Identification of potent compounds against SARs-CoV-2: An in-silico based drug searching against Mpro

Abstract

The worldwide pandemic of coronavirus disease 2019 (COVID-19) along with the various newly discovered major SARS-CoV-2 variants, including B.1.1.7, B.1.351, and B.1.1.28, constitute the Variant of Concerns (VOC). It's difficult to keep these variants from spreading over the planet. As a result of these VOCs, the fifth wave has already begun in several countries. The rapid spread of VOCs is posing a serious threat to human civilization. There is currently no specific medicine available for the treatment of COVID-19. Here, we present the findings of methods that used a combination of structure-assisted drug design, virtual screening, and high-throughput screening to swiftly generate lead compounds against Mpro protein of SARs-CoV-2. Therapeutics, in addition to vaccinations, are an essential element of the healthcare response to COVID-19's persistent threat. In the current study, we designed the efficient compounds that may combat all emerging variants of SARs-CoV-2 by targeting the common Mpro protein. The present study was aimed to discover new compounds that may be proposed as new therapeutic agents to treat COVID-19 infection without any adverse effects. For this purpose, a computational-based virtual screening of 352 in-house synthesized compounds library was performed through molecular docking and Molecular Dynamics (MD) simulation approach. As a result, four novel potent compounds were successfully shortlisted by implementing certain pharmacological, physiological, and ADMET criteria i.e., compounds 3, 4, 21, and 22. Furthermore, MD simulations were performed to evaluate the stability and dynamic behavior of these compounds with Mpro complex for about 30 ns. Eventually, compound 22 was found to be highly potent against Mpro protein and was further evaluated by applying 100 ns simulations. Our findings showed that these shortlisted compounds may have potency to treat the COVID-19 infection for which further experimental validation is proposed as part of a follow-up investigation.

Keywords: COVID-19; Molecular docking and simulation; Mpro; Synthetic compounds; Virtual screening.

Graphical abstract

Fig. 1

The 3D structure of Mpro, integrated with inhibitor (brown) embedded in enzyme's catalytic pocket.

Fig. 2

Virtual screening of 352 in-house synthetic compounds against SARS-CoV-II Mpro. It shows that compounds mainly showed binding affinities from −5.8 to −6.11 kcal/mol, −6.38 to −6.92 kcal/mol. Additionally, >200 compounds showed binding energies > than −6.0 kcal/mol.

Fig. 3

Docking studies of compounds showing significant Hydrogen bonds and Hydrophobic interaction in 2D (3A) and 3D (3B) formats of compound 22. Showing potent interaction within the binding cavity of Mpro.

Fig. 4

The correlation analysis of lead compounds docking scores and their predicted Ki, showing the strong correlation with R² = 0.99.

Fig. 5

Molecular dynamics simulation study highlighting Root mean square deviation (RMSD) of protein backbone (A), Root Mean Square Fluctuation of proteins in complex with shortlisted four compounds after simulations (B), Radius of gyration of proteins (Rg) showing the protein stability during Mpro-inhibitor interaction (C). i.e., Compound 3 (black), 4 (red), 22 (green), 21 (blue), Co-crystalized ligand (yellow).

Fig. 6

Hydrogen bonds mediated by ligand 22 with MPro of CoV-II. Significant atom types shown involve in mediation of H-bond with the amino acids of binding pocket of MPro for the 30 ns simulation, showing the potential interaction of compound 22 with enzyme.

Fig. 7

Molecular dynamics simulation of Compound 22 (A), Root mean square deviation (RMSD) of apo protein (black) and ligand bounded protein (red) within the binding pocket of Mpro. It shows the significant differences in the binding pattern of compound 22 with Mpro i.e., a fluctuation can be observed after the ligand bound to Mpro (Red) (B), Root Mean Square Fluctuation of proteins amino acid residues throughout 200 ns simulations showing stability of protein and compound with mild fluctuations (C), the RMSF analysis of Apo protein (Black) and Mpro-compound 22 complex (red) showing the clear fluctuations in the ligand bounded protein amino acids, as a significant peaks of active amino acids can be observed from 100 to 200 range of amino acids i.e., Asn142, Gly143, Ser144, Cys145, Glu166, and Gln189. (D) Radius of gyration (Rg) of proteins (Black) and ligand bounded protein (Red) indicating protein stability upon inhibitor interaction with Mpro. A significant compactness of protein can be observed after the compound 22 occupied the binding pocket of Mpro with a significant decrease in Rg can be observed in range of 2.15 nm compared to 2.25 nm of Apo protein (E), and persistent hydrogen bonds mediation of compound 22 with the significant amino acids of binding pocket of Mpro such as Asn142, Gly143, Ser144, Cys145 respectively. The results of hydrogen bonds are further supported by the RMSF fluctuation observed (in Fig. 7C) within the binding pocket of Mpro, required for the anchoring and catalysis of substrate.

Fig. 8

Binding free energy calculation for compound 22 via MMGBSA Eq (1), showing binding free energy as −28 kcal/mol within the binding cavity of Mpro for last 100 frames of 200 ns simulation.