Predicting molecular docking of per- and polyfluoroalkyl substances to blood protein using generative artificial intelligence algorithm DiffDock.

Journal: BioTechniques
PMID: 37947020

Abstract

This study computationally evaluates the molecular docking affinity of various perfluoroalkyl and polyfluoroalkyl substances (PFAs) towards blood proteins using a generative machine-learning algorithm, DiffDock, specialized in protein-ligand blind-docking learning and prediction. Concerns about the chemical pathways and accumulation of PFAs in the environment and eventually in the human body has been rising due to empirical findings that levels of PFAs in human blood has been rising. DiffDock may offer a fast approach in determining the fate and potential molecular pathways of PFAs in human body.

Authors

  • Dhan Lord B Fortela
    Department of Chemical Engineering, University of Louisiana, Lafayette, LA 70504, USA.
  • Ashley P Mikolajczyk
    Department of Chemical Engineering, University of Louisiana, Lafayette, LA 70504, USA.
  • Miranda R Carnes
    Department of Chemical Engineering, University of Louisiana, Lafayette, LA 70504, USA.
  • Wayne Sharp
    Energy Institute of Louisiana, University of Louisiana, Lafayette, LA 70504, USA.
  • Emmanuel Revellame
    Department of Chemical Engineering, University of Louisiana, Lafayette, LA 70504, USA.
  • Rafael Hernandez
    Department of Chemical Engineering, University of Louisiana, Lafayette, LA 70504, USA.
  • William E Holmes
    Department of Chemical Engineering, University of Louisiana, Lafayette, LA 70504, USA.
  • Mark E Zappi
    Department of Civil Engineering, University of Louisiana at Lafayette, P. O. Box 43598, Lafayette, LA, 70504, USA; Center of Environmental Technology, The Energy Institute of Louisiana, University of Louisiana at Lafayette, P. O. Box 43597, Lafayette, LA, 70504, USA; Department of Chemical Engineering, University of Louisiana at Lafayette, P. O. Box 43675, Lafayette, LA, 70504, USA.

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