Targeting SARS-CoV-2 Macrodomain-1 to Restore the Innate Immune Response Using In Silico Screening of Medicinal Compounds and Free Energy Calculation Approaches

Abstract

Among the different drug targets of SARS-CoV-2, a multi-domain protein known as NSP3 is a critical element of the translational and replication machinery. The macrodomain-I, in particular, has been reported to have an essential role in the viral attack on the innate immune response. In this study, we explore natural medicinal compounds and identify potential inhibitors to target the SARS-CoV-2-NSP3 macrodomain-I. Computational modeling and simulation tools were utilized to investigate the structural-dynamic properties using triplicates of 100 ns MD simulations. In addition, the MM/GBSA method was used to calculate the total binding free energy of each inhibitor bound to macrodomain-I. Two significant hits were identified: 3,5,7,4'-tetrahydroxyflavanone 3'-(4-hydroxybenzoic acid) and 2-hydroxy-3-O-beta-glucopyranosyl-benzoic acid. The structural-dynamic investigation of both compounds with macrodomain-I revealed stable dynamics and compact behavior. In addition, the total binding free energy for each complex demonstrated a robust binding affinity, of ΔG -61.98 ± 0.9 kcal/mol for Compound A, while for Compound B, the ΔG was -45.125 ± 2.8 kcal/mol, indicating the inhibitory potential of these compounds. In silico bioactivity and dissociation constant (K_D) determination for both complexes further validated the inhibitory potency of each compound. In conclusion, the aforementioned natural products have the potential to inhibit NSP3, to directly rescue the host immune response. The current study provides the basis for novel drug development against SARS-CoV-2 and its variants.

Keywords: NSP3; SARS-CoV-2; computational biology; macrodomain-I; medicinal compounds.

Figure 1

(A) Domain structure of NSP3. (B) Cartoon representation of Mac-I (blue) with the bound ligand shown in stick form with orange carbons. (C) Two-dimensional structure of the drug showing its interaction with Mac-I. Hydrogen bonds are represented by green dashed lines, and pink dashed lines indicate hydrophobic interactions.

Figure 2

Interaction pattern of 2-hydroxy-3-O-beta-glucopyranosyl-benzoic acid (Compound B). (A) Cartoon representation of Mac-I (blue) and the bound ligand shown in stick form with pink carbon atoms. (B) Two-dimensional representation of the drug–Mac-I interactions. Hydrogen bonds are represented by green dashed lines; pink dashed lines indicate hydrophobic interactions.

Figure 3

Interaction pattern of 3,5,7,4′-Tetrahydroxyflavanone 3′-(4-hydroxybenzoic acid) (Compound A). (A) Cartoon representation of Mac-I (blue) and the bound ligand shown in stick form with purple carbon atoms. (B) Two-dimensional representation of the drug–Mac-I interactions. Hydrogen bonds are represented by green dashed lines; pink dashed lines indicate hydrophobic interactions.

Figure 4

Dynamic stability and compactness assessment of ligands bound to Mac-I. (A) RMSD of both the ligands in complex with Mac-I. (B) Structural compactness in a dynamic environment.

Figure 5

H-bonds of Mac-I bound to Compounds A (magenta) and B (fuchsia). The H-bonds were calculated for each run separately.

Figure 6

Residue flexibility assessment of Mac-I interacting with Compounds A and B to elucidate the dynamic environment.

Figure 7

(A) In silico bioactivity results for 3,5,7,4′-tetrahydroxyflavanone 3′-(4-hydroxybenzoic acid)–Mac-I and 2-hydroxy-3-O-beta-glucopyranosyl-benzoic acid–Mac-I complexes. (B) K_D results for 3,5,7,4′ tetrahydroxyflavanone 3′-(4-hydroxybenzoic acid)–Mac-I and 2-hydroxy-3-O-beta-glucopyranosyl-benzoic acid–Mac-I complexes.