, which is difficult, expensive, and time consuming to determine experimentally, giving rise to computational approaches. These computational approaches utilize known electrochemical properties of protein amino acid residues in order to predict if they are a part of an interface or not. Prediction can occur in a partner independent fashion, where amino acid residues are considered independently of their neighbor, or in a partner specific fashion, where pairs of potentially interacting residues are considered…

• UO
  University of California, San Francisco

  Award: P41-GM103311
• NI
  National Institute of General Medical Sciences

  Awards: GM103311, P41-GM103311