Toward the Discovery of SARS-CoV-2 Main Protease Inhibitors: Exploring Therapeutic Potentials of Evodiamine and Its Derivatives,Virtual Screening,Molecular Docking,and Molecular Dynamic Studies

Abstract

Continuous scientific research is necessary to help in the discovery of new promising remedies for the treatment of COVID-19, caused by the SARS-CoV-2 virus. This current research was aimed at identifying potential novel inhibitors of the SARS-CoV-2 main protease, which represents one of the most important targets in the viral life cycle. Protein data bank file ID: 7JQ2 was used containing the co-crystallized inhibitor MPI5 with the Main protease. A virtual screening process for natural evodiamine compounds was performed through absorption, distribution, metabolism, elimination, and toxicity studies, and the promising hits were docked into the binding site of the enzyme. 13-(4-Chlorobenzoyl)-10-hydroxy-14-methyl-8,13,13b,14-tetrahydroindolo[2′,3′:3,4]pyrido[2,1-b]-quinazolin-5(7H)-one (29) interacted favorably with the enzyme; it showed high similarity to MPI5. Molecular dynamic simulations for 29 proved the stability of its binding to SARS-CoV-2 protease over 100 ns; subsequent MMGBSA analysis also supported this principle. Furthermore, 29 elucidated higher limiting action on enzymatic behavior throughout the whole process when compared to MPI5. This provides sufficient evidence for the potential of evodiamine compounds in modern antiviral research, especially compound 29, against the modern COVID-19 pandemic.

Keywords

COVID-19 natural products evodiamines main protease docking dynamics heterocyclic molecules

Introduction

Evodia (Rutaceae) species are widely spread throughout Asia, Oceania, and eastern Africa, with twenty species and five variations described in the literature.¹ Among them is Evodia rutaecarpa Benth. (Wu-Chu-Yu), the dried unripe fruit of which is extensively used in traditional Chinese medicine for treating multiple gastrointestinal disorders such as headache, abdominal pain, and hemorrhage.² Moreover, it is even used as a cardiotonic due to its positive inotropic and chronotropic effects.³ Phytochemical analysis of the extract revealed the alkaloids evodiamine (A), rutaecarpine (B), evodiamine (A), and rutaecarpine (B), which exhibited anti-inflammatory potential through either inhibition of cyclo-oxygenase 2 (COX-2) gene expressions or direct inhibition of COX-2, respectively. Furthermore, evodiamine (A) has been proven to prevent tumor cell proliferation, invasion, and metastasis while causing minimal harm to normal human peripheral blood cells. Its anticancer properties were observed through the stabilization of the complex formed between DNA and topoisomerase I, as well as the inhibition of heat shock protein (HSP) 27.^4,5

The therapeutic potential of E rutaecarpa encourages its screening against other targets. This concept led us to elucidate its antiviral properties against several viral targets such as the recent corona virus (SARS-CoV-2) outbreak of 2019, which has claimed the lives of millions worldwide.⁶ Inspired by the findings that suggest the effectiveness of favipiravir against SARS-CoV-2 RNA polymerase,^7,8 previously reported research work has demonstrated the antiviral capabilities of several phytochemicals⁹ such as evodiamine (A) to inhibit replication of the influenza A virus,¹⁰ as well as the potential of its in vitro autophagy to extend its scope further toward SARS-CoV-2 infections.¹¹ Therefore, taking into consideration the importance of proteases for viral survival and replication,¹² we decided to study the impact of evodiamine (A) and its derivatives on SARS-CoV-2 viral protease through various computational techniques. The investigated natural derivatives are shown below.

Methodology

Absorption, Distribution, Metabolism, Elimination, and Toxicity

The absorption, distribution, metabolism, elimination, and toxicity (ADMET) of the selected compounds were determined using Discovery studio 2.5 ADMET descriptor protocol.¹³ All compounds were drawn using the built-in tool and then saved for various analyses.^14,15

Molecular Docking

Molecular docking simulations were performed via MOE 2019 software. The receptor was downloaded from the protein data bank (PDB ID: 7JQ2).¹⁶ The receptor and compounds were prepared and optimized using the standard structure optimization protocol of the software “QuickPrep,” and then compounds were compiled into a database. Afterward, both receptor and the ligands were prepared using the standard structure optimization protocol of the software. Energy minimization was performed using AMBER12: EHT force field and the active site was set to the pocket surrounding the co-crystalized ligands. The docking was performed using the MOE DOCKTITE Wizard-induced fit protocol using the GBVI/WSA dG scoring function.¹⁷ Validation was achieved through re-docking of the co-crystalized ligand into the binding pocket and calculation of root mean square deviation (RMSD) between poses. Biovia DS Visualizer was used to analyze the docking results as well.^18,19

Molecular Dynamics and Molecular Mechanics-Generalized Born Surface Area Calculations

The Schrödinger Desmond package was used for molecular dynamics simulations of free protein and complexes of protein with MPI5 and 29.²⁰ Preparation was performed using the “OPLS4” forcefield, as described before. The system was constructed using “TIP3P” water molecules in an orthorhombic box.²¹ The systems underwent simulation under “NPT” ensemble (300 K and 1 bar) for 100 ns for each one, then followed by interaction analysis to calculate RMSD, root mean square fluctuation (RMSF), and other properties.²²

The molecular mechanics-generalized born surface area (MM-GBSA) technique was used to compute the binding free energy of the protein–ligand complexes studied, which integrated MM force fields with a Generalized Born and Surface Area continuum solvation model using the Schrodinger Prime package.^23,24

Results and Discussion

ADMET Study

The predicted pharmacokinetic properties of the compounds showed that most of them possess good intestinal absorption, despite their poor solubility (Figure 1). Furthermore, the blood–brain barrier was permeable to nearly all of the compounds, except 29. That gives a good indication for this compound has less CNS side effects. Finally, the compounds showed no potential interaction with cytochrome P2D6, except for 5, 6, 11, 12, 16, 19, 20, and 31 (Table 1).

Figure 1.

(A) Predicted ADMET absorption chart. (B) Statistical analysis of ADMET properties.

Table 1.

ADME Properties for the Selected Natural Evodiamine Compounds.

Code	Compound	Solubility	Absorption	BBB	CYP2D6	Log p_98	PSA_98
1	14-Methyl-8,14-dihydroindolo[2′,3′:3,4]pyrido[2,1-b]quinazolin-5(7H)-one	2	0	1	False	2.51	35.33
2	14-Methyl-5-oxo-7,8-dihydro-5H-indolo[2′,3′:3,4]pyrido[2,1-b]quinazolin-14-ium-13-ide	2	0	1	False	3.43	31.91
3	Sodium 2-(14-methyl-5-oxo-7,8,13b,14-tetrahydroindolo[2′,3′:3,4]pyrido[2,1-b]quinazolin-13(5H)-yl)acetate	2	0	2	False	3.30	55.58
4	Sodium 4-(14-methyl-5-oxo-7,8,13b,14-tetrahydroindolo[2′,3′:3,4]pyrido[2,1-b]quinazolin-13(5H)-yl)butanoate	2	0	1	False	3.87	55.58
5	14-Ethyl-8,13,13b,14-tetrahydroindolo[2′,3′:3,4]pyrido[2,1-b]quinazolin-5(7H)-one	1	0	1	True	4.02	39.06
6	14-Propyl-8,13,13b,14-tetrahydroindolo[2′,3′:3,4]pyrido[2,1-b]quinazolin-5(7H)-one	1	0	1	True	4.55	39.06
7	13b-Hydroxy-14-methyl-8,13,13b,14-tetrahydroindolo[2′,3′:3,4]pyrido[2,1-b]quinazolin-5(7H)-one	2	0	1	False	3.86	59.88
8	2-(14-Methyl-5-oxo-7,8,13b,14-tetrahydroindolo[2′,3′:3,4]pyrido[2,1-b]quinazolin-13(5H)-yl)acetic acid	2	0	2	False	3.36	67.47
9	Ethyl 14-methyl-5-oxo-7,8,13b,14-tetrahydroindolo[2′,3′:3,4]pyrido[2,1-b]quinazoline-13(5H)-carboxylate	1	0	1	False	4.66	55.58
10	13-Benzoyl-14-methyl-8,13,13b,14-tetrahydroindolo[2′,3′:3,4]pyrido[2,1-b]quinazolin-5(7H)-one	1	0	0	False	5.33	46.65
11	13-(3-Chlorophenyl)-14-methyl-8,13,13b,14-tetrahydroindolo[2′,3′:3,4]pyrido[2,1-b]quinazolin-5(7H)-one	0	1	0	False	6.12	29.35
12	13-Hexyl-14-methyl-8,13,13b,14-tetrahydroindolo[2′,3′:3,4]pyrido[2,1-b]quinazolin-5(7H)-one	1	1	0	True	6.12	29.35
13	13-Benzyl-14-methyl-8,13,13b,14-tetrahydroindolo[2′,3′:3,4]pyrido[2,1-b]quinazolin-5(7H)-one	1	0	0	False	5.46	29.35
14	2-(14-Methyl-5-oxo-7,8,13b,14-tetrahydroindolo[2′,3′:3,4]pyrido[2,1-b]quinazolin-13(5H)-yl)acetonitrile	2	0	1	False	3.61	52.29
15	Ethyl 2-(14-methyl-5-oxo-7,8,13b,14-tetrahydroindolo[2′,3′:3,4]pyrido[2,1-b]quinazolin-13(5H)-yl)acetate	2	0	1	False	3.93	55.58
16	13-(4-Chlorobenzyl)-14-methyl-8,13,13b,14-tetrahydroindolo[2′,3′:3,4]pyrido[2,1-b]quinazolin-5(7H)-one	0	1	0	True	6.13	29.35
17	13-Butyryl-14-methyl-8,13,13b,14-tetrahydroindolo[2′,3′:3,4]pyrido[2,1-b]quinazolin-5(7H)-one	1	0	1	False	4.79	46.65
18	2-Methoxy-14-methyl-8,13,13b,14-tetrahydroindolo[2′,3′:3,4]pyrido[2,1-b]quinazolin-5(7H)-one	2	0	1	False	3.66	47.99
19	2,3-Dimethoxy-14-methyl-8,13,13b,14-tetrahydroindolo[2′,3′:3,4]pyrido[2,1-b]quinazolin-5(7H)-one	2	0	1	True	3.64	56.92
20	3-Fluoro-14-methyl-8,13,13b,14-tetrahydroindolo[2′,3′:3,4]pyrido[2,1-b]quinazolin-5(7H)-one	1	0	1	True	3.88	39.06
21	3-Chloro-14-methyl-8,13,13b,14-tetrahydroindolo[2′,3′:3,4]pyrido[2,1-b]quinazolin-5(7H)-one	1	0	1	False	4.34	39.06
22	3-Hydroxy-14-methyl-8,13,13b,14-tetrahydroindolo[2′,3′:3,4]pyrido[2,1-b]quinazolin-5(7H)-one	2	0	2	False	3.43	59.88
23	3-Methoxy-14-methyl-8,13,13b,14-tetrahydroindolo[2′,3′:3,4]pyrido[2,1-b]quinazolin-5(7H)-one	2	0	1	False	3.66	47.99
24	3-Fluoro-10-hydroxy-14-methyl-8,13,13b,14-tetrahydroindolo[2′,3′:3,4]pyrido[2,1-b]quinazolin-5(7H)-one	2	0	1	False	3.64	59.88
25	3-Fluoro-10-methoxy-14-methyl-8,13,13b,14-tetrahydroindolo[2′,3′:3,4]pyrido[2,1-b]quinazolin-5(7H)-one	1	0	1	False	3.86	47.99
26	10-Acetyl-14-methyl-8,13,13b,14-tetrahydroindolo[2′,3′:3,4]pyrido[2,1-b]quinazolin-5(7H)-one	2	0	1	False	3.41	56.36
27	10-Iodo-14-methyl-8,13,13b,14-tetrahydroindolo[2′,3′:3,4]pyrido[2,1-b]quinazolin-5(7H)-one	1	0	1	False	4.25	39.06
28	10-Hydroxy-14-methyl-8,13,13b,14-tetrahydroindolo[2′,3′:3,4]pyrido[2,1-b]quinazolin-5(7H)-one	2	0	2	False	3.43	59.88
29	13-(4-Chlorobenzoyl)-10-hydroxy-14-methyl-8,13,13b,14-tetrahydroindolo[2′,3′:3,4]pyrido[2,1-b]quinazolin-5(7H)-one	1	1	4	False	5.75	67.47
30	10-(Benzyloxy)-14-methyl-8,13,13b,14-tetrahydroindolo[2′,3′:3,4]pyrido[2,1-b]quinazolin-5(7H)-one	1	0	0	False	5.24	47.99
31	14-Methyl-5,7,8,13,13b,14-hexahydroindolo[2′,3′:3,4]pyrido[2,1-b]quinazoline	1	0	0	True	4.33	21.76
32	10,14-Dimethyl-8,13,13b,14-tetrahydroindolo[2′,3′:3,4]pyrido[2,1-b]quinazolin-5(7H)-one	1	0	1	False	4.16	39.06
33	1,14-Dimethyl-8,13,13b,14-tetrahydroindolo[2′,3′:3,4]pyrido[2,1-b]quinazolin-5(7H)-one	1	0	1	False	4.16	39.06
34	4-Hydroxy-14-methyl-8,13,13b,14-tetrahydroindolo[2′,3′:3,4]pyrido[2,1-b]quinazolin-5(7H)-one	2	0	2	False	3.43	59.88
35	4-Chloro-14-methyl-8,13,13b,14-tetrahydroindolo[2′,3′:3,4]pyrido[2,1-b]quinazolin-5(7H)-one	1	0	1	False	4.34	39.06
36	12-Chloro-14-methyl-8,13,13b,14-tetrahydroindolo[2′,3′:3,4]pyrido[2,1-b]quinazolin-5(7H)-one	1	0	1	False	4.34	39.06
37	10-Hydroxy-2-methyl-7,8,13,13b-tetrahydro-5H-benzo[5′,6′][1,3]oxazino[3′,2′:1,2]pyrido[3,4-b]indol-5-one	2	0	2	False	3.74	65.45
38	10-Hydroxy-4-methyl-7,8,13,13b-tetrahydro-5H-benzo[5′,6′][1,3]oxazino[3′,2′:1,2]pyrido[3,4-b]indol-5-one	2	0	2	False	3.74	65.45
39	3-Chloro-10-hydroxy-7,8,13,13b-tetrahydro-5H-benzo[5′,6′][1,3]oxazino[3′,2′:1,2]pyrido[3,4-b]indol-5-one	2	0	1	False	3.92	65.45
40	3-Bromo-10-hydroxy-7,8,13,13b-tetrahydro-5H-benzo[5′,6′][1,3]oxazino[3′,2′:1,2]pyrido[3,4-b]indol-5-one	2	0	1	False	4	65.45

S (Solubility): 0 (Extremely low), 1 (Very low), 2 (Low), 3 (Good), 4 (Optimal).

A (Absorption): 0 (Good), 1 (Moderate), 2 (Poor), 3 (Very poor).

BBB permeability: 0 (Very high), 1 (High), 2 (Medium), 3 (Low), 4 (Undefined).

Molecular Docking

To explore the antiviral potential of evodiamine compounds, SARS-CoV-19 protease was selected and obtained from the protein data bank (PDB: 7JQ2).¹⁶ Docking protocol validation was successful in redocking the co-crystallized inhibitor MPI5, with RMSD between redocked and co-crystallized poses of 1.97 Å (Figure 2). Afterward, the compounds were docked and their binding modes were viewed using Biovia discovery studio visualizer 2021. The compounds achieved favorable binding to the proteases as demonstrated by the negative values of docking score energies (kcal/mol), as shown in Table 2.

Figure 2.

Docking protocol validation: (co-crystallized pose = green and redocked pose = pink) inside SARS-CoV-2 main protease binding site.

Table 2.

Binding Affinities of Compounds Against SARS-CoV-2 in kcal/mol.

Compound	SARS-CoV-2	Compound	SARS-CoV-2	Compound	SARS-CoV-2
1	−6.80	15	−7.10	29	−7.63
2	−6.67	16	−7.16	30	−7.28
3	----	17	−7.22	31	−6.43
4	----	18	−6.93	32	−6.89
5	−6.85	19	−6.73	33	−6.89
6	−7.11	20	−6.85	34	−6.80
7	−6.45	21	−7.05	35	−6.64
8	−6.75	22	−6.89	36	−6.91
9	−7.02	23	−6.98	37	−6.58
10	−7.30	24	−6.79	38	−6.31
11	−7.06	25	−6.81	39	−6.92
12	−7.22	26	−6.96	40	−6.88
13	−6.90	27	−6.63	MPI5	−9.99
14	−6.59	28	−6.59

The MPI5 structure can be divided into three main parts according to the types of interactions. The first part with the lipophilic residues of the phenyl and cyclohexyl moieties, binds through hydrophobic alkyl and pi-alkyl interactions with HIS41, MET49, MET165, PRO168, and ALA191. The second part is in which a hydrogen bond is formed between the hydrophilic amide backbone and GLY143, SER144, CYS145, HIS164, and GLU166, while the pyrrolidinone ring binds with PHE140, GLU166, and HIS172. The third and final part is the covalent binding observed with CYS145 and the methylene carbon that bears the hydroxyl group (Figure 3).

Figure 3.

2D interactions of MPI5 with SARS-CoV-2 protease.

The top five scoring molecules of our natural evodiamine library were 29, 10, 30, 12, and 17, which achieved binding scores of − 7.63, 7.30, − 7.28, − 7.22, and − 7.22 kcal/mol, respectively. The interactions of compound 29 (Figure 4) revealed the placement of the para chlorobenzoyl residue in the same pocket as that of the cyclohexyl moiety of MPI5, which enabled similar hydrophobic interactions with HIS41, MET49, and MET165, along with hydrogen bonding to TYR54. The lipophilic part of our selected evodiamine derivative spanned across part of the amide linkage toward the pyrrolidinone ring, thus forming hydrogen bonds with LEU141, ASN142, and CYS145, as well hydrophobic interactions with HIS41 and CYS145. This lipophilic replacement resulted in diminished hydrogen bonding, which explains the decrease in the binding score when compared to MPI5. Interestingly, the methyl of the quinazolinone part is placed in the benzylacetamido part, which increases its potential for further derivatization for increased binding.

Figure 4.

(A) 3D superimposition of MPI5 (green) and compound 29 (pink). B) 2D interactions of 29 with SARS-CoV-2 protease.

Interactions of the other top-scoring hits illustrated their binding to four common amino acid residues (His41, Cys145, Met49, and Met165) as MPI5, as shown in Figure 5. All had similar hydrophobic interactions with His41, Met49, and Met165, as well as forming a hydrogen bond with His41 through the carbonyl of the terminal amide in 10 and 17. However, the binding of the compounds to Cys145 transformed into hydrophobic interactions in all of them rather than hydrogen bonding as in the case of MPI5.

Figure 5.

2D interactions of compounds 10, 30, 12, and 17 with SARS-CoV-2 protease.

Consequently, the promising properties of the top-scoring compound 29, as well as its particular similarity to MPI5 in binding, urged an in-depth subsequent evaluation of its binding.

Molecular Dynamics

The molecular dynamic simulation was used for extensive analysis of the binding modes and stability under realistic physiological conditions. Using the Schrodinger Maestro suite, the protein with and without MPI5 and 29 were simulated for 100 ns. Afterward, the simulation trajectories were analyzed and several attributes were calculated such as the RMSD of the protein–ligand complex for determination of the binding interaction stability, the RMSD of the ligand to evaluate the conformational changes that the ligand undergoes over the estimated simulation process, as well as RMSF of the amino acid residues and their contact with ligands.

The molecular dynamic simulation of the free protease shows relative homogeneity in behavior as demonstrated by RMSD's initial consistency around 2 Å for the first 40 ns, followed by a plateau at 2.60 Å (Figure 6). When MPI5 was introduced, the RMSD behavior of the protease remained unchanged for the first 40 ns before increasing to around 2.50 Å for the rest of the simulation, whereas MPI5's RMSD fluctuated throughout the simulation at around 1.20 Å. On the other hand, the effect of binding of compound 29 to the protein is observed as a consistent decrease in RMSD to around 1.25 Å, and its own RMSD remained uniform at 0.25 Å as well. These values imply the ability of 29 to restrict enzymatic movement, in turn, limiting its activity in a stronger fashion than MPI5. Furthermore, 29 possessed high conformational stability during the whole process which elucidates the high binding stability of the complex.

Figure 6.

RMSD plots of protease in the absence and presence of inhibitors MPI5 and 29 during 100 ns simulation.

Similarly, the same observation can be drawn when examining the RMSF values of the amino acid residues in the presence and absence of MPI5 and 29. As shown in Figure 7, the residues showed more prominent limitations in fluctuations in the case of compound 29 compared to the free protease and MPI5 complexed one.

Figure 7.

RMSF plots of protease in the absence and presence of inhibitors MPI5 and 29 during 100 ns simulation (ligand contacts are highlighted in green).

One of the most commonly used methods for calculating binding free energy is Molecular Mechanics Generalized-Born Surface Area (MM-GBSA). The lower a ligand-protein complex's projected binding free energy is, the more stable the complex is expected to be, and the higher the ligand's activity and potency (Table 3). Both complexes showed stable binding throughout the dynamic simulation.

Table 3.

MMGBSA of SARS-CoV-2 Protease in Complex with MPI5 and 29 in kcal/mol.

	MPI5		29
		Start	End	Start	End
ΔG _Binding	−84.90	−91.31	−74.17	−69.76
ΔG _Binding _Coulomb	−33.08	−39.98	−23.68	−19.24
ΔG _{Binding (NS)}	−91.38	−97.47	−79.36	−70.42
ΔG _{Binding (NS)} _Coulomb	−35.00	−40.62	−22.85	−17.35

Conclusion

The recent SARS-COV-2 (COVID-19) pandemic revealed the imbalance between drug research and health hazard outbreaks. We have tried to bridge the gap through investigation of the potential antiviral aspects taking advantage of modern computer-aided drug design such as virtual screening and molecular dynamics. Evodia (Rutaceae) species contain many compounds widely used in folk medicine for the treatment of various ailments. Among these compounds, research shed light on the antiviral properties of evodiamine (A). Thus, we screened a database of evodiamine and derivatives against COVID-19 main protease. These natural compounds displayed potential inhibitory activity and compound 29 showed the highest binding ability and its structure occupied most of the binding pocket as MPI5. Further evaluation of its binding properties was also performed through molecular dynamic simulations and generalized MMGBSA over a period of simulation of 100 ns. binding of compound 29 with the main protease proved to be stable throughout the whole simulation process. Moreover, it has displayed a stronger limiting effect on enzymatic fluctuations compared to the co-crystallized ligand MPI5. These results support previous postulations of the prospective antiviral promiscuity of Evodia phytochemicals while shedding light on their inhibitory effects on SARS-CoV-2 protease.

Footnotes

Acknowledgement

The authors are grateful to the deanship of scientific research at Taif University for funding this project through Grant no (20211).

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethical Approval

Ethical Approval is not applicable to this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by deanship of scientific research at Taif University grant number (20211).

Statement of Human and Animal Rights

This article does not contain any studies with human or animal subjects.

Statement of Informed Consent

There are no human subjects in this article and informed consent is not applicable.

ORCID iDs

Amany Belal

Ahmed B. M. Mehany

References

X-L

Shan

, et al. Three new quinazolines from Evodia rutaecarpa and their biological activity. Fitoterapia. 2018;127:186–192. doi:10.1016/j.fitote.2018.02.003

Tang

Y-Q

Feng

X-Z

Huang

. Quinolone alkaloids from Evodia rutaecarpa. Phytochemistry. 1996;43(3):719‐722. doi:10.1016/0031-9422(96)00304-4

Liao

Chen

Chow

. Pharmacological studies of Chinese herbs. (9) pharmacological effects of evodiae fructus (author’s transl). Taiwan Yi Xue Hui Za Zhi. 1981;80(1):30‐38. http://www.ncbi.nlm.nih.gov/pubmed/6946188

Hyun

Min

H-Y

, et al. Evodiamine inhibits both stem cell and non-stem-cell populations in human cancer cells by targeting heat shock protein 70. Theranostics. 2021;11(6):2932‐2952. doi:10.7150/thno.49876

Jiang

. Evodiamine: a novel anti-cancer alkaloid from Evodia rutaecarpa. Molecules. 2009;14(5):1852‐1859. doi:10.3390/molecules14051852

Srivastava

Kaushik

Bhargava

Jain

Saxena

Jyoti

. A bioinformatics approach for the prediction of immunogenic properties and structure of the SARS-COV-2 B. 1.617. 1 variant spike protein. Biomed Res Int. 2021;2021:1‐8. doi:10.1155/2021/7251119

Agrawal

Raju

Udwadia

. Favipiravir: a new and emerging antiviral option in COVID-19. Med J Armed Forces India. 2020;76(4):370‐376. doi:10.1016/j.mjafi.2020.08.004

Shannon

Selisko

, et al. Favipiravir strikes the SARS-CoV-2 at its Achilles heel, the RNA polymerase. bioRxiv Prepr Serv Biol. 2020. Published online May 15, 2020:098731. doi:10.1101/2020.05.15.098731. Pubmed 32511380.

Qazi

Das

Khuntia

, et al. In silico molecular docking and molecular dynamic simulation analysis of phytochemicals from Indian foods as potential inhibitors of SARS-CoV-2 RdRp and 3CLpro. Nat Prod Commun. 2021;16(9):1934578X2110317. doi:10.1177/1934578X211031707

10.

Dai

J-P

W-Z

Zhao

X-F

, et al. A drug screening method based on the autophagy pathway and studies of the mechanism of evodiamine against influenza A virus. PLoS One. 2012;7(8):e42706. doi:10.1371/journal.pone.0042706

11.

Vidoni

Fuzimoto

Ferraresi

Isidoro

. Targeting autophagy with natural products to prevent SARS-CoV-2 infection. J Tradit Complement Med. 2022;12(1):55‐68. doi:10.1016/j.jtcme.2021.10.003

12.

Zhang

Pei

Lai

. Computational multitarget drug design. J Chem Inf Model. 2017;57(3):403‐412. doi:10.1021/acs.jcim.6b00491

13.

Sun

Belal

Elanany

, et al. Identification of some promising heterocycles useful in treatment of allergic rhinitis: virtual screening, pharmacophore mapping, molecular docking, and molecular dynamics. Russ J Bioorganic Chem. 2022;48(2):438‐456. doi:10.1134/S1068162022330019

14.

Belal

. Drug likeness, targets, molecular docking and ADMET studies for some indolizine derivatives. Pharmazie. 2018;73(11):635‐642. doi:10.1691/ph.2018.8061

15.

Belal

. Pyrrolizines as potential anticancer agents: design, synthesis, caspase-3 activation and micronucleus (MN) induction. Anticancer Agents Med Chem. 2019;18(15):2124‐2130. doi:10.2174/1871520618666180409155520

16.

Yang

, et al. A quick route to multiple highly potent SARS-CoV-2 main protease inhibitors. ChemMedChem. 2021;16(6):942‐948. doi:10.1002/cmdc.202000924

17.

Belal

. 3D-Pharmacophore modeling, molecular docking, and virtual screening for discovery of novel CDK4/6 selective inhibitors. Russ J Bioorganic Chem. 2021;47(1):317‐333. doi:10.1134/S1068162021330013

18.

ULC CCG. Molecular Operating Environment (MOE), 2019.01. Published online 2020.

19.

Belal

Elanany

Raafat

Hamza

Mehany

ABM

. Calendula officinalis phytochemicals for the treatment of wounds through matrix metalloproteinases-8 and 9 (MMP-8 and MMP-9): in silico approach. Nat Prod Commun. 2022;17(5):1934578X2210988. doi:10.1177/1934578X221098848

20.

Bowers

Sacerdoti

Salmon

, et al. Molecular dynamics-Scalable algorithms for molecular dynamics simulations on commodity clusters. In: Proceedings of the 2006 ACM/IEEE Conference on Supercomputing - SC ’06. SC ’06. ACM Press; 2006:84. https://doi.org/10.1145/1188455.1188544

21.

Ghoreishi

, et al. OPLS4: improving force field accuracy on challenging regimes of chemical space. J Chem Theory Comput. 2021;17(7):4291‐4300. doi:10.1021/acs.jctc.1c00302

22.

Schrödinger LLC. Schrödinger Release 2021-3: Maestro. Schrödinger LLC. Published online 2021.

23.

Jacobson

Pincus

Rapp

, et al. A hierarchical approach to all-atom protein loop prediction. Proteins: Struct, Funct, Bioinf. 2004;55(2):351‐367. doi:10.1002/prot.10613

24.

Belal

Elanany

Santali

, et al. Screening a panel of topical ophthalmic medications against MMP-2 and MMP-9 to investigate their potential in keratoconus management. Molecules. 2022;27(11):3584. doi:10.3390/molecules27113584