Ligand identification in CryoEM and X-ray maps using deep learning.

Journal: Bioinformatics (Oxford, England)
PMID: 39700427

Abstract

MOTIVATION: Accurately identifying ligands plays a crucial role in the process of structure-guided drug design. Based on density maps from X-ray diffraction or cryogenic-sample electron microscopy (cryoEM), scientists verify whether small-molecule ligands bind to active sites of interest. However, the interpretation of density maps is challenging, and cognitive bias can sometimes mislead investigators into modeling fictitious compounds. Ligand identification can be aided by automatic methods, but existing approaches are available only for X-ray diffraction and are based on iterative fitting or feature-engineered machine learning rather than end-to-end deep learning.

Authors

  • Jacek Karolczak
    Institute of Computing Science, Poznan University of Technology, Poznan 60-965, Poland.
  • Anna Przybyłowska
    Institute of Computing Science, Poznan University of Technology, Poznan 60-965, Poland.
  • Konrad Szewczyk
    Institute of Computing Science, Poznan University of Technology, Poznan 60-965, Poland.
  • Witold Taisner
    Institute of Computing Science, Poznan University of Technology, Poznan 60-965, Poland.
  • John M Heumann
    Department of Molecular, Cellular and Developmental Biology, University of Colorado Boulder, Boulder, CO 80309, United States.
  • Michael H B Stowell
    Department of Molecular, Cellular and Developmental Biology, University of Colorado Boulder, Boulder, CO 80309, United States.
  • Michał Nowicki
    Institute of Robotics and Machine Intelligence, Poznan University of Technology, Poznan 60-965, Poland.
  • Dariusz Brzezinski
    Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA, USA.

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