
==== Front
Curr Res Microb Sci
Curr Res Microb Sci
Current Research in Microbial Sciences
2666-5174
Elsevier

S2666-5174(24)00052-X
10.1016/j.crmicr.2024.100270
100270
Articles from the special issue: Host and pathogen determinants mediating host-pathogen outcome in health and disease, edited by Pablo González, Luisa Duarte and Leandro Carreño
Integrating In-silico and In-vitro approaches to identify plant-derived bioactive molecules against spore coat protein CotH3 and high affinity iron permease FTR1 of Rhizopus oryzae
Gupta Lovely a
Kumar Pawan b
Sen Pooja a
Sharma Aniket ac
Kumar Lokesh a
Sengupta Abhishek a
Vijayaraghavan Pooja vrpooja@amity.edu
a⁎
a Amity Institute of Biotechnology, Amity University Uttar Pradesh, Sector-125, Noida, 201301, Uttar Pradesh, India
b School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India
c Department of Animal Science, University of Wyoming, Laramie, WY, 82071, USA
⁎ Corresponding author. vrpooja@amity.edu
23 8 2024
2024
23 8 2024
7 100270© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Highlights

• Rhizopus oryzae causes mucormycosis, a deadly fungal infection with high mortality rates.

• Current antifungal drugs face resistance issues, making surgical treatment often necessary.

• Virulence factors in R. oryzae, such as CotH3 and FTR1, are potential therapeutic targets.

• In-silico screening identified eugenol and isoeugenol as promising antifungal molecules.

• In-vitro studies confirmed the antifungal activity of eugenol and isoeugenol against R. oryzae.

Rhizopus oryzae is one of the major causative agents of mucormycosis. The disease has a poor prognosis with a high mortality rate, and resistance towards current antifungal drugs poses additional concern. The disease treatment is complicated with antifungals; therefore, surgical approach is preferred in many cases. A comprehensive understanding of the pathogenicity-associated virulence factors of R. oryzae is essential to develop new antifungals against this fungus. Virulence factors in R. oryzae include cell wall proteins, spore germination proteins and enzymes that evade host immunity. The spore coat protein (CotH3) and high-affinity iron permease (FTR1) have been identified as promising therapeutic targets in R. oryzae. In-silico screening is a preferred approach to identify hit molecules for further in-vitro studies. In the present study, twelve bioactive molecules were docked within the active site of CotH3 and FTR1. Further, molecular dynamics simulation analysis of best-docked protein-ligand structures revealed the dynamics information of their stability in the biological system. Eugenol and isoeugenol exhibited significant binding scores with both the protein targets of R. oryzae and followed the Lipinski rule of drug-likeness. To corroborate the in-silico results, in-vitro studies were conducted using bioactive compounds eugenol, isoeugenol, and myristicin against R. oryzae isolated from the soil sample. Eugenol, isoeugenol exhibited antifungal activity at 156 µg/mL whereas myristicin at 312 µg/mL. Hence, the study suggested that eugenol and isoeugenol could be explored further as potential antifungal molecules against R. oryzae.

Graphical abstract

Image, graphical abstract

Keywords

Bioactive molecules
CotH3
FTR1
Rhizopus oryzae
Virulence
==== Body
pmcIntroduction

Mucormycosis is a serious but rare fungal infection, is caused by a group of fungi belonging to the order Mucorales, with reported mortality rates ranging from 50 % to 100 % (Inglesfield et al., 2018). A higher mortality rate is observed in patients with neutropenia, solid organ transplants, iron overload, and uncontrolled diabetes mellitus (Petrikkos et al. 2012; Claustre et al. 2020). Globally, the prevalence of mucormycosis varies between 0.005 – 1.7 per million population; the reported prevalence in India was 0.14 per 1000 individuals in 2019–2020 (Chander et al. 2018; Skiada et al. 2020). India reported the highest burden of mucormycosis in patients with COVID-19 (Aranjani et al. 2021).

The most common causative agents of mucormycosis are Rhizopus, Mucor, and Lichtheimia species. Rhizopus spp. is the most common among them, which releases large numbers of airborne conidia. Rhizopus oryzae var arrhizus is the major Rhizopus spp. causing mucormycosis followed by R. delemar, R. microspores, Mucor regularis, and Rhizomucor (Chander et al. 2018; Prakash and Chakrabarti 2019). Various virulence factors are crucial in the R. oryzae infection process, including, spore coat protein (CotH), high-affinity iron permease (FTR1), alkaline Rhizopus protease enzyme (ARP), calcineurin, and serine and aspartate proteases (Ibrahim et al., 2010; Morales-Franco et al. 2021). Among these, spore coat protein homolog CotH3 has been detected exclusively on the spore surface of the order Mucorales. CotH3 plays a key role in invasion in the mucormycosis pathogenesis by disrupting and damaging immune cells (Gebremariam et al. 2014). It's a kinase protein present on the surface of the conidia that promotes adhesion to the host endothelial cell surface through its binding to the glucose-regulator protein 78 (GRP78) during host cell invasion. When endothelial cells are exposed to acidosis and elevated levels of glucose and iron (in hyperglycemia and diabetes ketoacidosis), GRP78 expression increases, leading to fungal invasion and damage to endothelial cells in a receptor-dependent manner (Roilides et al. 2014). CotH3 gene and protein are prime targets to restrict the virulence of R.oryzae under hyperglycemia and other forms of acidosis.

In patients with diabetes ketoacidosis, treatment with iron deferoxamine increases iron availability, thereby increasing the risk for mucormycosis (Ibrahim and Kontoyiannis 2013). Virulence factor FTR1 has a crucial role in iron uptake and transportation at the time of infection. The acquisition of iron is a crucial pathogenic event for opportunistic fungi such as R. oryzae (Stanford and Voigt 2020). Different mechanisms of iron uptake have been reported in fungi. The reductive system of iron uptake involves activity of an external reductase of ferric iron and subsequently transportation by a complex of multicopper oxidase and ferrous permease (Knight et al. 2002; Fu et al. 2004). FTR1 and CotH3 proteins are highly conserved within the order Mucorales, thereby becoming promising drug targets for mucormycosis treatment.

The treatment of mucormycosis is compromised by a limited spectrum of effective antifungal drugs (https://www.cdc.gov/fungal/diseases/mucormycosis/treatment.html). Only three antifungal drugs are currently approved namely, lipid formulations of amphotericin B, posaconazole, and isavuconazole (Roemer and Krysan 2014). However, an alarming increase in resistant fungal strains and severe side effects of amphotericin B (hepatotoxicity, nephrotoxicity, and myelotoxicity) pose a severe challenge to therapeutic strategies (Dannaoui 2017; Sen et al. 2022). Therefore, new drug targets should be explored to develop safe and effective antifungals for the treatment of mucormycosis. Medicinal plants represent a vast source of new pharmacologically active molecules for the treatment of fungal diseases (Chathurdevi and Gowrie 2016; Adeleke and Babalola 2021). They are source of many active molecules including eugenol, isoeugenol, alpha-pinene, camphene, 1,8-cineole, elemicin, limonene, methyl-eugenol, myristicin, and β-terpineol, with potential antimicrobial activities (Park et al. 2012; Torbati et al. 2014; Şimşek and Duman 2017).

In-silico approach is a knowledge-based method that helps to select bioactive molecules with a high likelihood of biological activity. The approach can focus on the molecules with relevant biological effects directly retrieved from the published literature (Rollinger et al. 2009). Docking study proposes protein-ligand binding characteristics; and the ligand that performs significantly in-silico activity can be used as a promising starting molecule for the in-vitro experimental work, preferably by target binding assays. The present study aimed to screen bioactive compounds against virulence proteins CotH3 and FTR1 of R. oryzae via in-silico approach, and in-vitro evaluation of antifungal activity of bioactive compounds eugenol, isoeugenol, and myristicin.

Materials and methods

Preparation of ligands

The identified molecules listed in Table S1 were originally derived from the hexane extract of Myristica fragrans (Hoda et al. 2020) and were selected based on their reported bioactivity (Kuete 2017; Hoda et al. 2020); The 3D chemical structures of 12 selected bioactive molecules were retrieved from the PubChem database (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4702940/) in Spatial Data File (SDF) format. Using OpenBabel tool, input file of ligands in the SDF file format (.sdf) was converted to the PDB file formats (.pdb) for molecular docking (O'Boyle et al. 2011).

Search for the target sequence, structures, and preparation

The protein sequences of CotH3 (accession no KAG1547117.1) and FTR1 (accession no AAQ24109.1) of R. oryzae were retrieved from NCBI (https://www.ncbi.nlm.nih.gov/). As their 3D structures were not available in the Protein Data Bank (PDB), homology modelling was conducted using Swiss Model via ExPasy web server (https://swissmodel.expasy.org/) with template alignment. Tertiary structures of modelled protein were evaluated using PROCHECK software. Modelled protein structures were further prepared using Swiss PDB viewer (SPDBV-4.10) version with charge assignment, solvation parameters and fragmental volumes (https://spdbv.unil.ch/). The protein molecules were further optimized using AutoDock4 Tool for the molecular docking (Morris et al., 2009).

Molecular docking

The docking analysis was performed by molecular docking program AutoDock4.2.3 software (Morris et al. 2009). The grid box (60 × 60 × 60) was set to cover the whole protein. Docking calculations were carried out with the Lamarckian genetic algorithm. The total number of docking runs was set to 50 with other default values during each docking run. Poses were further clustered utilizing all-atom root mean square deviation (RMSD) cut-off of 0.3 Å to remove redundancy with an average 20 cluster representatives. The protein structure was inflexible at all steps. All the docking poses and interaction analysis were produced via Discovery Studio Visualizer programs (Şimşek and Duman 2017).

Absorption, distribution, metabolism, excretion, and toxicity (ADME-Tox) profile prediction

The ADME-Tox profiles of selected molecules were predicted by SwissADME program (http://www.swissadme.ch/index.php; Sharma et al. 2020). The major ADME-Tox properties parameters taken in this study were: molecular weight, H-bond acceptor, H-bond donors, predicted octanol/water partition coefficient (MLogP), rotatable bonds, topological polar surface area (TPSA), blood brain barrier (BBB) permeant and oral bioavailability score.

Molecular dynamics (MD) simulation and analysis

MD simulation was performed using GROMACS software to analyze conformational dynamics of the unbound and bound state of FTR1 and CotH3 protein. GROMOS force field (Schmid et al. 2011) was used for protein and PRODRG web-server (http://davapc1.bioch.dundee.ac.uk/cgi-bin/prodrg/) was used to parametrised the ligand molecules during simulation. The cubic simulation box of 10 Å was prepared considering the protein at center, and the box was filled with TIP3P water molecules (Pekka and Nilsson 2001) for solvation under periodic boundary conditions. Some water molecules were replaced by counter ions to neutralize systems. Particle Mesh Ewald (PME) method was used for calculating all electrostatic interactions and 2 fs time step was used with the SHAKE algorithm to constrain the hydrogen bonds.

The simulation box was initially minimized for 500,000 steps using the steepest descent and conjugate gradient methods. The system was then gradually heated from 0 to 300 K in six stages, followed by a 1 ns equilibration run under NVT ensemble. The final 100 ns production run was conducted under NPT ensemble with an integration time step of 2.0 fs. Trajectories were saved every 100 ps and analysed using GROMACS trajectory module for the RMSD, root mean square fluctuation (RMSF), and solvent accessible surface areas (SASA) (Lindahl et al. 2001).

Fungal isolation, its molecular characterization and antifungal susceptibility

Ten soil samples were collected from various agricultural fields in the Haryana region and stored in sterile polythene bags. Soil samples were processed and inoculated on potato dextrose agar (PDA) following the method described by Sen et al. (2023). Inoculated PDA plates were then incubated at 28 ± 2 °C for 5 days, and fungal growth was observed. All samples were processed in biological triplicate. Fungal isolates were identified based on morphological and microscopic characteristics using standard mycological reference charts (Dugan 2012; Barnett and Hunter 2006). The identified Rhizopus isolate was subcultured and subjected to molecular identification. The full-length 18S internal transcribed spacer (ITS) region was amplified using the universal primers ITS1 and ITS4 (White et al., 1990) and the PCR-amplified ITS region was sequenced by Sanger's sequencing. The obtained sequences were compared to the sequences in the GenBank database (www.ncbi.nlm.nih.gov.in) using BLAST analysis, and identification was confirmed when a sequence identity of 99–100 % was observed. Minimum inhibitory concentration (MIC) of eugenol, isoeugenol, myristicin and triazole drugs itraconazole, voriconazole, posaconazole and polyene antifungal amphotericin B against R. oryzae was determined by using CLSI M38-A2 broth microdilution method (Alexander 2017). Two-fold dilutions were carried out in a 96-well microplate to obtain concentrations ranging from 32 to 0.0625 μg/mL for itraconazole, voriconazole, posaconazole, and amphotericin B; 5000- 9.76 µg/mL for eugenol, isoeugenol, and myristicin. The growth in each well was compared with that of the positive control; the plate was incubated at 28 ± 2 °C for 5 days. The experiment was conducted in triplicates. The MIC is defined as the lowest concentration of the compound, which completely inhibits microbial growth (Alexander 2017). The results were expressed in micrograms per milliliters (µg/mL).

Statistical analysis

For the statistical analysis, one sample t-test was used to analyse the MIC values of antifungal drugs and bioactive compounds tested against R. oryzae. All experiments were conducted in biological duplicates and technical triplicates. Statistics were performed using GraphPad Prism software 8.0.2.263 version.

Results

Homology model of R. oryzae CotH3 and FTR1 and its accuracy assessment

R. oryzae CotH3 and FTR1 proteins were modelled via SwissModel. The 3D structure of CotH3 was modelled based on the template PDB ID 5JD9 (Fig.1A), while FTR1 was modelled using template PDB ID 4JR9 chain A (Fig. 1C). To assess the stereo-chemical quality and accuracy of the models, PROCHECK was employed and results were presented in Ramachandran plots (Fig. 1, Fig. 1). For CotH3, 84.3 % of residues were in the most favoured regions, 14.6 % residues in additional allowed regions, and only 0.6 % residues in disallowed regions. Similarly, in the FTR1 protein, 85.1 % of residues were in the most favoured regions, 12.3 % residues were in additional allowed regions and no residues were found in disallowed regions. Overall, the homology-modelled proteins demonstrated good quality based on the Ramachandran plot analysis.Fig. 1 Ribbon structure of the spore coat protein CotH3 (A) and high-affinity iron permease FTR1 (C) of Rhizopus oryzae; and Ramachandran plot analysis of CotH3 (B) and FTR1 (D). Filled black squares are all non-glycine and proline residues, filled black triangles are all glycine (non-end) and disallowed residues are red portions.

Fig 1

Molecular docking

Molecular docking was performed to evaluate the interaction of 12 plant-derived molecules having antimicrobial activity with CotH3 and FTR1 proteins of R. oryzae. The docking was directed at the reported catalytic site of CotH3 protein where it binds to GRP78 host receptor during infection. The amino acid residue present at the docking pocket was Met364. The coordinates of Met364 residue were x-axis: 21.766, y-axis: 51.757, and z-axis: 29.357. For FTR1 protein, blind docking was employed to evaluate the probable binding site for ligands. The strategy of AutoDock4- based blind docking included a search over the entire surface of the protein for binding sites. The binding affinities of shortlisted molecules at the active site were assessed. The docking scores are listed in Table S2 along with their interactions.

Interactions of 12 bioactive molecules with the active site of CotH3 were analysed using AutoDock4. The best docking score of −6.93 Kcal/mol with three hydrogen bonds (Val 420 and Met 421) (Fig. 2) was given by myristicin at the catalytic site of CotH3 protein followed by isoeugenol (−6.89 Kcal/mol). In addition, eugenol and methyl-eugenol exhibited binding affinity of −6.75 Kcal/mol and −6.60 Kcal/mol, respectively. Eugenol formed two hydrogen bonds (Val 420, and Met 421), and methyl eugenol formed three hydrogen bonds: two with Val 420 and one with Met 421 (Fig. 2). Isoeugenol with more negative binding affinity formed four hydrogen bonds (two with Val 420, one with Met 364 and one with Met 421) at the catalytic site of CotH3 of R. oryzae (Fig. 2). The catalytic site of CotH3 possesses amino acids Met 364, Gln 366, which forms hydrogen bond and show van der Waal interaction with the eugenol, isoeugenol, methyl-eugenol and myristicin. β-Terpineol formed only one hydrogen bond with a docking score of −5.95 Kcal/mol, which is a very low binding affinity at the catalytic site. Limonene, elemicin, 1,8-cineole, camphene, and α-pinene displayed binding affinities of −6.06, −6.38, −5.73, −5.90 Kcal/mol and −5.50 Kcal/mol, respectively. However, these compounds did not form any hydrogen bonds at the active site of CotH3. Linoleic acid and oleic acid showed a low binding affinity with less negative docking score.Fig. 2 Binding interactions of eugenol, isoeugenol, methyl-eugenol, and myristicin with the active site of CotH3 protein of Rhizopus oryzae.

Fig 2

The binding affinity was reduced for the FTR1 protein, although all molecules docked at the same predicted active site on the protein. The highest binding affinity was shown by β-terpineol (−5.6 Kcal/mol) followed by eugenol and isoeugenol (−5.43 and −5.46 Kcal/mol, respectively; Fig. 3). Eugenol and isoeugenol formed one hydrogen bond at the predicted active site with Thr 61, whereas β-terpineol formed one hydrogen bond with Leu 49. Elemicin, methyl-eugenol, myristicin, linoleic acid, and oleic acid binding affinities were −5.21 Kcal/mol, −5.22 Kcal/mol, −5.30 Kcal/mol, −5.39 Kcal/mol and −5.33 Kcal/mol, respectively. Linoleic and oleic acids formed two hydrogen bonds with Ala 32, Leu 31 and Trp 27, Lys 33, respectively. Limonene showed a binding affinity of −5.04 Kcal/mol but did not form any conventional hydrogen bond. Camphene, 1,8-cineole, and α-pinene gave negative binding affinity below −5.00 Kcal/mol and, were not considered for further analysis.Fig. 3 Binding interactions of β-Terpineol, eugenol and isoeugenol with the active site of FTR1 protein of Rhizopus oryzae.

Fig 3

ADME-Tox profile prediction

The ADME-Tox study of five selected molecules (eugenol, isoeugenol, methyl-eugenol, β-terpineol, and myristicin) was evaluated for their drug-likeness properties. Table 1 lists key criteria for a molecule to possess drug-like properties via oral route. All the molecules had molecular weight <500 g mol-1, MlogP value <5, rotatable bonds <10, and TPSA <140Å2. Only methyl eugenol and myristicin did not possess hydrogen bond donors; eugenol, isoeugenol, and β-terpineol followed Lipinski's rule of five, Veber and Egan's rules. None of the studied molecules violated the drug likeness properties. Gastrointestinal absorption and brain access are two more crucial pharmacokinetic properties to determine a molecule as drug candidate. In relation, the BOILED-Egg predictive model showed all molecules in the yellow region, with the highest probability of permeating to the brain. The oral bioavailability score for all molecules was 0.55.Table 1 Physicochemical and drug-likeness properties of CotH3 and FTR1 protein inhibitors determined by SwissADME.

Table 1Bioactive molecules	Molecular Formula	Molecular weight (g mol-1)	LogP	H-bond donor	H-bond acceptor	Rotatable bonds	TPSA	Molar refractivity	Violation* of Lipinski, Veber, Egan's rule	
Eugenol	C10H12O2	164.20	2.25	1	2	3	29.46Å2	49.06	No	
Isoeugenol	C10H12O2	164.20	2.41	1	2	2	29.46Å2	49.86	No	
Methyl Eugenol	C11H14O2	178.23	2.58	0	2	4	18.46Å2	53.53	No	
Myristicin	C11H12O3	192.21	2.49	0	3	3	27.69Å2	53.10	No	
β-Terpineol	C10H18O	154.25	2.44	1	1	1	20.23Å2	48.80	No	
⁎ Molecular weight (<500 g mol-1), LogP (<5), H-bond donor (<5), H-bond acceptor (<10), Rotatable bonds (<10), Topological polar surface area (TPSA <140Å2).

Molecular dynamics simulation

MD simulation was performed for 100 ns simulation time for six systems which included three from CotH3 and three from FTR1 (only receptor and two docked ligand-protein complex). Based on the binding affinities and ADME-Tox parameters, eugenol and isoeugenol ligands were selected for further MD simulation studies.

The RMSD (Fig. 4A and B) and SASA (Fig. 4E and F) trajectories were calculated to assess the protein-ligand stability in the presence of a bound ligand while RMSF (Fig. 4C and D) was employed to understand the average fluctuation of protein residues. Fig. 4 displays the RMSD, RMSF and SASA plots for CotH3 and FTR1 systems. RMSD distribution for eugenol-bound CotH3 complex is relatively higher than the receptor only (Fig. 4A). The probing showed that the major contribution in fluctuation was of the coiled secondary structure situated at the N-terminal side of the protein. The RMSF plot (Fig. 4C) displayed that 50–200 amino acid region have very high fluctuation compared to receptor-only for that region. We have further investigated the docked ligand position in the CotH3 binding site. For both CotH3 complexes, SASA is nearly consistent till 100 ns duration whereas in case of FTR1 complex, it gets reflected after eugenol binding depicting some conformational changes (Fig. 4E and F). Centre-mean distance between docked ligands and binding site residue (Trp 323) was calculated and Fig. S1 demonstrated that eugenol and isoeugenol remained in the binding site of the protein and after 100 ns simulation, the distance plot for both complexes plateaued with very minimum deviation. Results suggested that eugenol and isoeugenol bound CotH3 complexes achieved stability, despite relatively high RMSD deviation which is mainly of coiled secondary structure.Fig. 4 Root Mean Square Deviation RMSD values of complexes during 100 ns MD simulations (A,B); Root Mean Square Fluctuation RMSF (C,D), and Solvent Accessible Surface Areas SASA (E,F) plots of the CotH3 and FTR1 of Rhizopusoryzae.

Fig 4

In case of FTR1 complexes, FTR1 model structure has four helices, three helices clustered together while the fourth helix is connected with coiled secondary structure (17–50 residues) with the rest helices. Due to the flexibility incorporated by the >30 residues long coiled region, this region and attached helix do not directly engage with the binding of the docked ligands. So, we have recalculated the RMSD after excluding the 1–50 residues from both complexes and Fig. S2 shows the RMSD plot for 51–130 region. This calculation reflects that both complexes are very well stable and do not show much deviation upon ligand binding. Hence, CotH3 and FTR1 docked complexes are stable and can be used for further understanding.

Isolation of R. oryzae, its molecular identification and antifungal susceptibility

Among all ten soil samples processed, only one Rhizopus isolate was identified based on its morphological and microscopic characteristics (Fig 5). The sequences obtained from amplification of conserved ribosomal ITS region were compared with BLAST Programme on NCBI. The sequence was identified as R. oryzae and submitted in the GenBank (Accession number OQ868363). MIC of eugenol and isoeugenol was found to be 156 μg/mL whereas MIC of myristicin was calculated as 312 μg/mL. MIC of itraconazole, voriconazole, posaconazole and amphotericin B was 4 μg/mL, 32 μg/mL, 2 μg/mL, and 16 μg/mL, respectively (Fig. 6).Fig. 5 Rhizopus oryzae colony morphology on potato dextrose agar (A) and microscopic images 40X (B) and 100X (C) magnifications.

Fig 5

Fig. 6 Graphical representation of Minimum inhibitory concentrations (MICs; µg/mL) of current antifungal drugs (Amphotericin B, Posaconazole, Voriconazole and Itraconazole) and bioactive compounds (Myristicin, Isoeugenol and eugenol) against R. oryzae isolate.

Fig 6

Discussion

Rhizopus causes pulmonary infections in patients with hematologic malignancies, and it also causes rhino orbital/cerebral mucormycosis in patients suffering from diabetic ketoacidosis (DKA). Elevated concentrations of glucose, iron, and ketone bodies, which occur in patients with hyperglycemia and DKA, enhance GRP78 and CotH3 expression, leading to augmented fungal invasion and damage in the host cell. R. oryzae thrives under high glucose and acidic conditions and interacts with the host cell receptor GRP78 via CotH3 (Liu et al. 2010; Gebremariam et al. 2014). CotH proteins are present in Mucorales but are absent in other medically important fungi such as Aspergillus and Candida. These proteins are the prime targets for mucormycosis, which have not been studied in detail.

In the present study, homology modelling was performed to retrieve 3D structure of CotH3 protein via Swiss-model web server. The CotH3 protein has multiple α-helical domains which provide a structural scaffold to it. However, the protein in its secondary structure differs considerably in the extent of its β-sheet conformation (Gebremariam et al. 2014). The CotH3 exhibited an extended docking-predicted GRP78 contact point, with minimal β-sheet stabilization. The amino acid sequence MGQTNDGAYRDPTDNN of CotH3 is a highly conserved sequence used to raise antibodies in mouse model. The antibodies inhibited endothelial cell invasion in vitro and protected mice against mucormycosis. In the present study, molecular docking was performed using CotH3 protein as a target and twelve bioactive molecules as ligands, selected from our previous study. The current study showed, that out of twelve listed molecules, myristicin, eugenol, isoeugenol and methyl-eugenol interacted at the active site of CotH3 protein with negative binding energy and formed at least two hydrogen bonds at the binding site. The binding affinity revealed the interaction and strength by which a compound interacts with and binds to the active site of the target protein. Compounds having binding affinity of −6.5 Kcal/mol or less are considered good inhibitors of enzymatic activities (Shah et al. 2020).

High iron affinity system is identified as a key molecular virulence determinant, which contains ferric reductase, ferroxidase and permease (Hassan and Voigt 2019). FTR1 protein was also evaluated as another crucial virulence factor of R. oryzae. This system reduces free ferric ion by ferric reductase to obtain a more soluble iron form. The reduced iron is again oxidised by ferroxidase and is recognised by FTR1 protein which in the end concedes the transport inside the cell (Navarro-Mendoza et al. 2018). It is also regulated by the environmental level of iron and is activated when there is low availability of it in the surrounding environment. Lack of iron has been shown to reduce virulence, trigger growth defects, and induce apoptosis in the R. oryzae (Ibrahim and Kontoyiannis 2013). Since there was no data available for the active site of the FTR1 protein, blind docking was conducted to find the binding efficiency of plant-derived molecules with the protein. In the current study, the binding affinity between FTR1 and ligands was not significant but all the docked ligands found same active site on the protein i.e., Thr 61 residue. The ligand β-terpineol followed by eugenol and isoeugenol showed binding affinity of −5.60, −5.43 and −5.46 Kcal/mol, respectively. The lowest autodock binding energy and best interactions were used to ascertain the compound with the best conformation (Vikram and Mishra 2018; Kamboj et al. 2022).

In the present study, eugenol and isoeugenol displayed binding affinities with CotH3 and FTR1. The chemical structure of eugenol and isoeugenol differ in the position of the double bond in the propene side chain (Koeduka et al. 2008). Isoeugenol interacted with CotH3 protein binding site forming 4 hydrogen bonds, whereas eugenol formed 2 hydrogen bonds, having similar binding affinity. Further, MD simulation was conducted to examine the dynamical stability of the CotH3 and FTR1 receptor protein in the presence of the docked ligands. In the case of CotH3 complexes, CotH3-eugenol complex attended more RMSD and SASA values along the simulation time compared with CotH3-isoeugenol complex and CotH3 receptor itself. However, further trajectory analysis showed that coiled secondary structure was mainly responsible for this higher RMSD and in both CotH3 complexes; docked ligands remain stable on the proposed binding site. In case of FTR1 complexes, FTR1-eugenol complex achieved more RMSD compared to FTR1-isoeugenol complex. N-terminal helix connects with other helices along with nearly 30 residues long random coiled structure and this was found to be a major determinant in high RMSD in case of FTR1-eugenol complex. Excluding this region during RMSD calculation showed both complexes are well stable during 100 ns simulation. This simulation result further suggested that docked ligand molecules stabilise both receptor complexes.

Docking studies suggested that eugenol and isoeugenol exhibited efficacy against both the target virulence proteins of R. oryzae. The phenolic compounds can express their antifungal effect by targeting host-pathogen adhesion, reducing the fluidity of the membrane, and inhibiting cell wall synthesis or energy metabolism (Gupta et al. 2018; Donadio et al. 2021). Eugenol and isoeugenol interfere with microbial membrane functions or suppress virulence factors (toxins, and enzymes involves in various biosynthetic pathways), and inhibit biofilm formation (Gupta et al. 2022). MIC of eugenol and isoeugenol was 156 μg/mL against R. oryzae, which is lower than MIC reported against filamentous fungi Aspergillus fumigatus (Gupta et al. 2022). Natural sources are being actively investigated because of their importance in drug discovery (Newman and Cragg 2012; Atanasov et al. 2021). For drug development processes, complete knowledge of the interaction of a drug candidate with its molecular target is useful. As per the literature, structural modifications in the bioactive compounds would lead to enhance the drug efficacy and reduce their side-effects (Goswami et al. 2022).

Mucorales typically exhibit intrinsic resistance to certain antifungal drugs (itraconazole, fluconazole (Diflucan), voriconazole) (Caramalho et al. 2017) and there is very limited data related to their antifungal susceptibility and MIC values of antifungals (Espinel-Ingroff et al. 2015; Sipsas et al. 2018; Yousfi et al. 2019; Dogra et al. 2022). This significantly restricts the options for antifungal treatments. In the present study, R. oryzae isolate demonstrated resistance to amphotericin B, while showing susceptibility to posaconazole. There were high MIC values of voriconazole and itraconazole against R. oryzae isolate, which supports the intrinsic resistance to voriconazole and itraconazole that has previously been documented.

A good pharmacokinetics property plays an important role in the new drug candidate that should be evaluated in the process of drug development (Can et al. 2017). The ADME-Tox study of eugenol, isoeugenol, methyl-eugenol, β-terpineol, and myristicin displayed no violations of Lipinski's rule of five, Veber's rule and Egan's rule (Veber et al., 2002). BBB index was in favor of oral bioavailability of molecules and BBB permeation of compounds showed evidence for brain penetration which lies inside the yellow region of BOILED egg model. The BOILED egg model depicted the predictive power of gastrointestinal absorption and brain permeation (Daina and Zoete 2016). Furthermore, in-silico prediction of ADME properties of compounds that fall within the range could be used to evaluate the suitability of compounds as potential drugs.

Conclusion

In conclusion, this study identifies eugenol and isoeugenol as potential antifungal molecules against R. oryzae. Through in-silico screening and MD simulations, both molecules exhibited significant binding scores with key virulence factors CotH3 and FTR1, while adhering to drug-likeness criteria. In-vitro studies further confirmed their antifungal activity. These findings highlight the promise of eugenol and isoeugenol as potential therapeutic options for combating mucormycosis. Nevertheless, additional research and clinical investigations are essential to fully understand their efficacy and safety profiles, ultimately paving the way for novel and effective antifungal treatments against this life-threatening infection.

Author's contributions

LG, AS and PS conducted literature search, performed experiments and drafted the manuscript; PK and AS performed molecular dynamics and simulations analysis; LK conducted the fungi identification and PV conceptualised the idea and critically analysed the results and manuscript.

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.

Appendix Supplementary materials

Image, application 1

Data availability

Authors have mentioned the link in the attached manuscript file.

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.crmicr.2024.100270.
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