InDeep: 3D fully convolutional neural networks to assist in silico drug design on protein-protein interactions.

Journal: Bioinformatics (Oxford, England)
Published Date:

Abstract

MOTIVATION: Protein-protein interactions (PPIs) are key elements in numerous biological pathways and the subject of a growing number of drug discovery projects including against infectious diseases. Designing drugs on PPI targets remains a difficult task and requires extensive efforts to qualify a given interaction as an eligible target. To this end, besides the evident need to determine the role of PPIs in disease-associated pathways and their experimental characterization as therapeutics targets, prediction of their capacity to be bound by other protein partners or modulated by future drugs is of primary importance.

Authors

  • Vincent Mallet
    Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur, Université de Paris, CNRS UMR3528, Paris F-75015, France.
  • Luis Checa Ruano
    Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur, Université de Paris, CNRS UMR3528, Paris F-75015, France.
  • Alexandra Moine Franel
    Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur, Université de Paris, CNRS UMR3528, Paris F-75015, France.
  • Michael Nilges
    Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur, Université de Paris, CNRS UMR3528, Paris F-75015, France.
  • Karen Druart
    Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur, Université de Paris, CNRS UMR3528, Paris F-75015, France.
  • Guillaume Bouvier
    Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur, Université de Paris, CNRS UMR3528, Paris F-75015, France.
  • Olivier Sperandio
    Inserm U973, Université Paris Diderot, Paris, France.