Computational modeling of the bat HKU4 coronavirus 3CLpro inhibitors as a tool for the development of antivirals against the emerging Middle East respiratory syndrome (MERS) coronavirus

Abstract

The Middle East respiratory syndrome coronavirus (MERS-CoV) is an emerging virus that poses a major challenge to clinical management. The 3C-like protease (3CL^pro ) is essential for viral replication and thus represents a potential target for antiviral drug development. Presently, very few data are available on MERS-CoV 3CL^pro inhibition by small molecules. We conducted extensive exploration of the pharmacophoric space of a recently identified set of peptidomimetic inhibitors of the bat HKU4-CoV 3CL^pro . HKU4-CoV 3CL^pro shares high sequence identity (81%) with the MERS-CoV enzyme and thus represents a potential surrogate model for anti-MERS drug discovery. We used 2 well-established methods: Quantitative structure-activity relationship (QSAR)-guided modeling and docking-based comparative intermolecular contacts analysis. The established pharmacophore models highlight structural features needed for ligand recognition and revealed important binding-pocket regions involved in 3CL^pro -ligand interactions. The best models were used as 3D queries to screen the National Cancer Institute database for novel nonpeptidomimetic 3CL^pro inhibitors. The identified hits were tested for HKU4-CoV and MERS-CoV 3CL^pro inhibition. Two hits, which share the phenylsulfonamide fragment, showed moderate inhibitory activity against the MERS-CoV 3CL^pro and represent a potential starting point for the development of novel anti-MERS agents. To the best of our knowledge, this is the first pharmacophore modeling study supported by in vitro validation on the MERS-CoV 3CL^pro .

Highlights: MERS-CoV is an emerging virus that is closely related to the bat HKU4-CoV. 3CL^pro is a potential drug target for coronavirus infection. HKU4-CoV 3CL^pro is a useful surrogate model for the identification of MERS-CoV 3CL^pro enzyme inhibitors. dbCICA is a very robust modeling method for hit identification. The phenylsulfonamide scaffold represents a potential starting point for MERS coronavirus 3CL^pro inhibitors development.

Keywords: 3CLpro inhibitors; MERS; coronavirus; dbCICA; pharmacophore modeling.

Figure 1

Comparison of the binding site of 3CL^pro from HKU4‐CoV and MERS‐CoV. (A) A ribbon presentation of the superimposition of the HKU4‐CoV 3CL^pro complex with a potent inhibitor (blue ribbons and green carbon atoms, 1.8 Å, PDB code 4YOI) and the MERS‐CoV enzyme (red ribbons and gray carbon atoms, 2.1 Å, PDB code 4YLU), showing the high similarity in protein folding and a close‐up view of the main residues interacting with inhibitors in HKU4‐CoV and MERS‐CoV 3CL^pro binding pockets. The figure was prepared using the DS visualizer. (B) Amino acid sequence alignment of the 3CL^pro from HKU4‐CoV and MERS‐CoV enzyme. The sequence alignment was generated by using Clustal Omega. Residues strictly conserved have a red background; similar residues are indicated by black bold letters with a yellow background according to a Risler matrix implemented in ESPript. The symbols above the sequence correspond to the secondary structure of MERS‐CoV3CL^pro (PDB code 4YLU; Tomar et al30). The blue stars indicate residues in the binding pocket the enzymes. MERS‐CoV, Middle East respiratory syndrome coronavirus; PDB, Protein Data Bank

Figure 2

Experimental versus predicted bioactivities for the training and testing compounds. Predicted bioactivities calculated using the best QSAR models: (A) Equation (3) and (B) Equation (4). The solid line is the regression line for the fitted and predicted bioactivities of training and test compounds, respectively, whereas the dotted lines indicate arbitrary error margins.

Figure 3

Pharmacophoric features of the QSAR‐guided pharmacophores and the corresponding merged model: green‐vectored spheres: HBA; blue spheres: Hbic; purple‐vectored spheres: HBD; and orange‐vectored spheres: RingArom, (A) Hypo(N‐T1‐1), (B) Hypo(K‐T5‐3), (C) Merged‐Hypo(K‐T5‐3/N‐T1‐1), (D) Refined Merged‐Hypo(K‐T5‐3/N‐T1‐1), and (E) Hypo(L‐T5‐2) fitted against co‐crystallized ligand within HKU4‐CoV 3CL^pro (compound 1, IC₅₀ = 0.33μM, PDB code 4YOI, 1.8 Ǻ). (F) Ligand co‐crystallized within HKU4‐CoV 3CL^pro and the chemical structure of the co‐crystallized ligand. Arrows point to closely positioned common features in Hypo(N‐T1‐1) and Hypo(K‐T5‐3) allowing for merging. The 3D coordinates of these pharmacophores are shown in Table S6. HBA, hydrogen bond acceptor; HBD, hydrogen bond donor

Figure 4

Steps used in the manual generation of binding model Hypo(SB‐1) as guided by dbCICA model SB‐1 (Tables 2 and 3): (A) The binding site moieties selected by dbCICA model SB‐1 with significant contact atoms shown as spheres. (B) The docked pose of the potent training compound 3 (IC₅₀ = 1.2μM) within the binding pocket. (C) The docked poses of the potent compounds 3, 4, 5, 6, and 8. (D) Manually placed pharmacophoric features onto chemical moieties common among docked potent compounds 3, 4, 5, 6, and 8. (E) The docked pose of 3 and how it relates to the proposed pharmacophoric features. (F) Exclusion spheres fitted against binding site atoms showing negative correlations with bioactivity (dbCICA model SB‐1). Green vectored spheres: HBA, blue spheres: Hbic, violet spheres: HbicArom, and orange‐vectored spheres: RingArom. Exclusion spheres are shown in gray. dbCICA, docking‐based comparative intermolecular contacts analysis; HBA, hydrogen bond acceptor

Figure 5

dbCICA pharmacophores derived from successful dbCICA models in Tables 2 and 3. (A) Hypo(SB‐1) mapped against training compounds 5 and 6 (IC₅₀ = 1.5μM and 1.6μM, respectively, (Table S1), (B) Hypo(SB‐2) mapped against 5 and 6, (C) Hypo(SB‐3) fitted against 5, (D) Hypo(SB‐4) mapped against 6, and (E) Hypo(SB‐5) mapped against 5. Green vectored spheres: HBA, purple‐vectored spheres: HBD, blue spheres: Hbic, violet spheres HbicArom, and orange‐vectored spheres: RingArom. Exclusion spheres are shown in gray. dbCICA, docking‐based comparative intermolecular contacts analysis; HBA, hydrogen bond acceptor; HBD, hydrogen bond donor

Figure 6

The chemical structures, inhibitory activities, and apparent IC₅₀ values of the positive control 1, and the 2 tested hits captured by the dbCICA‐derived pharmacophores (222 and 223). dbCICA, docking‐based comparative intermolecular contacts analysis

Figure 7

dbCICA‐based pharmacophores derived from successful dbCICA models (Tables 2 and 3) mapped against hit compound 222. (A) Hypo(SB‐1), (B) Hypo(SB‐2), (C) Hypo(SB‐3), (D) Hypo(SB‐4), and (E) Hypo(SB‐5). Green‐vectored spheres: HBA, purple‐vectored spheres: HBD, blue spheres: Hbic, violet spheres: HbicArom, violet spheres: HbicArom, and orange‐vectored spheres: RingArom. Exclusion spheres are shown in gray. dbCICA, docking‐based comparative intermolecular contacts analysis; HBA, hydrogen bond acceptor; HBD, hydrogen bond donor