Identification of Potential Phytochemical/Antimicrobial Agents against Pseudoperonospora cubensis Causing Downy Mildew in Cucumber through In-Silico Docking

Compatibility interactions between the host and the fungal proteins are necessary to successfully establish a disease in plants by fungi or other diseases. Photochemical and antimicrobial substances are generally known to increase plant resilience, which is essential for eradicating fungus infections. Through homology modeling and in silico docking analysis, we assessed 50 phytochemicals from cucumber (Cucumis sativus), 15 antimicrobial compounds from botanical sources, and six compounds from chemical sources against two proteins of Pseudoperonospora cubensis linked to cucumber downy mildew. Alpha and beta sheets made up the 3D structures of the two protein models. According to Ramachandran plot analysis, the QNE 4 effector protein model was considered high quality because it had 86.8% of its residues in the preferred region. The results of the molecular docking analysis showed that the QNE4 and cytochrome oxidase subunit 1 proteins of P. cubensis showed good binding affinities with glucosyl flavones, terpenoids and flavonoids from phytochemicals, antimicrobial compounds from botanicals (garlic and clove), and chemically synthesized compounds, indicating the potential for antifungal activity.


Introduction
The cucumber crop is widely grown in temperate and tropical regions of the world. It stands in fourth position after tomato (Lycopersicon esculentum Mill.), cabbage (Brassica oleracea var. capitata L.) and onion (Allium cepa L.). Cucumber has been considered an essential food source for over 5000 years and is used in culinary and non-culinary products. Fresh fruits are used in salads, pickles, cakes, and cooking. At the same time, processed cucumbers are used in sandwiches. Based on usage, cucumber fruits are divided into two types. "Pickling cucumbers" are mainly used in processing foods such as pickles. "Slicing cucumbers" are used for fresh consumption. Cucumbers are widely used as edible fruits because fruits are crispy, delicious, low in calories, rich in nutrients, and an excellent source of fiber needed for a healthy digestive system. The fruits of cucumbers possess several medicinal properties, namely, preventing constipation, having a cooling effect, and checking jaundice and indigestion [1][2][3][4]. Along with these, the consumption of cucumbers also provides good nutritional benefits to human beings. Every 100 g of cucumber fruit contributes 5 g of carbohydrates, 0.4 g of protein, 0.1 g of fat, 0.3 g of minerals, 10 mg of calcium, 0.4 g of fiber, and traces of vitamin C and iron. Cucumbers are a boon to the cosmetic industry. Many cosmetic products contain cucumber extracts, such as soaps, lotions, creams, and perfumes. In addition, the seeds of cucumbers are used in Ayurvedic preparations [5]. The phytochemicals present in C. sativus, antimicrobial compounds from botanicals viz., Ocimum tenuiflorum, Allium cepa, Syzygium aromaticum, Azadirachta indica, and Mentha arvensis, and fungicides were obtained from the published literature [22][23][24][25][26][27][28]. A total of 71 compounds were selected for molecular docking, details of these compounds are given in Table 2. The three-dimensional (3D) structures of proteins (QNE and cytochrome oxidase subunit 1) were obtained from the protein data bank (www.rcsb.org) (accessed on 25 March 2021). Similarly, 3D confirmers of the selected ligands were retrieved from the PubChem (https://pubchem.ncbi.nlm.nih.gov) (accessed on 25 March 2021 database in PDB and SDF formats, respectively.   [19]. Using Biovia Discovery Studio 2020, all water molecules, ions, complex molecules of ligands, and proteins were removed [31]. A PDB structure was optimized with Auto Dock-MGL by adding the polar hydrogens to obtain the PDBQT files.

Active Site Prediction and Molecular Docking
Using Biovia Discovery Studio 2020, the active sites of fungal proteins were determined. Molecular docking of optimized ligands and proteins in PDBQT format was performed using Auto Dock Vina software [30]. Auto Dock Vina software uses its scoring function (binding affinity) to predict the interaction between ligand and protein. A grid box of 60 Å × 60 Å × 60 Å was used for proteins with different XYZ coordinates based on predicted active sites for molecular docking. After docking analysis, the output file consists of the top nine binding poses, with their respective binding affinity in kcal/mol. The ligand binding poses with the highest binding affinity and the lowest root mean square deviation (RMSD) were chosen. The protein-ligand interaction in 3D structure was visualized in Py-MOL. The two-dimensional (2D) structure was also visualized in Biovia Discovery Studio 2020. The 3D visualization indicates the target protein's binding pocket or precise location.
On the other hand, the 2D structure visualization shows the different bonds formed between the amino acid residues of the fungal target protein and ligand. The workflow of molecular docking of compounds with proteins of P. cubensis associated with cucumber is depicted in Figure 1. The botanicals studied in molecular docking were further evaluated under in vitro conditions. hydrogens [19]. Using Biovia Discovery Studio 2020, all water molecules, ions, complex molecules of ligands, and proteins were removed [31]. A PDB structure was optimized with Auto Dock-MGL by adding the polar hydrogens to obtain the PDBQT files.

Active Site Prediction and Molecular Docking
Using Biovia Discovery Studio 2020, the active sites of fungal proteins were determined. Molecular docking of optimized ligands and proteins in PDBQT format was performed using Auto Dock Vina software [30]. Auto Dock Vina software uses its scoring function (binding affinity) to predict the interaction between ligand and protein. A grid box of 60 Å × 60 Å × 60 Å was used for proteins with different XYZ coordinates based on predicted active sites for molecular docking. After docking analysis, the output file consists of the top nine binding poses, with their respective binding affinity in kcal/mol. The ligand binding poses with the highest binding affinity and the lowest root mean square deviation (RMSD) were chosen. The protein-ligand interaction in 3D structure was visualized in PyMOL. The two-dimensional (2D) structure was also visualized in Biovia Discovery Studio 2020. The 3D visualization indicates the target protein's binding pocket or precise location.
On the other hand, the 2D structure visualization shows the different bonds formed between the amino acid residues of the fungal target protein and ligand. The workflow of molecular docking of compounds with proteins of P. cubensis associated with cucumber is depicted in Figure 1. The botanicals studied in molecular docking were further evaluated under in vitro conditions.

In Vitro Evaluation of Botanicals
The botanicals were tested at three different concentrations of 5, 10, and 15% by m/v. The required concentration of botanicals was extracted by two different solvents.

In Vitro Evaluation of Botanicals
The botanicals were tested at three different concentrations of 5, 10, and 15% by m/v. The required concentration of botanicals was extracted by two different solvents.

Aqueous Extraction
Leaf samples from neem, tulsi, pudina, and cloves of garlic and clove were collected from the fields, College of Horticulture, Bengaluru, India. A hundred grams of each botanical sample were cleaned with tap water and shade dried at room temperature until complete evaporation of moisture. The samples were then made into powder by using an electric blender. Three concentrations of 5, 10, and 15% were prepared by suspending 5 g, 10 g, and 15 g of each botanical powder in 100 mL of sterile distilled water followed by filtration+ through cheesecloth to remove unwanted coarse particles. The filtered extract was centrifuged at 5000 rpm for 5 min to obtain a clear extract [32][33][34][35].

Methanolic Extraction
The procedure for the methanolic extraction of the botanicals was followed according to [35]. Leaf samples from neem, tulsi, and pudina, and cloves of garlic and clove were collected from the college farm located in Bengaluru, India. A hundred grams of each botanical sample were cleaned and made into powder. Thirty grams of each powdered botanical were extracted with 90 mL of methanol and kept on a rotary shaker for three days with periodic shaking. Then, the extract was filtered with muslin cloth and centrifuged at 5000 rpm for 15 min. The supernatant was collected in tubes and kept in a hot air oven until complete evaporation of the solvent. Then the leftover material in the tubes was utilized for the experimentation.
The fresh sporangia of P. cubensis were collected from the naturally infected cucumber research plot located at the College of Horticulture, Bengaluru, India. The procedure for sporangia collection was followed as per Bommesh et al. [34]. Five-day-infected cucumber leaves were picked and cut into small pieces before being soaked in sterile distilled water to make a sporangial suspension. Using a hemocytometer, the sporangia concentration was adjusted to 100 sporangia/mL. Then, a drop of sporangia suspension was mixed with a drop of botanical extract of 5%, 10%, and 15%, respectively, and kept in a BOD incubator at 20 • C and 100 percent relative humidity for 2 h. After 2 h of incubation, the sporangial germination was recorded under a microscope. A cavity slide with sterile distilled water was maintained as the control. The percentage of sporangia germination was calculated by the given formula. The experiment was laid out with a Completely Randomized Design (CRD) with three replications. The cavity slides of each botanical concentration (5%, 10%, and 15%) were maintained with three replications along with the control under similar conditions. All slides were kept in a BOD incubator at 20 • C and 100 percent relative humidity for 2 h. The percentage of sporangia was calculated from all three replications along with the control, then analysis of variance was performed from the mean values,. The data were changed into arc-sine transformation for statistical analysis using OPSTAT [36].

Prediction of the 3D Structure of Proteins of P. cubensis
The two protein sequences of P. cubensis were obtained and annotated ( Table 1). The BLASTn results showed high query coverage (>99%) and percent identity (>99.47%) in both the proteins of P. cubensis. Later, these sequences were selected for protein modeling using SWISS-MODEL.

Template Selection
The selection of templates for building homology models was performed using the wizard of SWISS-MODEL with the following criteria: the template should show high coverage, i.e., >65 percent of the target aligned to the template and sequence identity should be more than 30 percent. Then, we used the GMQE and QMEAN scoring functions as initial criteria to discriminate good models from bad. Higher GMQE and QMEAN scores and acceptable alignment values were obtained during modeling, suggesting that statistically acceptable homology models were generated [37]. The output file was obtained in a PDB format that was used to visualize the model in PyMOL version 2.3. [19]. Global model quality estimation (GMQE) is the quality estimation that combines properties from the target-template alignment. The quality estimate ranges between 0 and 1 with higher values for better models. Qualitative model energy analysis (QMEAN) is a composite scoring function describing the major geometrical aspects of protein structures (Table 3). The results showed that the predicted cytochrome oxidase subunit 1 protein of P. cubensis model had 44.77 percent alpha-helices with beta turns comprising 8.27 percent, whereas the QNE4 effector protein has 42.36 percent alpha-helices with 8.70 percent beta turns (Table 4).

Ramachandran Plot Analysis
The Ramachandran plot indicated the phi-psi torsion angle for all residues in the structure (except those at the chain termination). The darkest areas correspond to the 'core' region representing the most favorable combinations of phi-psi values. Ideally, one would hope to have over 90 percent of the residues in these 'core' regions. The percentage of residues in the 'core' region is one of the best guides to stereo-chemical quality. A good quality Ramachandran plot has over 90 percent in the most favored region [38].
Ramachandran plot analysis was carried out for two proteins (cytochrome oxidase subunit 1 and QNE4) of P. cubensis. The QNE4 effector protein was shown to have 86.8 percent of residues in the favored region (red color), 12.3 percent in the additionally allowed area (yellow color), 0 percent of residues in the generously allowed region (beige color), and 0.9 percent of residues in the disallowed region (white color) (Figure 2a). Similarly, the cytochrome oxidase subunit 1 protein had 82.8 percent of residues in the favored region (red color), 16.0 percent in the additionally allowed region (yellow color), 1.2 percent of residues in the generously allowed region (beige color), and 0 percent of residues in the disallowed region (white color) ( Table 5) (Figure 2b). Homology modeling plays a vital role in structural proteomics and developing or designing potential compounds using an in silico approach.

Physico-Chemical Properties of Two Proteins of P. cubensis
The physico-chemical properties of proteins of P. cubensis were determined by Prot-Param from the EXPASY server (www.expasy.ch/tools) (accessed on 28 March 2021) [21] and furnished in Table 6. The extinction coefficient indicates how much light a protein absorbs at a particular wavelength. The instability index estimates the protein's stability in a test tube. If it is greater than 40, it is not stable; hence the effector QNE4 protein was stable in nature and another protein, cytochrome oxidase subunit 1, was unstable in nature. The grand average of hydropathic (GRAVY) value, which is calculated as the sum of the hydropathic values of all the amino acids divided by the number of residues in the sequence. A negative GRAVY value indicates that the protein is non-polar and a positive value indicates that the protein is polar. Hence, our results revealed that both proteins are non-polar in nature ( Table 6). The overall stereochemical properties of the generated models were highly reliable and valuable in understanding the protein function.
Ramachandran plot analysis was carried out for two proteins (cytochrome oxidase subunit 1 and QNE4) of P. cubensis. The QNE4 effector protein was shown to have 86.8 percent of residues in the favored region (red color), 12.3 percent in the additionally allowed area (yellow color), 0 percent of residues in the generously allowed region (beige color), and 0.9 percent of residues in the disallowed region (white color) (Figure 2a). Similarly, the cytochrome oxidase subunit 1 protein had 82.8 percent of residues in the favored region (red color), 16.0 percent in the additionally allowed region (yellow color), 1.2 percent of residues in the generously allowed region (beige color), and 0 percent of residues in the disallowed region (white color) ( Table 5) (Figure 2b). Homology modeling plays a vital role in structural proteomics and developing or designing potential compounds using an in silico approach.

Physico-Chemical Properties of Two Proteins of P. cubensis
The physico-chemical properties of proteins of P. cubensis were determined by Prot-Param from the EXPASY server (www.expasy.ch/tools) (accessed on 28 March 2021) [21] and furnished in Table 6. The extinction coefficient indicates how much light a protein absorbs at a particular wavelength. The instability index estimates the protein's stability in a test tube. If it is greater than 40, it is not stable; hence the effector QNE4 protein was stable in nature and another protein, cytochrome oxidase subunit 1, was unstable in nature. The grand average of hydropathic (GRAVY) value, which is calculated as the sum of the hydropathic values of all the amino acids divided by the number of residues in the sequence. A negative GRAVY value indicates that the protein is non-polar and a positive value indicates that the protein is polar. Hence, our results revealed that both proteins are non-polar in nature ( Table 6). The overall stereochemical properties of the generated models were highly reliable and valuable in understanding the protein function.

Molecular Docking Studies
To develop effective phytochemicals/antimicrobial compounds from botanicals against P. cubensis associated with cucumber, approximately 71 compounds from plant and chemical sources were used for molecular docking with proteins as a potential target. Before the docking analysis, the ligands were optimized by minimizing the energy with force field type MMFF94, and this helps in removing clashes among atoms and developing a stable starting pose of the ligands for binding interaction [39]. The docking, coupled with a scoring function, can be utilized to screen a large number of potential phytochemicals in silico. Generally, in molecular docking, a binding affinity lower than the upper threshold (−6 kcal/mol) is considered the cut-off value for concluding good binding affinity between protein and ligand [39]. The 3D and 2D visualization of phytochemicals, antimicrobial compounds, and chemically synthesized compounds based on binding affinity with respective fungal receptor proteins has been represented (Supplementary Figures S1-S6), (Figures 3-6). Hydrogen bond energy majorly contributed to the score [40] of selected compounds used in the current molecular docking studies against two proteins of P. cubensis, which displayed very good dock scores above the threshold cut-off of −6 kcal/mol ( Table 7). The ligand structures and necessary hydrogen bond formation between the top phytochemicals, antimicrobial compounds, and fungicides with their respective fungal protein receptors have been illustrated in Tables 8-11. Table 7. Dock score of interactions between phytochemicals, antimicrobial compounds, botanicals, and chemically synthesized compounds against cytochrome oxidase subunit 1 and QNE4 effector protein of P. cubensis.

Interactions between the QNE4 Effector Protein and Phytochemicals, Antimicrobial Compounds, and Chemically Synthesized Compounds
Molecular docking analysis of QNE 4 with 50 phytochemicals showed that the majority of the compounds bind to the effector protein of P. cubensis with favorable binding energies ranging from −4.4 kcal/mol (for Indole-3-aldehyde) to −9.1 kcal/mol (cucumerin-A), whereas antimicrobial compounds from different botanical sources and fungicides showed binding energies in the range of −3.4 to −12.1 (Table 7)

Interactions between the QNE4 Effector Protein and Phytochemicals, Antimicrobial Compounds, and Chemically Synthesized Compounds
Molecular docking analysis of QNE 4 with 50 phytochemicals showed that the majority of the compounds bind to the effector protein of P. cubensis with favorable binding energies ranging from −4.4 kcal/mol (for Indole-3-aldehyde) to −9.1 kcal/mol (cucumerin-A), whereas antimicrobial compounds from different botanical sources and fungicides showed binding energies in the range of −3.4 to −12.1 (Table 7) Table 7). The antimicrobial compounds obtained from botanicals namely, garlic and clove have shown a good inhibitory action on ONE4 effector protein of P. cubensis. At the same time, azoxystrobin (−8.1 kcal/mol), salicylic acid (−6.5 kcal/mol) and curzate (−6.0 kcal/mol) are the chemical compounds which exhibited the highest binding affinities. Overall, cucumerin-A (−9.1 kcal/mol) showed good inhibitory action on the ONE4 effector protein of P. cubensis out of 71 compounds tested.
Among the phytochemical compounds, cucumerin-A (−9.1 kcal/mol) exhibited the highest docking score with the QNE 4 effector protein. The ARG339, TVR290, LEU126, ASN134 amino acid residue is involved in forming four hydrogen bonds in the binding pocket of the QNE 4 effector protein. Similarly, cucumerin-B interacted with the HS110 amino acid residue by forming one hydrogen bond. Likewise, isoscoparin interacted with the SER109, HS110, and GLY217 amino acid residues by forming three hydrogen bonds, apigenin −7-O-glucoside showed an interaction with the ASN214, SER109, MET224, and GLY107 amino acids and produced four hydrogen bonds, the HIS110 amino acid shared one hydrogen bond with cucurbitacin-B, three hydrogen bonds of the SER82, SER109, and ALA108 amino acids were generated upon interaction with cucumerin-B, the SER82, SER109, and ALA108 amino acids of cucurbitacin-D were involved in forming three hydrogen bonds, the SER140 and SER82 amino acids of cucurbitacin-A interacted with two hydrogen bonds, the LYS121, GLN127, and ARG339 amino acids of cucurbitacin-I contributed three hydrogen bonds, vicenin-2 created an interaction with the SER109, HIS83, GLY107, and SER82 amino acids and generated four hydrogen bonds, and carrageenan interacted with the SER109, PHE84, HIS83, and HIS110 amino acids by forming four hydrogen bonds with the binding of the QNE4 effector protein of P. cubensis (Table 8).
In binding interactions between 15 antimicrobial compounds from different botanicals and six compounds from chemical sources and QNE 4, the docking score ranged from −3.4 to −8.1. Out of 21 compounds, the azoxystrobin (−8.1 kcal/mol) chemical compound showed the top docking score with the QNE 4 effector protein and interacted with SER109 amino acid residues to form one hydrogen bond in the binding pocket of the QNE 4 effector protein. Likely, allyl acetate created an interaction with the ASP86 and HIS83 amino acids and produced two hydrogen bonds; three hydrogen bonds of the ALA376, ARG377, and LEU381 amino acids were generated upon interaction with salicylic acid, the THR456 and ALA376 amino acids of curzate were involved in forming two hydrogen bonds, and the ARG285 and GLN165 amino acids shared two hydrogen bonds with allixin with the QNE4 effector protein of P. cubensis (Table 9).

Interactions between the Cytochrome Oxidase Subunit 1 Protein and Phytochemicals, Antimicrobial Compounds, and Fungicides
Among the 50 phytochemicals used for screening against the cytochrome oxidase subunit 1 protein, Indole-3-aldehyde has shown the lowest dock score of −4.4 kcal/mol and cucurbitacin-I have shown the highest dock score of −8.3 kcal/mol (Table 7) Cucurbitacin-I interacted with the ARG461.GLU142, LEU141, TYR108, and SER125 amino acid residues through forming five hydrogen bonds with the cytochrome oxidase subunit 1 protein of P. cubensis. Likewise, the TRP106, SER167, HIS166, SER125, and MET127 amino acids of catechin shared five hydrogen bonds, cucurbitacin-D displayed an interaction with the ARG146, TYR108, and SER125 amino acids and produced three hydrogen bonds, three hydrogen bonds of the TRP106, SER167, and HIS166 amino acids were generated upon interactions with cucurbitacin-E, swertianolin created an interaction with the SER125, HIS166, SER167, and TRP106 amino acids and developed four hydrogen bonds, the TRP168, SER125, SER167, HIS166, and TRP104 amino acids of cucurbitacin-A were involved in forming five hydrogen bonds, cucurbitacin-B interacted with the ASN152, ARG146, LEU141, VAL145, and VAL147 amino acids by forming five hydrogen bonds, the LEU141, SER125, TRP106, and SER167 amino acids of cucumerin-A contributed four hydrogen bonds, Luotonin A interacted with the TYR108 and SER125 amino acids by forming two hydrogen bonds, and cucumerin-B interacted with the LEU141, MET127, SER125, TYR108, and SER167 amino acids by forming five hydrogen bonds with the active site of the cytochrome oxidase subunit 1 protein (Table 10). Azoxystrobin interacted with the SER125, TYR108, TRP168, and TRP106 amino acid residues in forming four hydrogen bonds with the cytochrome oxidase subunit 1 protein of P. cubensis. Similarly, the SER125, TYR108, and TRP168 amino acids shared three hydrogen bonds with allyl acetate, and two hydrogen bonds of the TAM 552 and SER153 amino acids were interfaced with kresoxim methyl. The VAL147, SER125, and TYR108 amino acids of curzate contributed three hydrogen bonds with the active sites of the cytochrome oxidase subunit 1 protein of P. cubensis (Table 11).  The docking score for the 21 antimicrobial compounds and fungicides ranged from −3.2 kcal/mol (for azadiractin b) to −7.2 kcal/mol (for azoxystrobin) ( Table 7). Four compounds; azoxystrobin (−7.2 kcal/mol), allyl acetate (−6.6 kcal/mol), kresoxim methyl (−6.3 kcal/mol), and curzate (−5.3 kcal/mol) exhibited uppermost binding affinities ( Table 7). The compounds from chemical sources and antimicrobial compounds from garlic showed superior affinities with the target cytochrome oxidase subunit 1 protein of P. cubensis. Azoxystrobin interacted with the SER125, TYR108, TRP168, and TRP106 amino acid residues in forming four hydrogen bonds with the cytochrome oxidase subunit 1 protein of P. cubensis. Similarly, the SER125, TYR108, and TRP168 amino acids shared three hydrogen bonds with allyl acetate, and two hydrogen bonds of the TAM 552 and SER153 amino acids were interfaced with kresoxim methyl. The VAL147, SER125, and TYR108 amino acids of curzate contributed three hydrogen bonds with the active sites of the cytochrome oxidase subunit 1 protein of P. cubensis (Table 11).

In Vitro Evaluation of Botanicals
Evaluation of botanicals against sporangial germination of P. cubensis in vitro was carried out at different concentrations of five botanicals. The data revealed that all the treatments (botanicals) significantly inhibited the sporangial germination of P. cubensis. Among all of the botanicals tested, garlic bulb extract at 15 percent concentration showed significantly higher percentage inhibition (71.42%) followed by clove oil (64.51%) (Figure 7). The slightest inhibition of sporangial germination (33.33%) was observed at 5 percent concentration of neem (Table 12).

In Vitro Evaluation of Botanicals
Evaluation of botanicals against sporangial germination of P. cubensis in vitro was carried out at different concentrations of five botanicals. The data revealed that all the treatments (botanicals) significantly inhibited the sporangial germination of P. cubensis. Among all of the botanicals tested, garlic bulb extract at 15 percent concentration showed significantly higher percentage inhibition (71.42%) followed by clove oil (64.51%) ( Figure  7). The slightest inhibition of sporangial germination (33.33%) was observed at 5 percent concentration of neem (Table 12).

In Vitro Evaluation of Botanicals
Evaluation of botanicals against sporangial germination of P. cubensis in vitro was carried out at different concentrations of five botanicals. The data revealed that all the treatments (botanicals) significantly inhibited the sporangial germination of P. cubensis. Among all of the botanicals tested, garlic bulb extract at 15 percent concentration showed significantly higher percentage inhibition (71.42%) followed by clove oil (64.51%) ( Figure  7). The slightest inhibition of sporangial germination (33.33%) was observed at 5 percent concentration of neem (Table 12).

In Vitro Evaluation of Botanicals
Evaluation of botanicals against sporangial germination of P. cubensis in vitro was carried out at different concentrations of five botanicals. The data revealed that all the treatments (botanicals) significantly inhibited the sporangial germination of P. cubensis. Among all of the botanicals tested, garlic bulb extract at 15 percent concentration showed significantly higher percentage inhibition (71.42%) followed by clove oil (64.51%) ( Figure  7). The slightest inhibition of sporangial germination (33.33%) was observed at 5 percent concentration of neem (Table 12).

In Vitro Evaluation of Botanicals
Evaluation of botanicals against sporangial germination of P. cubensis in vitro was carried out at different concentrations of five botanicals. The data revealed that all the treatments (botanicals) significantly inhibited the sporangial germination of P. cubensis. Among all of the botanicals tested, garlic bulb extract at 15 percent concentration showed significantly higher percentage inhibition (71.42%) followed by clove oil (64.51%) ( Figure  7). The slightest inhibition of sporangial germination (33.33%) was observed at 5 percent concentration of neem (Table 12).
Among the botanicals tested, antimicrobial compounds from garlic (allyl acetate, allicin, and alliin) and clove (eugenol acetate and (E)-β-caryophyllene) showed an excellent binding affinity with the ONE4 and cytochrome c oxidase subunit 1 proteins of P. cubensis. It was reported that the alliin from garlic showed significant binding interactions with the target-Avr3a11 effector protein of Phytopthora capsici compared to the commonly used fungicides, indicating that alliin can act as a potential inhibitor of Avr3a11 [40]. It was revealed that chemical compounds from garlic have antioxidant properties by conducting molecular docking analysis of the chemical compounds of garlic against NADPH oxidase [41]. The best docking score obtained on NADPH oxidase corresponds to α bisabolol (∆G = −10.62 kcal/mol), followed by 5-methyl-1, 2, 3, 4-tetrathiane (∆G = −9.33 kcal/mol). In silico analysis of eugenol against the β-glucosidase effector protein of Fusarium solani f. sp. piperis revealed that eugenol showed promising fungicidal activity and cytotoxic activity similar to that of tebuconazole fungicide. β-glucosidase showed good binding interaction with eugenol by forming amino acid residues with Arg177 followed by a hydrogen bond with Glu596, indicating an essential role in the interactions and justifying the antifungal action of this compound [42].
Out of the six chemically synthesized compounds evaluated, oxalic acid, salicylic acid, azoxystrobin, and curzate showed good binding interactions with the effector proteins of P. cubensis. Likewise, the resistance mechanisms of QoI fungicides (azoxystrobin) were studied earlier through molecular docking studies of the cytochrome b gene of Peronophythora litchi, the causal agent of litchi downy mildew [43]. They revealed that QoI fungicides (azoxystrobin) are potent inhibitors of P. litchi. Similarly, it was mentioned that salicylic acid has antifungal and antibacterial activity. They conducted homology modeling and docking analysis of salicylic acid against the PR1 protein of Xanthomonas oryzae. The results showed that salicylic acid has more binding affinity and interaction with the PR1 protein [44]. Among the five botanicals tested, garlic bulb extract showed maximum inhibition (71.42%) followed by clove oil (64.51%). Garlic bulb extract at a 15 percent concentration showed maximum inhibition of sporangial germination (71.42%), followed by clove oil at a 5 percent concentration (71.76%). Results from earlier reports found that the concentrations of 50-1000 µg ml/1 allicin in garlic juice reduced the severity of cucumber downy mildew caused by P. cubensis by approximately 50-100 per cent under controlled conditions [42]. The volatile antimicrobial substance allicin (dially thiosulphinate) from garlic (Allium sativum) at concentrations 50-100 µg/mL reduced the severity of P. cubensis on cucumber by approximately 50-100% [45]. In addition, clove oil at 4 percent effectively reduced the downy mildew incidence in cucumber [46].