Automatic recognition of ligands in electron density by machine learning.

Journal: Bioinformatics (Oxford, England)
Published Date: Feb 1, 2019

Abstract

MOTIVATION: The correct identification of ligands in crystal structures of protein complexes is the cornerstone of structure-guided drug design. However, cognitive bias can sometimes mislead investigators into modeling fictitious compounds without solid support from the electron density maps. Ligand identification can be aided by automatic methods, but existing approaches are based on time-consuming iterative fitting.

Authors

  • Marcin Kowiel
    Center for Biocrystallographic Research, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland.
  • Dariusz Brzezinski
    Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA, USA.
  • Przemyslaw J Porebski
    Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA, USA.
  • Ivan G Shabalin
    Department of Molecular Physiology and Biological Physics, University of Virginia, 1340 Jefferson Park Avenue, Charlottesville, VA, 22908, USA.
  • Mariusz Jaskolski
    Center for Biocrystallographic Research, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland.
  • Wladek Minor
    Department of Molecular Physiology and Biological Physics, University of Virginia, 1340 Jefferson Park Avenue, Charlottesville, VA, 22908, USA. wladek@iwonka.med.virginia.edu.

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