Title:Protein-Ligand Docking Simulations with AutoDock4 Focused on the Main Protease of SARS-CoV-2
Volume: 28
Issue: 37
关键词:
COVID-19、SARS-CoV-2、蛋白质-配体相互作用、autoDock4、对接、机器学习、主蛋白酶。
摘要:
Background: The main protease of SARS-CoV-2 (Mpro) is one of the targets
identified in SARS-CoV-2, the causative agent of COVID-19. The application of X-ray
diffraction crystallography made available the three-dimensional structure of this protein
target in complex with ligands, which paved the way for docking studies.
Objective: Our goal here is to review recent efforts in the application of docking simulations
to identify inhibitors of the Mpro using the program AutoDock4.
Methods: We searched PubMed to identify studies that applied AutoDock4 for docking
against this protein target. We used the structures available for Mpro to analyze intermolecular
interactions and reviewed the methods used to search for inhibitors.
Results: The application of docking against the structures available for the Mpro found ligands
with an estimated inhibition in the nanomolar range. Such computational approaches
focused on the crystal structures revealed potential inhibitors of Mpro that might exhibit
pharmacological activity against SARS-CoV-2. Nevertheless, most of these studies lack
the proper validation of the docking protocol. Also, they all ignored the potential use of
machine learning to predict affinity.
Conclusion: The combination of structural data with computational approaches opened
the possibility to accelerate the search for drugs to treat COVID-19. Several studies used
AutoDock4 to search for inhibitors of Mpro. Most of them did not employ a validated
docking protocol, which lends support to critics of their computational methodology. Furthermore,
one of these studies reported the binding of chloroquine and hydroxychloroquine
to Mpro. This study ignores the scientific evidence against the use of these antimalarial
drugs to treat COVID-19.
