Drugs Repurposing Using QSAR, Docking and Molecular Dynamics for Possible Inhibitors of the SARS-CoV-2 Mpro Protease

Abstract

Wuhan, China was the epicenter of the first zoonotic transmission of the severe acute respiratory syndrome coronavirus clade 2 (SARS-CoV-2) in December 2019 and it is the causative agent of the novel human coronavirus disease 2019 (COVID-19). Almost from the beginning of the COVID-19 outbreak several attempts were made to predict possible drugs capable of inhibiting the virus replication. In the present work a drug repurposing study is performed to identify potential SARS-CoV-2 protease inhibitors. We created a Quantitative Structure-Activity Relationship (QSAR) model based on a machine learning strategy using hundreds of inhibitor molecules of the main protease (M^pro) of the SARS-CoV coronavirus. The QSAR model was used for virtual screening of a large list of drugs from the DrugBank database. The best 20 candidates were then evaluated in-silico against the M^pro of SARS-CoV-2 by using docking and molecular dynamics analyses. Docking was done by using the Gold software, and the free energies of binding were predicted with the MM-PBSA method as implemented in AMBER. Our results indicate that levothyroxine, amobarbital and ABP-700 are the best potential inhibitors of the SARS-CoV-2 virus through their binding to the M^pro enzyme. Five other compounds showed also a negative but small free energy of binding: nikethamide, nifurtimox, rebimastat, apomine and rebastinib.

Keywords: COVID-19; QSAR; SARS-CoV-2; drugs repurposing; molecular dynamics.

Figure 1

(A). AUROC plot defined by True Positive Rate (TPR) vs. False Positive Rate (FPR) (Sensitivity vs. Specificity) for our model (GraphConvModel) compared to a No Skill or random model. (B) PRC-AUC plot defined by Precision vs. Recall for our model (GraphConvModel) compared with a No Skill or random model.

Figure 2

Two PCA components of the hidden features extracted by the GraphConvModel for training, test and prediction subsets. Green and blue marks correspond to the molecules used in training and test datasets for QSAR model construction. Purple marks correspond with all molecules used in virtual screening (DrugBank database) and yellow marks indicates the location of the best candidates obtained after molecular dynamic simulation: levothyroxine, amobarbital and ABP-700.

Figure 3

Estimated free energies of binding of the potential SARS-CoV-2 M^pro inhibitors. Levothyroxine (DB01583(2)), Amobarbital (DB01351), ABP-700 (DB15411), Nikethamide (DB13655), Nifurtimox (DB11820), Rebimastat (DB06573), Apomine (DB12276), Rebastinib (DB13005), Aleplasinin (DB12635), Isoflurophate (DB00677), liothyronine (DB01583(1)), Marimastat (DB00789), Niceritrol (DB13441), Telinavir (DB12178), Inositol nicotinate (DB08949), Aluminium nicotinate (DB13576) and Ortataxel (DB11669). The energies computed for the crystallographic complexes (PDB IDs 6LZE, 6M0K, 6Y2G and 7BUY) are marked with asterisks (*).

Figure 4

Predicted binding modes of Levothyroxine (A), Amobarbital (B) and ABP-700 (C) to the SARS-CoV-2 Mpro enzyme. The predicted hydrogen bonds between the ligands and the receptor are depicted using an all-atoms representation of the M^pro residues and dashed lines connecting them to the interacting ligand atoms. The color scheme is: black for carbon, red for oxygen, blue for nitrogen, yellow for sulfur and green for iodine.