Analysis of therapeutic targets for SARS-CoV-2 and discovery of potential drugs by computational methods

Abstract

SARS-CoV-2 has caused tens of thousands of infections and more than one thousand deaths. There are currently no registered therapies for treating coronavirus infections. Because of time consuming process of new drug development, drug repositioning may be the only solution to the epidemic of sudden infectious diseases. We systematically analyzed all the proteins encoded by SARS-CoV-2 genes, compared them with proteins from other coronaviruses, predicted their structures, and built 19 structures that could be done by homology modeling. By performing target-based virtual ligand screening, a total of 21 targets (including two human targets) were screened against compound libraries including ZINC drug database and our own database of natural products. Structure and screening results of important targets such as 3-chymotrypsin-like protease (3CLpro), Spike, RNA-dependent RNA polymerase (RdRp), and papain like protease (PLpro) were discussed in detail. In addition, a database of 78 commonly used anti-viral drugs including those currently on the market and undergoing clinical trials for SARS-CoV-2 was constructed. Possible targets of these compounds and potential drugs acting on a certain target were predicted. This study will provide new lead compounds and targets for further in vitro and in vivo studies of SARS-CoV-2, new insights for those drugs currently ongoing clinical studies, and also possible new strategies for drug repositioning to treat SARS-CoV-2 infections.

Keywords: 3CLpro, 3-chymotrypsin-like protease; Drug repurposing; E, envelope; Homology modeling; M, membrane protein; Molecular docking; N, nucleocapsid protein; Nsp, non-structure protein; ORF, open reading frame; PDB, protein data bank; RdRp, RNA-Dependence RNA polymerase; Remdesivir; S, Spike; SARS-CoV-2; SUD, SARS unique domain; UB, ubiquitin-like domain.

Graphical abstract

Figure 1

Phylogenetic analysis and sequence alignment of 22 coronaviruses from different hosts. (A) Homology tree of 22 nucleotide sequences alignment. The homology of each branch is marked in red. (B) The evolutionary history was inferred using the Neighbor-Joining method. The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (500 replicates) is shown above the branches.

Figure 2

Overall genome and protein analysis of SARS-CoV-2. (A) Genome of SARS-CoV-2. (B) The large replicase polyprotein PP1ab. The purple rectangle represents the replicase polyprotein PP1ab which contains 7096 amino acids. The position of red triangle means cleavage site of PLpro, and position of yellow triangle means the site of 3CLpro, the numbers below the purple rectangle represent the residues that bound the individual proteins or domains. Orange rectangle below the individual proteins or domains means exactly homology 3D structure were detected. (C) Four structure proteins are represented in the cartoon pattern of coronavirus.

Figure 3

Sequence alignment of the proteins from SARS-CoV-2 with other CoVs. (A) Sequence alignment of PLpro, 3CLpro, RdRp among SARS-CoV, SARS-CoV-2 and MERS CoV. (B) The computational structure of SARS-CoV-2 3CLpro (shown as pink cartoon) modeled from SARS 3CLpro (3WA0) aligned with crystal structure of SARS-CoV-2 3CLpro (6LU7) (shown in blue cartoon). The computational model of the of SARS-CoV-2 3CLpro showed a Cα RMSD of 0.471 Å on the overall structure compared to the SARS-CoV-2 3CLpro structure, and that showed a Cα RMSD of 0.126 Å on the substrate binding region. (C) Sequence alignment of spike protein receptor binding domain among SARS-CoV, bat-CoVRaTG13 and SARS-CoV-2. “▵” represents residues previously identified as importantly interact with human ACE2 from SARS. Black arrow marked amino acids that are different between SARS-CoV-2 and bat-CoVRaTG13.

Figure 4

Low-energy binding conformations of ribavirin bound to PLpro generated by molecular docking. (A) Superimposing of 2019-nCoV PLpro (yellow) with the crystal structure of SARS PLpro (orange) (PDB code 3e9s). (B) Detailed view of rivavirin binding in the active site of the enzyme (Supporting PDB file SARS_CoV-2_PLpro_homo_Ribavirin.pdb).

Figure 5

Low-energy binding conformations of Montelukast bound to 3CLpro generated by molecular docking. (A) Montelukast was fitted well in the active pocket of SARS-CoV-2 3CLpro, 3CLpro was shown as electrostatic surface model. (B) Detailed view of montelukastin binding in the active pocket of 3CLpro (Supporting PDB file SARS_CoV-2_3CLpro_homo_Montelukast.pdb).

Figure 6

Low-energy binding conformation of hesperidin bound to Spike RBD generated by molecular docking. (A) Hesperidinwas fitted into the shallow pocket in the surface of SARS-CoV-2 Spike RBD. (B) Detailed view of hesperidinbinding in the pocket of Spike RBD. (C) Hesperidin blocks the interface of ACE2 and Spike RBD binding (Supporting PDB file SARS_CoV-2 _Spike_RBD_homo_Hesperidin.pdb).

Figure 7

Low-energy binding conformation of remdesivir bound to SARS-CoV-2 RdRp generated by molecular docking. (A) Remdesivir was fitted in the bottom of the RNA template channel (top view). (B) Remdesivir was fitted in the bottom of the RNA template channel (bottom view). (C) Detailed view of remdesivir binding with RdRp (Supporting PDB file SARS_CoV-2_RdRp_homo_Remdesivir.pdb).

Figure 8

Low-energy binding conformation of remdesivir bound to human TMPRSS2 generated by molecular docking. (A) Remdesivir was fitted in an allosteric pocket far away from the enzyme active center (bottom). (B) Detailed view of remdesivir binding with TMPRSS2 (Supporting PDB file HS_TMPRSS2_homo_Remdesivir.pdb).