Geometric potentials from deep learning improve prediction of CDR H3 loop structures.

Journal: Bioinformatics (Oxford, England)
Published Date: Jul 1, 2020

Abstract

MOTIVATION: Antibody structure is largely conserved, except for a complementarity-determining region featuring six variable loops. Five of these loops adopt canonical folds which can typically be predicted with existing methods, while the remaining loop (CDR H3) remains a challenge due to its highly diverse set of observed conformations. In recent years, deep neural networks have proven to be effective at capturing the complex patterns of protein structure. This work proposes DeepH3, a deep residual neural network that learns to predict inter-residue distances and orientations from antibody heavy and light chain sequence. The output of DeepH3 is a set of probability distributions over distances and orientation angles between pairs of residues. These distributions are converted to geometric potentials and used to discriminate between decoy structures produced by RosettaAntibody and predict new CDR H3 loop structures de novo.

Authors

  • Jeffrey A Ruffolo
    Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, MD 21218, USA.
  • Carlos Guerra
    Department of Computer Science, George Mason University, Fairfax, VA 22030, USA.
  • Sai Pooja Mahajan
    Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA.
  • Jeremias Sulam
    Johns Hopkins University.
  • Jeffrey J Gray
    Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, MD 21218, USA.

Keywords

External Resources

Popular Topics
Recent Journals