GraphQA: protein model quality assessment using graph convolutional networks.

Journal: Bioinformatics (Oxford, England)
Published Date:

Abstract

MOTIVATION: Proteins are ubiquitous molecules whose function in biological processes is determined by their 3D structure. Experimental identification of a protein's structure can be time-consuming, prohibitively expensive and not always possible. Alternatively, protein folding can be modeled using computational methods, which however are not guaranteed to always produce optimal results. GraphQA is a graph-based method to estimate the quality of protein models, that possesses favorable properties such as representation learning, explicit modeling of both sequential and 3D structure, geometric invariance and computational efficiency.

Authors

  • Federico Baldassarre
    Division of Robotics, Perception and Learning (RPL), KTH - Royal Institute of Technology, 10044 Stockholm, Sweden.
  • David Menéndez Hurtado
    Department of Biochemistry and Biophysics and Science for Life Laboratory, Stockholm University, Solna, Sweden.
  • Arne Elofsson
    Science for Life Laboratory and Department of Biochemistry and Biophysics, Stockholm University, Stockholm 10691, Sweden arne@bioinfo.se debbie@hms.harvard.edu cccsander@gmail.com.
  • Hossein Azizpour
    School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm, Sweden; Science for Life Laboratory, Stockholm, Sweden.