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Saudi Journal of Biological Sciences logoLink to Saudi Journal of Biological Sciences
. 2023 Mar 1;30(4):103611. doi: 10.1016/j.sjbs.2023.103611

The molecular interplay of known phytochemicals as Culex pipiens and Rift Valley fever virus inhibitors through molecular docking

Nael Abutaha 1, Fahd A AL-Mekhlafi 1,⁎, Mohamed A Wadaan 1, Ahmed Moustafa Rady 1, Almohannad AA Baabbad 1, Mohammed S Al-Khalifa 1
PMCID: PMC10036733  PMID: 36970253

Abstract

Infectious diseases transmitted by vectors have claimed millions of lives. The mosquito Culex pipiens is a main vector species of Rift Valley Fever virus (RVFV) transmission. RVFV is an arbovirus that infects both people and animals. No effective vaccine or drugs are available for RVFV. Therefore, it is vital to find effective therapies for this viral infection. Because of their critical roles in transmission and infection, acetylcholinesterase 1 (AChE1) of Cx. Pipiens and RVFV glycoproteins, and nucleocapsid proteins are appealing protein targets. To understand intermolecular interactions, computational screening was carried out using molecular docking. More than 50 compounds were tested against different target proteins in the current study. Anabsinthin (-11.1 kcal/mol), zapoterin (-9.4 kcal/mol), porrigenin A (-9.4 kcal/mol), and 3-Acetyl-11-keto-beta-boswellic acid (AKBA) (-9.4 kcal/mol) were the top hit compounds for Cx. Pipiens. Similarly, the top hit compounds for RVFV were zapoterin, porrigenin A, anabsinthin, and yamogenin. The toxicity of Rofficerone is predicted as fatal (Class II), whereas Yamogenin is safe (Class VI). Further investigations are needed to validate the selected promising candidates against Cx. pipiens and RVFV infection using in-vitro and in-vivo methods.

Keywords: Culex pipiens, Insecticide, In silico, Toxicity predictions, RVF virus

1. Introduction

Vector-borne diseases can cause severe life-threatening infections and account for 17% of all infectious disease vectors that spread infections to different life forms (Organization and UNICEF, 2017) (Rao et al., 2021a).

Synthetic insecticides are used to control the proliferation of mosquitoes. Many insecticides have been used significantly despite human and environmental health concerns and toxicity to non-targeted organisms (Subra, 1981). Resistance in mosquito vectors has evolved as a result of the careless use of synthetic insecticidal agents. Additionally, the non-degradable nature of insecticides can cause bio-magnification, making the condition more devastating (Paily et al., 2006, Thakur et al., 2010, Zulhussnain et al., 2020). Collectively, the current status necessitates looking for target-specific, biodegradable, cost-effective and environment-friendly insecticidal agents against mosquitoes.

Cx. pipiens is a vector responsible for RVFV outbreaks. The RVFV (Family: Bunyaviridae, Genus: Phlebovirus) can cause a variety of illnesses, from minor complaints to encephalitis (Davies et al., 1985). RVFV's genome, like that of other bunyavirus family members, is made up of 3 negative-sense RNA segments labelled Small (S), Medium (M), and Large (L) (Schreur et al., 2018). The RNA-dependent RNA polymerase (RdRp), which is required for the replication cycle, is encoded by the L segment (Muller et al., 1991). The two main structural glycoproteins, glycoprotein N (GN) and GC, are encoded by the M segment (GC). For RVFV to enter the host cell, GN and GC must come together around its outer lipid envelope (Terasaki et al., 2011) (Liu et al., 2008). NSm1 and NSm2 are nonstructural (NS) proteins encoded by the S segment (Nitsche, 2018). The N protein (27 kDa) is crucial for viral replication (Liu et al., 2008) (Fatima et al., 2022) and the assembly of the virus via the interactions with the GC and GN. Because of their importance in the life cycle of the virus, they represent a valuable therapeutic approach (Fatima et al., 2022).

RVFV outbreaks have not only been documented in African and Indian Ocean islands but, most recently in Saudi Arabia. The first RVFV epidemic outside of Africa occurred in Jazan, Saudi Arabia (Al-Hazmi et al., 2003) (Eifan et al., 2021), which raised concerns about its global spread at different locations due to the transit and relocation of mosquitoes, human travel, and the trade of animals and their products (Linthicum et al., 2016).

Data curation advancements have enabled the screening of many molecules from open-access sources to find lead compounds for many target proteins (Parmar et al., 2022) (Rao et al., 2022). The goal of the research presented in this manuscript is to find natural compounds from open access sources that can inhibit AChE1 and induce mortality in mosquitos, thereby breaking the cycle of RVFV disease transmission. Similarly, the same sources are screened to identify promising compounds that can interfere with viral target proteins using in silico studies.

2. Material and methods

2.1. Modelling of AChE1

The Fasta format of AChE1 protein sequence of Cx. pipiens (Accession Number: Q86GC8) was obtained from https://www.uniprot.org. The 3D structure of AChE1 was predicted using the I-TASSER server (https://zhanglab.ccmb.med.umich.edu/I-TASSER/). LOMETS (Local Meta-Threading Server) approach was employed to look for templates from the PDB website. LOMETS contains multiple threading programs that generate many template alignments (Yang and Zhang, 2015). The program used the highest significant templates in the threading alignments and was measured by the Z-score, the energy score in standard deviation units relative to the statistical mean of all alignments(Yang and Zhang, 2015). C-score ordered the five homology models generated. Model quality was also evaluated by Ramachandran plots generated via Ramachandran Plot Server, Zlab (Ramachandran Plot (umassmed.edu), Verify 3D, and Errat in the SAVES server (saves.mbi.ucla.edu). This program validates the accuracy of a protein structure through the computation of several parameters, the creation of PostScript charts, and an examination of its overall geometry (Chhabra et al., 2010) (Laskowski et al., 1996). Before molecular docking analysis, the coordinates of the ligand docking site of modelled AChE1 were assessed using the CASTp 3.0 server.

2.2. Ligands preparation

Fifity compounds were acquired from PubChem as structure data files (sdf) and transformed using Open Babel 2.3.2 to '.pdb' format. Autodock 4.2 was used to create the ‘pdbqt’ file of the ligands.

2.3. Proteins preparation

Nucleocapsid (N) (PDB ID: 3OV9), Glycoprotein (C) (PDB ID: 4HJC), and Glycoprotein (N) (PDB ID: 6F8P) x-ray structures were obtained from the Protein Data Bank (PDB) (https://www.rcsb.org). The protein structures were ready for docking using AutoDockTool-1.5.7 (Huey et al., 2007), specifically by eliminating water molecules, calculating Gasteiger charges, and inserting polar hydrogens (Morris et al., 2009).

2.4. Molecular docking

The site-specific and molecular docking grid coordinates were generated using AutoDockTool-1.5.7 and AutoDock Vina 1.1.2. The generated 9 ligand-receptor complex conformations were ranked by binding energy (Trott and Olson, 2010). Bond energies that occurred between the receptors and ligands were identified. The best-docked conformation was visualized using the PyMol (DeLano, 2002) and Discovery Studio Visualizer (Biovia, 2017).

2.5. Analysis of drug-likeness of selected compounds

A critical stage in drug discovery is the evaluation of the drug-likeness of compounds in addition to absorption, distribution, metabolism, and excretion (ADME) (Bitew et al., 2021). The Protox II and ADMETlab 2.0 servers were used to predict the ADME properties. Based on the Protox II server, the toxicity of the compound is grouped into six categories (I to VI). Class I is the most toxic, and class VI is non-toxic (LD50 greater than 5000).

3. Result

3.1. Modelling of AChE1

Using I-TASSER, the 3D model structure of AChE1 was generated using the top 10 threading programs (FFAS-3D, HHSEARCH I, SPARKS-X, HHSEARCH I, HHSEARCH2, HHSEARCH, Neff-PPAS, pGenTHREADER, HHSEARCH, wdPPAS) for modelling the AChE1 sequence. Five models were generated by I-TASSER (Fig. 1), and the models were assessed using C-score and TM-score. The C-score is a confidence measure utilized to evaluate the predicted quality of the model. A model with a high confidence score has a higher C-score. The C-score normally ranges from −5 to 2. All the predicted protein models were good because C-score ranged from −0.27 to −3.44. The Template Modeling score) TM-score(, a proposed scale for evaluating the similarity between two structures, is another significant parameter. AChE1 had an estimated TM-score of 0.68 0.12. A model with the proper topology has a TM-score greater than 0.5, whereas one with a TM-score less than 0.17 suggests random similarity. (Yang and Zhang, 2015) (Roy et al., 2010) (Zhang, 2008). The built model has a considerable RMSD (Root-Mean-Square Deviation) of 8.7 ± 4.5 Å. Validation and quality of AChE1 predicted model was also assessed using PROCHEK server and Ramachandran plot (Fig. 1 and Table 1(supplementary material)). Model 1 was the most accurately predicted one based on the C-score. In the protein structure, amino acid analysis using Ramachandran plots showed more than 85.809% of amino acids were found in the region with the highest preference, and 7.426% of amino acids (aa) are in the preferred region. Only 6.766% of aa were in the questionable region.

Fig. 1.

Fig. 1

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Ramachandran plot and surface view of the top 5 models predicted by I-TASSER. Preferred regions are indicated in green, and brown and questionable regions are represented in red.

3.2. Docking evaluation

3.2.1. Culex pipiens

AChE1 of Culex pipiens is a significant target in larvicidal development. In the current investigation, we investigated the binding affinity between the AChE1 protein with different compounds (Table 1). Table 2 and Fig. 2 provide 2D and 3D illustrations of zapoterin, porrigenin A, 3-Acetyl-11-keto-beta-boswellic acid, and absinthian-protein interactions. Similarly, Table 2 summarises the binding energy score of the compounds studied. The compounds investigated exhibited binding energy ranging from −6.9 to −11.1 kcal/mol. Anabsinthin (-11.1 kcal/mol), zapoterin (-9.4 kcal/mol), porrigenin A (-9.4 kcal/mol), and 3-Acetyl-11-keto-beta-boswellic acid (-9.4 kcal/mol) were the highest scoring substances with low binding energies. Overall, the docking investigation revealed that anabsinthin interacts with the AChE1 residues of ASN530 (H-bond), PRO359, TYR525, TRP653, LYS654, ALEU657 (Hydrophobic interactions), and LYS654 (Salt Bridge) with a binding affinity of −11.1 kcal/mol, zapoterin with the AChE1 residues of PRO359, TYR525, TRP, 653 LYS654, PRO658, TYR489, and LYS654 with binding energy (-9.4 kcal/mol), porrigenin A (-9.4 kcal/mol) that interacts with the AChE1 residues ASN532, ARG629 (H-bond), and ALA436, GLU533, ALG537, LYS654 (Hydrophobic interactions), 3-Acetyl-11-keto-beta-boswellic acid that interacts with the AChE1 residues of ASN530, VAL363, TYR489, TYR525, TRP653, LEU657, and LYS654 with binding energy (-9.4 kcal/mol). Anabsinthin is the best larvicidal agent compared to other compounds investigated.

Table 1.

The docked score of different compounds against the modelled Acetylcholinesterase 1 (AChE1) of Cx. Pipiens and RVFV Nucleocapsid (N) (PDB ID: 3OV9), Glycoprotein (C) (PDB ID: 4HJC), and Glycoprotein (N) (PDB ID: 6F8P), proteins.

Sr. No. PubChem CID Name 4HJC 3OV9 6F8P AChE1
1 441,900 Yamogenin −7.9 −8.7 −9.8 −8.9
2 131,752,031 Ursololactone −7.7 −8.5 −8.5 −8.5
3 441,812 Zapoterin −7.5 −9.2 −8.8 −9.4
4 441,677 alpha-Elemolic acid −6.1 −8.7 −6.6 −7.6
5 441,809 Soularubinone −6.5 −8.2 −8.3 −7.8
6 441,893 Ruscogenin −8.2 −8.2 −8.9 −8.9
7 139,064,268 3,4,5,6,6a,7,8,8a,10,11,12,14b-Dodecahydro-1H-picene-2-carboxylic acid −7.6 −8.8 −9.0 −8.0
8 16,745,530 (3S,4aR,6aR,6bR,8aS,11S,12aS,14bS)-11-(Hydroxymethyl)-4,4,6a,6b,8a,11,14b-heptamethyl-1,2,3,4a,5,6,7,8,9,10,12,12a,14,14a-tetradecahydropicene-3,14-diol −6.8 −9.1 −8.3 −8.5
9 21,668,683 26-Deoxyactein −7.6 −8.8 −8.6 −8.5
10 441,796 Glaucarubinone −6.5 −8.0 −8.1 −8.4
11 44,566,819 Porrigenin A −8.0 −8.3 −9.9 −9.4
12 11,453,544 α-Onocerin −5.6 −8.7 −8.4 −8.5
13 3,083,930 β-Onocerin −6.7 −9.4 −8.2 −7.8
14 11,655,911 Quercetin-3′-Glucuronide −7.7 −8.1 −9.8 −8.7
15 4998 Quassin −6.6 −8.2 −8.0 −8.2
16 12,314,864 Queretaroic acid −6.5 −8.0 −8.3 −8.1
17 73,657,088 Camellenodiol −6.6 −8.1 −8.0 −8.4
18 44,558,930 Anabsinthin −8.3 −10.4 −9.6 −11.1
19 441,791 Chaparrin −7.0 −8.0 −9.0 −8.0
20 73,154 Chaparrinone −7.1 −8.1 −8.4 −8.4
21 5,282,164 Gamma-Oryzanol −8.2 −7.8 −8.6 −7.9
22 10,100,589 Cimicifugoside −7.4 −8.0 −8.0 −8.0
23 187,898 Esculentic acid (Phytolacca) −7.2 −7.9 −7.6 −7.9
24 24,892,745 Deoxylimonoic acid D-ring-lactone −5.9 −7.5 −6.6 −6.9
25 441,936 Pfaffic acid −7.5 −8.8 −9.1 −8.2
26 24,820,753 Limonin −7.3 −7.5 −8.2 −7.4
27 439,529 Limonoic acid −6.3 −7.8 −7.4 −7.2
28 21,121,725 4,4-Dimethyl-14a-formyl-5alpha-cholesta-8,24-dien-3beta-ol −6.5 −8.5 −7.3 −7.5
29 16,061,213 4-Oxomytiloxanthin −7.6 −8.1 −8.6 −7.5
30 6,443,726 7,8-Didehydroastaxanthin −6.8 −8.1 −7.8 −7.8
31 22,298,935 32-Hydroxylanosterol −7.2 −7.1 −7.7 −7.3
32 11,168,203 3-Acetyl-11-keto-beta-boswellic acid −7.4 −7.9 −7.9 −9.4
33 46,173,816 Limonoate A-ring-lactone −7.6 −9.0 −8.3 −7.6
34 612,009 Theasapogenol A −7.2 −8.3 −8.5 −8.1
35 15,558,616 Alisol A −6.9 −7.8 −8.5 −7.8
36 14,036,813 Alisol C −6.5 −7.8 −7.4 −7.8
37 160,465 Bassic acid −7.1 −7.7 −7.6 −8.4
38 65,048 Medicagenic acid −7.4 −7.8 −7.9 −8.0
39 156,580 Pokeberrygenin −7.1 −8.3 −7.6 −7.9
40 441,893 Ruscogenin −6.5 −7.3 −8.1 −6.9
41 5,316,120 3-O-cis-Coumaroylmaslinic acid −6.2 −7.1 −7.4 −7.5
42 101,763,940 Rofficerone −7.6 −9.9 −8.6 −8.3
43 21,628,395 Madlongiside C −7.6 −8.0 −8.2 −8.4
44 11,590,967 20beta-Hydroxyursolic acid −7.4 −8.8 −8.2 −8.6
45 Ganoderic acid A −6.8 −7.0 −6.4 −7.5
46 15,181,201 3-alpha-O-acetyl-alpha-boswellic acid −7.5 −9.1 −7.4 −7.6
47 14,140,065 (3beta,15alpha,22S,24E)-3,15,22-Trihydroxylanosta-7,9(11),24-trien-26-oic acid −7.6 −9.0 −8.3 −8.9
48 131,752,020 Albigenic acid −7.2 −7.8 −7.7 −7.9
49 12,305,182 Isoarborinol −7.1 −7.8 −9.0 −8.7
50 101,906 Hecogenin acetate −8.3 −8.6 −9.0 −8.9
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Table 2.

Molecular docking values of the best compounds against AChE1, 6F8P and 3OV9.

Compound H-bond Residual Amino acid Interactions
 Salt Bridges
Hydrophobic interactions π-Stacking
AChE1
Zapoterin – PRO359, TYR525, TRP653, LYS654, PRO658 TYR489 LYS654
Porrigenin A ASN532, ARG629 ALA436, GLU533, ALG537, LYS654 + +
Anabsinthin ASN530 PRO359, TYR525, TRP653, LYS654, LEU657 LYS654
3-Acetyl-11-keto-beta-boswellic acid ASN530 VAL363, TYR489, TYR525, TRP653, LEU657 LYS654
6F8P
Yamogenin – PRO201, TYR297, ASP301, VAL342, TYR459 – –
Porrigenin A HIS249, TYR297, LEU299, ASP301 VAL342, TYR459 – –
Quercetin-3′-Glucuronide ASN160, GLY163, ASP370, CYS374, LYS395, ILE399, GLN401 ASP398 – –
Anabsinthin TYR297 199LYS, 200PHE, 247LYS, 297TYR, 301ASP, 459TYR, – LYS199, HIS249
Pfaffic acid SER205, TYR459 PHE200, PRO201, ASP301, VAL342, TYR459 ARG461
3OV9
Zapoterin GLY113, TRB114 ILE58, LEU111, TRP114, PHE208, BTRP114
β-Onocerin PHE33, PRO147, PHE176, ILE180, PRO199, ALA203
Anabsinthin PHE33, PRO147, PHE176, ILE180, PRO199 HIS146
Hecogenin acetate ASN66, PRO127, PHE176, ILE180, BRO 199
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Fig. 2.

Fig. 2

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Interaction modes of AChE1 inhibitors (A). Inhibitory binding modes of selected ligands. (B) 2D interaction analysis of Porrigenin A, Zapoterin, Zapoterin and Anabsinthin.

3.2.2. Rift Valley Fever virus (RVFV)

Different compounds were docked against three different RVFV proteins, and only the compounds that showed high binding affinity (greater than9.0 kcal/mol) to RVFV proteins were considered for further analysis. Yamogenin, porrigenin A, quercetin-3′-glucuronide, anabsinthin, pfaffic acid, zapoterin, β-Onocerin, and hecogenin acetate showed a high binding affinity with the 2 target proteins, namely 6F8P and 3OV9. Porrigenin A has the strongest affinity for 6F8P, followed by yamogenin, quercetin-3′-glucuronide, and anabsinthin, respectively (Table 1). Porrigenin A (-9.9 kcal/mol) formed multiple hydrogen bonds with HIS249, TYR297, LEU299, ASP301 when bound to 6F8P protein and hydrophobic interactions with VAL342, TYR459. Similarly, Calyxin D (−11.30 kcal/ mol) formed multiple hydrogen bonds with ARG461, ASP301, HIS249, and LYS247. The docking of yamogenin on 6F8P shows 6 hydrophobic interactions with 5 residues PRO201, TYR297, ASP301, VAL342, and TYR459. Quercetin-3′-Glucuronide was bound to 6F8P protein with a binding affinity of −9.8 kcal/mol. Similarly, quercetin- 3′-Glucuronide has also formed eleven hydrogen bonds with 7 residues, namely, ASN160, GLY163, ASP370, CYS374, LYS395, ILE399, GLN401 and one hydrophobic interaction with ASP398 (Table 2 and Fig. 3).

Fig. 3.

Fig. 3

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Interaction modes of 6F8P inhibitors (A). Inhibitory binding modes of selected ligands. (B) 2D interaction analysis of Anabsinthin A, Yamogenin, Quercetin-3′-Glucuronide and Porrigenin A.

The binding between 3OV9 protein and compounds investigated revealed that the binding affinity differed according to the compounds' nature. The minimum binding energy values reflect the docking results (Table 1). However, anabsinthin was the best compound docked and has also formed six hydrophobic interactions with 5 residues, namely, PHE33, PRO147, PHE176, ILE180, PRO199 and one Salt Bridges with HIS146 (Table 2 and Fig. 4).

Fig. 4.

Fig. 4

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Interaction modes of 3OV9 inhibitors. (A) Inhibitory binding modes of selected ligands. (B) 2D interaction analysis of Anabsinthin A, Zapoterin, Zap Hecogenin acetate and β-Onocerin.

3.3. Toxicity profile/toxicological endpoints

The ten compounds' toxicological endpoints (cytotoxicity, mutagenicity, immunotoxicity, and carcinogenicity) and organ toxicity (hepatotoxicity) were predicted. The results show (Table 4) that the compounds are neither hepatotoxic (except Pfaffic acid) nor cytotoxic (except for Limonoate A-ring-lactone). Yamogenin, porrigenin A, anabsinthin, rofficerone, zapoterin, and hecogenin acetate show one toxicological endpoint (immunotoxicity). On the other hand, Pfaffic acid showed immunotoxicity, hepatotoxicity, and carcinogenicity. However, neither rofficerone nor -Onocerin exhibited any of the predicted toxicological effects. The remaining compounds are predicted to have acute toxicity ranging from II to VI; however, they are classified as harmful toxic classes. The LD50 values ranged from 244 to 8000 mg/Kg (Table 4, supplementary material). The toxicity of substances was divided into six classes by the worldwide standardized system of chemicals grouping and labelling (as stated in Protox II). Class I is the most toxic, while class VI is non-toxic (LD50 greater than 5000). Rofficerone is predicted as fatal (Class II), whereas Yamogenin is safe (Class VI) (Table 4, supplementary material).

ADMETlab descriptors were also used to predict the toxicity of the compounds, such as skin sensitization (SkinSen), Ames Mutagenicity (AMES), human hepatotoxicity (H-HT), and hERG Blockers (hERG). The signs - and + in the category section (Table 00) denote the expected toxicities as negative and positive, respectively. Porrigenin A (SkinSen), quercetin-3′-Glucuronide (DILI), anabsinthin (H-HT And DILI), and limonoate A-ring-lactone (DILI) were predicted to show different levels of toxicity (Table 5, supplementary material).

4. Discussion

In recent years, integrating computational (in silico) methodologies has made it possible to investigate therapeutic targets' structure, function, and regulation more successfully (Zheng et al., 2018). Compared to the conventional method, which is expensive and time-consuming. Structure-based drug design (CBDD) is a key tool for drug discovery (Batool et al., 2019). This approach is based on several in silico methods developed to improve these protein-based therapeutics (Wang et al., 2018) (Zheng et al., 2018).

There are 350 million people affected by mosquito-borne diseases worldwide each year (Organization and UNICEF, 2017). Cx. pipiens is a major contributor to the spread of disease among all mosquito species. Notable carriers of the avian intestinal illness, West Nile virus, and Saint Louis encephalitis are Cx. pipiens (Ewing et al., 2019).

Most synthetic larvicides offered for sale (carbamates and organophosphates) work by inhibiting AChE, which is also the mechanism of action for many synthetic insecticides. They prevent voltage-gated sodium channels from closing (Silver et al., 2014). AChE is a crucial enzyme that stops nerve impulses from travelling through the synaptic pathway. The symptoms of intoxication include hyperexcitability, convulsions, paralysis, and restlessness (Arora et al., 2017). The foundation of the current work is based on the rationale of finding new compounds using in silico methods that operate as AChE1 inhibitors because of their potential larvicidal effects.

Since Cx. pipiens AChE1 protein has not been crystallized; it is essential to model AChE1 protein as closely as its actual structure. I-TASSER was chosen from among the various bioinformatics tools since it has been shown to have important advantages in protein structure prediction (Zhou and Skolnick, 2007). I-TASSER has been recognized as the top approach for protein structure prediction by the Critical Assessment of Protein Structure Prediction (CASP) (Cozzetto et al., 2009).

Many plant phytochemicals are known for their larvicidal properties. Several review articles described at length the promising use of crude extract and single isolated compounds as larvicidal agents. However, their failure to make it to the market is attributed to several issues, such as insufficient or excess plant extract application and in suffice knowledge of their mode of action (Ekor, 2014). In the current work, we assessed the binding affinity of 50 compounds with the AChE1 target. The top four hit compounds were zapoterin, porrigenin A, 3-Acetyl-11-keto-beta-boswellic acid, and anabsinthin. Acetylcholinesterase activity has been shown to be decreased by 3-acetyl-11-keto-beta-boswellic acid (Gong et al., 2022). AChE of the cockroach was reported to interact with another substance, such as 2,3-dimethyl maleic anhydride, and cause mortality (Rao et al., 2021b).

Since there are currently no approved treatment drugs, viral illnesses have emerged as one of the major public health concerns, making infection control challenging. The failure to stop the Coronavirus outbreak's calls for additional research to find novel antiviral molecules. Numerous medications that treat a variety of human ailments are made from natural ingredients. Due to the rise in drug resistance to numerous ailments and the insignificant side effects of conventional therapies, plant-based compounds are the best prospective solutions (Khan et al., 2019).

Recent research has looked at a number of plant extracts, including Artemisia afra, Elaeodendron croceum, Adansonia digitata, E. transvaalese, Sutherlandia frutescens, Helichrysum aureonitens, and Euclea natalensis to reduce the RVFV viral load in Vero cells. The authors attributed the high antiviral activity of A. afraand E. croceum extracts to 4,5-dicaffeolyquinic acid, epigallocatechin-gallate and gallic acid (More et al., 2021). The diverse modes of action, such as the suppression of viral attachment and access to the host cells, could be responsible for the morphological alterations of the viral particles following incubation with the plant extracts. As a result, plant extracts may prevent viral replication in various ways, such as preventing viral entry into cells and binding virions to cell receptors (Wu et al., 2015).

Due to their crucial functions in RVFV replication, nucleocapsid (N), glycoprotein N, and glycoprotein C proteins are desirable therapeutic targets. The nucleocapsid (N) is essential for viral replication (Liu et al., 2008) (Ferron et al., 2011) (Raymond et al., 2010) and also interacts with glycoproteins N and C to carry out viral assembly (Liu et al., 2008). Therefore, glycoproteins and nucleocapsid proteins are desirable targets.

In the current work, we assessed the binding affinity of 50 compounds with key antiviral targets. The top seven hit compounds, zapoterin, porrigenin A, anabsinthin, yamogenin, quercetin-3′-Glucuronide, pfaffic acid and β-Onocerin displayed optimal binding against the targeted RVFV proteins. However, Anabsinthin was placed first because it had the best binding affinity. The investigated substances may have additive or synergistic effects against the virus. This is important because a higher virus mutation rate leads to continual change. The benefits of combining treatment approaches for HCV and HIV infections have already been established (Lin et al., 2016) (Nakata et al., 2008).

5. Conclusions

More than 50 compounds were tested against different target proteins in the current study. Anabsinthin, zapoterin, porrigenin A, and 3-Acetyl-11-keto-beta-boswellic acid were the top hit compounds for Cx. Pipiens. Similarly, the top hit compounds for RVFV were zapoterin, porrigenin A, anabsinthin, and yamogenin. These compounds may operate as possible inhibitors of the Cx. pipiens and RVFV-targeted proteins, according to our computational calculations. Additional in-vitro and in-vivo research must be carried out to verify the findings of the current chemoinformatics investigation.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

Researchers Supporting Project number (RSPD2023R757), King Saud University, Riyadh, Saudi Arabia.

Footnotes

Peer review under responsibility of King Saud University.

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.sjbs.2023.103611.

Contributor Information

Nael Abutaha, Email: nabutaha@ksu.edu.sa.

Fahd A. AL-Mekhlafi, Email: falmekhlafi@ksu.edu.sa.

Mohamed A Wadaan, Email: wadaan@ksu.edu.sa.

Ahmed Moustafa Rady, Email: Ahabbo@ksu.edu.sa.

Almohannad A.A. Baabbad, Email: almbaabbad@ksu.edu.sa.

Mohammed S. Al-Khalifa, Email: mkhalifa@KSU.EDU.SA.

Appendix A. Supplementary material

The following are the Supplementary data to this article:

Supplementary Data 1
mmc1.docx (17.7KB, docx)

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