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Frontiers in Virology ·

Volume 6 - 2026 | https://doi.org/10.3389/fviro.2026.1857484

In silico identification of organosulfur compounds from Allium ascalonicum L. as potential inhibitors of influenza A (H5N1): integrated DFT, docking, and molecular dynamics analysis

  • Rangga Adhi Prastika1,2
  • Alifaghi Pahlevi Ervianto Putra2
  • Muhammad Alesha Fadhana2
  • Volta Kellik Setiawan1,3
  • Moh. Royhan Afnani1,4
  • Sukma Sahadewa5
  • Fara Disa Durry6
  • Aswin Rafif Khairullah7
  • I Made Dwi Mertha Adnyana8
  • Abhishek Singh9
  • Maksim Rebezov10,11
  • Abdugani Abdurasulov10
  • Vikash Jakhmola9
  • Nitin Sharma12
  • Nadezhda V. Kupaeva13
  • Imam Siswanto14*corresponding author
  • Arif Nur Muhammad Ansori1,15*corresponding author
  • Arli Aditya Parikesit1,16
  1. 1Drug and Vaccine Innovation Research Group, Virtual Research Center for Bioinformatics and Biotechnology, Surabaya, Indonesia
  2. 2Department of Nanotechnology Engineering, Faculty of Advanced Technology and Multidisciplinary, Universitas Airlangga, Surabaya, Indonesia
  3. 3Faculty of Teacher Training and Education, Universitas Mulawarman, Samarinda, Indonesia
  4. 4Master Program of Biology, Faculty of Biology, Universitas Gadjah Mada, Yogyakarta, Indonesia
  5. 5Faculty of Medicine, Universitas Wijaya Kusuma Surabaya, Surabaya, Indonesia
  6. 6Faculty of Medicine, Universitas Pembangunan Nasional Veteran Jawa Timur, Surabaya, Indonesia
  7. 7Research Center for Veterinary Science, National Research and Innovation Agency, Bogor, Indonesia
  8. 8Department of Microbiology and Parasitology, Faculty of Medicine and Health Sciences, Universitas Jambi, Jambi, Indonesia
  9. 9Uttaranchal Institute of Pharmaceutical Sciences, Uttaranchal University, Dehradun, India
  10. 10Department of Veterinary Medicine and Biotechnology, Osh State University, Osh, Kyrgyzstan
  11. 11Faculty of Biotechnology and Food Engineering, Ural State Agrarian University, Yekaterinburg, Russia
  12. 12Department of Pharmaceutics, Amity Institute of Pharmacy, Amity University Uttar Pradesh, Noida, India
  13. 13V. M. Gorbatov Federal Research Center for Food Systems of the Russian Academy of Sciences, Moscow, Russia
  14. 14Department of Chemistry, Faculty of Science and Technology, Universitas Airlangga, Surabaya, Indonesia
  15. 15Postgraduate School, Universitas Airlangga, Surabaya, Indonesia
  16. 16School of Health and Life Sciences, i3L University, Jakarta, Indonesia
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Abstract

Influenza A (H5N1) remains a major public health concern due to its high pathogenicity and ongoing viral evolution, underscoring the need for novel antiviral candidates. In this study, we performed an integrated in silico evaluation of organosulfur compounds derived from Allium ascalonicum L. (shallot) cultivated in the Tolaki-Mekongga region, Sulawesi, Indonesia, targeting key viral proteins including polymerase (PB2), nucleoprotein (NP), and neuraminidase (NA). Density functional theory (DFT) analyses were conducted to characterize the electronic properties of the compounds, while PASS prediction indicated moderate potential antiviral activity for Propanethiol and Dipropyl disulfide. Pharmacokinetic profiling suggested acceptable ADMET properties for several candidates. Molecular docking revealed favorable binding conformations across all targets, with γ-glutamyl-S-propenylcysteine exhibiting the most favorable binding energies among the evaluated organosulfur compounds (PB2: -4.9 kcal/mol; NP: -5.8 kcal/mol; NA: -5.2 kcal/mol). These values were comparable to those of oseltamivir and favipiravir for NP and NA, although weaker binding was observed against PB2. Subsequent simulations of molecular dynamics demonstrated stable protein–ligand complexes over 100 ns, further supporting the predicted binding interactions. Consistently, MM-GBSA calculations indicated favorable binding free energies, particularly for γ-glutamyl-S-propenylcysteine (PB2: -30.52 ± 0.29 kcal/mol; NP: -22.76 ± 0.12 kcal/mol; NA: -26.13 ± 0.35 kcal/mol). Overall, these findings suggest that shallot-derived organosulfur compounds, especially γ-glutamyl-S-propenylcysteine, exhibit potential for interaction with H5N1 viral targets and may warrant further investigation as antiviral candidates. Experimental validation through in vitro and in vivo studies is required to confirm their biological activity and therapeutic potential.

Keywords

Introduction

The emergence and rapid spread of the highly pathogenic influenza A (H5N1) virus continue to pose a significant global health threat, with the potential to trigger devastating pandemics. Viruses can undergo rapid genetic changes through mutation, genetic reassortment, and recombination, leading to the emergence of new variants with enhanced virulence, transmissibility, or drug resistance (1, 2). The severity of this threat is exemplified by recent cases in the United States, including the first severe H5N1 infection linked to backyard poultry exposure in Louisiana (3). The identification of different viral genotypes (D1.1 and B3.13) in human cases, along with infections detected across multiple states and neighboring countries, underscores the evolving nature of the virus and its ability to cross species barriers (4, 5). Despite the availability of antiviral treatments such as oseltamivir and favipiravir, the emergence of resistance to oseltamivir (6), together with the potential for resistance to favipiravir despite its high genetic barrier (7), highlights the need for alternative therapeutic agents. Current antiviral drugs also face several limitations, including the development of viral resistance, adverse side effects, high treatment costs, and limited availability (8, 9). Therefore, the discovery of novel therapeutic agents is essential for effectively combating the influenza A (H5N1) virus.

Natural compounds have gained increasing attention in antiviral research because of their diverse biological activities and potentially fewer side effects. Numerous studies have demonstrated that plant-derived compounds possess promising therapeutic potential owing to their complex chemical structures, multiple mechanisms of action, and long history of traditional medicinal use (10–15). These plant extracts often contain a variety of bioactive constituents, including flavonoids, alkaloids, terpenoids, polyphenols, and organosulfur compounds, which have demonstrated antiviral activity against diverse viral pathogens. Natural compounds can interfere with multiple stages of the viral life cycle, making them particularly attractive candidates for antiviral development (16). Furthermore, these compounds can target critical stages of viral infection, including viral attachment, entry, uncoating, replication, assembly, and release (17). The multi-target nature of natural compounds is especially valuable because viruses, particularly influenza viruses, can rapidly develop resistance to conventional single-target antiviral agents.

Organosulfur compounds represent a diverse class of naturally occurring substances characterized by sulfur-containing functional groups. These compounds are particularly abundant in members of the Allium genus, including garlic (Allium sativum), onions (Allium cepa), leeks (Allium porrum), and chives (Allium schoenoprasum) (18). These bioactive molecules exhibit remarkable structural diversity, ranging from simple sulfides to complex polysulfides and thiosulfinates (19). The therapeutic potential of organosulfur compounds has been extensively documented, with studies demonstrating antioxidant, anti-inflammatory, antimicrobial, and antiviral activities (20, 21). Their mechanisms of action often involve interactions with protein thiol groups, modulation of cellular redox homeostasis, and regulation of key enzymatic pathways (22). In addition, these compounds have shown potential activity against H5N1 through interactions with viral proteins and disruption of essential viral processes. Certain organosulfur compounds containing sulfhydryl (-SH) groups can form disulfide bonds with protein thiols, potentially altering protein structure and function (19). This mechanism may be particularly relevant to H5N1 infection because key viral proteins, including hemagglutinin (HA) and neuraminidase (NA), contain conserved cysteine residues that are critical for their biological functions (23). Recent studies have further highlighted the potential of organosulfur compounds in drug development due to their ability to modulate multiple molecular targets simultaneously, potentially reducing the likelihood of resistance development.

Shallot (Allium ascalonicum L.), a member of the Amaryllidaceae family, has emerged as a promising source of organosulfur compounds. Traditional Indonesian medicine, particularly in Tolaki-Mekongga, Sulawesi, Indonesia, has long recognized shallots for their therapeutic applications in treating respiratory infections, fever, and influenza-like illnesses (24). A similar traditional use of shallots for influenza has also been reported in Banyumas, Indonesia (25). This widespread traditional usage is supported by modern phytochemical analyses, which have revealed that shallots contain a complex profile of organosulfur compounds, including various sulfides, thiosulfinates, and sulfoxides (26–28). These bioactive compounds exert antiviral effects through multiple mechanisms, including direct virucidal activity, interference with viral attachment and entry, and modulation of host immune responses (29). This multi-target mode of action, combined with a long history of traditional medicinal use and growing scientific evidence, suggests that shallots, particularly their organosulfur constituents, may offer promising therapeutic potential against viral infections, including H5N1 influenza.

Computational approaches, particularly in silico methods, have become invaluable tools for the preliminary evaluation of potential drug candidates. These methods enable rapid compound screening and prediction of interactions with viral targets, significantly reducing the time and resources required for the early stages of drug development (30–32). Density Functional Theory (DFT) calculations can further complement these approaches by providing information on the electronic properties of bioactive compounds. Molecular docking is widely used to predict the binding modes and interactions between bioactive compounds and target proteins. Unlike molecular docking, which provides a static representation of ligand–protein interactions, molecular dynamics simulations enable the evaluation of the stability and dynamic behavior of protein–ligand complexes over time. Molecular dynamics analysis can provide information regarding conformational flexibility, interaction persistence, and the overall stability of ligand binding throughout the simulation period (33–35). This study aims to evaluate the antiviral potential of organosulfur compounds isolated from shallot against the H5N1 influenza virus using an integrated in silico approach. By analyzing the molecular interactions between these sulfur-containing compounds and key viral proteins, this study seeks to identify promising candidates for further experimental investigation in the development of natural anti-influenza agents.

Methods

Density functional theory study

Four organosulfur compounds, Propanethiol, Dipropyl disulfide, γ-Glutamyl-S-propenylcysteine and S-n-Propenyl-L-cysteine-S-oxide were modeled computationally using GaussView 6.0 software. Gaussian16 program was employed for DFT calculation. The B3LYP/6-311++g(d,p) level of theory has been successfully employed in previous studies to characterize natural compounds and identify potential antiviral agents (36). From DFT calculation, several compound electronic parameters can be investigated. The energy gap (Eg) value was determined from the HOMO-LUMO gap using Equation 1 (37).

(1) Eg=ELUMO−EHOMO

Several quantum descriptor parameters such as hardness (η), softness (S), chemical potential (μ), electronegativity (χ), and electrophilicity index (ω) were calculated based on Koopman’s theorem using Equations 2–6 (38, 39).

(2) η=12(ELUMO−EHOMO)

(3) S=12η

(4) μ=12(EHOMO+ELUMO)

(5) χ=−12(EHOMO+ELUMO)

(6) ω=μ22η.

After initial DFT calculation was done, the calculation process was continued to TD-DFT calculation. The TD-DFT calculation process was used to investigate the compound’s theoretical UV-Vis spectra. Meanwhile, the DFT frequency calculation is also employed to investigate the compound’s IR and Raman spectra. All TD-DFT and DFT freq calculation was done in B3LYP/6-311++g(d,p) level of theory.

Prediction of potential biological activity using QSAR analysis

Quantitative structure–activity relationship (QSAR) analysis was conducted to screen the predicted biological activity of the candidate compounds using the Way2Drug PASS Online (https://www.way2drug.com/PassOnline/). Activity parameters associated with influenza virus were specifically selected for analysis. A compound was considered to exhibit potential activity when its probability of activity (Pa) exceeded its probability of inactivity (Pi) (40, 41). In general, compounds with Pa score > 0.7 are considered to have a high probability of exhibiting the predicted biological activity, whereas compounds with Pa score > 0.3 (moderate probability) are considered indicative of computationally a proof and supported to antiviral activity (42–44). The predicted probability values range from 0 to 1 with generally Pa + Pi ≠ 1 (41). In addition, this can be determined that higher Pa have better pharmacological efficacy and potential for experimental requirement (45).

Pharmacokinetic analysis

Analysis of pharmacokinetics was used pkCSM (https://biosig.lab.uq.edu.au/pkcsm/prediction) to determine physicochemical properties and ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) (46). Analysis of physicochemical properties based on the Rules of Lipinski’s rule of five (RO5), such as molecular weight (MW), LogP, Hydrogen Acceptor, and Hydrogen donors for druglikeness (47).

Protein structure modeling and validation

The receptor structures of Influenza A (H5N1) virus, including Polymerase (PB2) (GenBank: PQ809550.1), Nucleocapsid protein (NP) (GenBank: PQ809553.1), and Neuraminidase (NA) (GenBank: PQ809551.1), were obtained from the NCBI database (https://www.ncbi.nlm.nih.gov). The nucleotide sequences were translated into protein sequences using the ExPASy webserver (https://web.expasy.org/translate/) (48), and the correct open reading frame (ORF) was selected based on the identification of start and stop codons. The three-dimensional (3D) structures of these proteins were then remodeled using the Phyre2 webserver (49, 50). The quality of the predicted three-dimensional (3D) protein structures was subsequently evaluated using multiple validation tools. Structural validation was performed using the ERRAT server (https://saves.mbi.ucla.edu/) to assess the overall quality factor based on non-bonded atomic interactions (51). Additionally, stereochemical properties of the protein models were analyzed using the Ramachandran plot generated by Ramplot (https://www.ramplot.in/), which evaluates the distribution of dihedral angles (φ and ψ) to determine the structural reliability of amino acid residues (52). Furthermore, the overall model quality was assessed using the ProSA-web server (https://prosa.services.came.sbg.ac.at/prosa.php), which provides a Z-score indicating the compatibility of the predicted structures with experimentally determined protein structures of similar size (53).

Molecular docking study

The ligand structures such as Oseltamivir phosphate (CID: 65028), Favipiravir (CID: 492405) (control ligand), Propanethiol (CID:7848), Dipropyl disulfide (CID: 12377), S-n-Propenyl-L-cysteine-S-oxide (CID: 441454), and γ-Glutamyl-S-propenylcysteine (CID: 11346811) were obtained from the PubChem database (http://pubchem.ncbi.nlm.nih.gov/) (54–59). The predicted active-site domains of these proteins were identified using PrankWeb (https://prankweb.cz/). The highest-ranked binding pocket predicted by PrankWeb was subsequently selected for docking analysis and used to define the docking grid for each receptor. Detailed binding pocket characteristics are presented in Supplementary Table 1. To evaluate the predicted binding energy between the ligands and receptors, molecular docking simulations were performed. AutoDock Vina integrated with UCSF Chimera version 1.17.3 was used for the docking study. All ligand compounds were protonated, and partial charges were assigned using the AM1-BCC method prior to molecular docking. The docking poses were evaluated based on binding energy and RMSD lower bound (l.b.) values, with lower RMSD values (2.0 Å) indicating a better geometric fit of the ligand within the receptor binding site (60, 61). The docking grids for PB2, NP, and NA were defined sequentially as follows: for PB2, the center coordinates were set at X: -6.627 Å, Y: -28.753 Å, and Z: 52.600 Å with box dimensions of X: 31.456 Å, Y: 27.755 Å, and Z: 31.831 Å; for NP, the center coordinates were X: 82.706 Å, Y: 78.799 Å, and Z: 83.055 Å with box dimensions of X: 35.530 Å, Y: 36.072 Å, and Z: 27.044 Å; and for NA, the center coordinates were X: -25.258 Å, Y: -14.939 Å, and Z: 36.956 Å with box dimensions of X: 20.673 Å, Y: 19.116 Å, and Z: 22.615 Å. The binding interactions between the ligand and receptor were interpreted and visualized using the Discovery Studio Analyzer 2025.

Molecular dynamics simulations study

Molecular dynamics simulations (MDs) were performed using GROMACS 2023.02 package to evaluate the structural stability and dynamic behavior (62, 63). The initial protein structures were prepared and subsequently solvated in a suitable water model within a periodic boundary condition. All topology and parameter files for the ligand compounds were generated using the SwissParam server (https://www.swissparam.ch/) in combination with the CHARMM27 forcefield (64). CHARMM27 was employed to maintain compatibility with the ligand parameters generated by SwissParam and to ensure a consistent force-field framework throughout the simulations. The systems were energy-minimized to remove steric clashes, followed by equilibration under NVT (0.08 ns) and NPT (0.08 ns) ensembles at 300 K to stabilize temperature and pressure prior to the production run. To further quantify the binding energy, Molecular Mechanics Generalized Born Surface Area (MM-GBSA) calculations were performed using snapshots extracted from the 0–100 ns MD trajectories. In this approach, the binding free energy (ΔGbind) is defined according to Equation 7 as reported in previous studies (65):

(7) ΔGbind=ΔGcomplex−ΔGreceptor−ΔGligand

Result and discussion

Density functional theory study

Four organosulfur compounds, Propanethiol, Dipropyl disulfide, γ-Glutamyl-S-propenylcysteine, and S-n-Propenyl-L-cysteine-S-oxide were optimized using DFT method in Gaussian16 with B3LYP/6-311++g(d,p) level of theory. Figure 1 is the visualization of all four organosulfur compounds after the initial DFT process.

Optimized molecular structures obtained from DFT calculations for each ligand; **(A)** Propanethiol, **(B)** Dipropyl disulfide, **(C)** γ-glutamyl-S-propenylcysteine, **(D)** S-n-propenyl-L-cysteine-S-oxide.
Figure 1. Optimized molecular structures obtained from DFT calculations for each ligand; (A) Propanethiol, (B) Dipropyl disulfide, (C) γ-glutamyl-S-propenylcysteine, (D) S-n-propenyl-L-cysteine-S-oxide.

After the DFT calculations were completed, the optimization energies of all compounds were analyzed. The calculated optimization energies are presented in Table 1. As shown in Table 1, the magnitude of the negative optimization energy increased with increasing molecular complexity. These values primarily reflect the energetic stability of the optimized molecular geometries.

Table 1. Each compound energy value from DFT calculations.

CompoundEnergy (HF)Energy (eV)Energy (kcal mol-1)
Propanethiol-517.395-14079.01-324670.074
Dipropyl disulfide-1033.588-28125.364-648586.140
γ-Glutamyl-S-propenylcysteine-1314.155-35759.995-824644.870
S-n-Propenyl-L-cysteine-S-oxide-914.007-24871.406-573548.095

The frontier molecular orbitals (FMOs), particularly the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO), play an important role in describing the electronic properties and chemical reactivity of molecular systems. The HOMO represents the electron-donating ability of a molecule, while the LUMO corresponds to the electron-accepting capacity, making these frontier orbitals important descriptors of molecular reactivity and electronic interactions (66). As shown in Figure 2, the visualization of HOMO and LUMO of the four organosulfur compounds were visualized. It can be seen that the four organosulfur compounds have fairly similar HOMO and LUMO values. These four organosulfur compounds have HOMO values ranging from -6.45 eV to -6.73 eV and LUMO values ranging from -0.45 eV to -0.87 eV. All HOMO and LUMO values of organosulfur compounds have negative values, indicating good stability (67). In addition to the HOMO and LUMO values, these four organosulfur compounds also have similar energy gap values, ranging from 5.61 to 6.11 eV. The energy gap of the studied drugs is in the following order: S-n-Propenyl-L-cysteine-S-oxide < Dipropyl disulfide < γ-Glutamyl-S-propenylcysteine < Propanethiol. A larger HOMO-LUMO energy gap generally indicates greater chemical stability and lower reactivity, as also reported for ascorbic acid, where higher stability is associated with a larger energy gap and hardness (68, 69). Based on these results, Propanethiol, which exhibits the highest energy gap, can be considered the most stable compound among those studied in DFT terms. Conversely, compounds with smaller energy gaps facilitate electron transitions more readily, which may enhance their chemical reactivity and potential interactions with biological targets (70).

HOMO-LUMO visualization for each ligand.
Figure 2. HOMO-LUMO visualization for each ligand.

In addition to the visualization of HOMO and LUMO of each organosulfur compound, an analysis of quantum descriptor parameters of each organosulfur compound was also conducted. Table 2 shows several quantum descriptor parameters for each organosulfur compound. Because the energy gap values were relatively similar among the investigated compounds, the corresponding quantum descriptor parameters also exhibited comparable values. All compounds exhibited relatively high hardness values and low softness values. High chemical hardness and low softness values are generally associated with lower chemical reactivity (71). In this condition, all compounds were found to be chemically stable and likely resistant to structural and chemical changes (72). The chemical potential values of all compounds were negative, which indicates their tendency to act as electron acceptors, and combined with the electrophilicity index value further supports their electron-accepting characteristic. Additionally, the combination of hardness and electronegativity values suggests that these compounds possess resistance to electronic deformation and a tendency to attract electrons (71). Additionally, the theoretical UV-Vis, FT-IR, and Raman spectra were analyzed to support the structural characterization of the compounds and complement the DFT-derived electronic property analysis. Since these spectroscopic calculations were included for molecular characterization purposes, detailed spectral profiles are provided in the Supplementary Information (Supplementary Figures 1-3).

Table 2. Several quantum descriptor parameters from each compound (all values are in eV except S in eV-1).

CompoundEHOMOELUMOEgηSμχω
Propanethiol-6.558-0.4536.1053.0530.164-3.5063.5062.014
Dipropyl disulfide-6.458-0.8175.6412.8210.177-3.6383.6382.346
γ-Glutamyl-S-propenylcysteine-6.730-0.8725.8582.9290.171-3.8013.8012.466
S-n-Propenyl-L-cysteine-S-oxide-6.449-0.8415.6082.8040.178-3.6453.6452.368

Biological relevance of predicted antiviral activity

The PASS prediction results indicate that several of the candidate compounds exhibit potential antiviral activity against influenza, although with varying Pa score values. In the general antiviral (influenza) category (Figure 3), Propanethiol (Pa = 0.337) and Dipropyl disulfide (Pa = 0.361) showed the highest predicted activities among the evaluated compounds. Based on a pharmacological perspective, antiviral activity is a crucial property of compounds capable of inhibiting viral replication and contributing to the control or elimination of viral infections (73, 74). This effect can be achieved through interference with different stages of the viral life cycle, ultimately reducing viral propagation and infection severity (75). Although the predicted Pa score values in this study do not reach the high-activity threshold (Pa score > 0.7) which is generally considered indicative of highly significant bioactivity (76, 77), the presence of measurable antiviral probability supports their potential as preliminary candidates. Therefore, the selected organosulfur compounds were further evaluated through molecular docking and MDs to investigate their interactions with H5N1 viral targets.

Potential activity for candidate compounds.
Figure 3. Potential activity for candidate compounds.

Pharmacokinetic analysis

The results of the physicochemical analysis of the compounds show that none of them violate Lipinski’s rules (Table 3), indicating their potential as drugs because they comply with Lipinski’s rules (78). Further analysis through the ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) property was shown in Table 4. The water solubility value is between -3.527 and -1.025 log mol/L, indicating that the compound is soluble in water with a category above a value of -4 (79). The lowest HIA results were shown by γ-Glutamyl-S-propenylcysteine, at 8.312%, followed by Propanethiol, Dipropyl disulfide, and S-n-propenyl-L-cysteine-S-oxide, which showed good results above 70%, indicating high absorption in the digestive tract (80). Caco2 permeability has a correlation with HIA, as well as being shown to be higher in value of Caco2 permeability, which affects HIA, with results ranging from -0.517 to 1.399 (81). The analysis showed that Propanethiol and γ-Glutamyl-S-propenylcysteine were predicted to act as P-glycoprotein substrates, while Dipropyl disulfide and S-n-Propenyl-L-cysteine-S-oxide were identified as non-substrates. Compounds that function as P-glycoprotein substrates may undergo efflux processes, in which the molecules are actively transported out of cells. This mechanism can decrease the intracellular concentration of the compounds and potentially reduce their oral bioavailability (82). Volume of Distribution at Steady State (VDss) results range from 0.28 to 1.9 L/kg, indicating the compound is moderately distributed to tissue (83). The predicted human unbound fraction (Fu) ranged from 0.452 to 0.68; if values are below 0.7, they indicate that a moderate proportion of the compounds remains unbound in plasma, suggesting notable plasma protein binding while still allowing sufficient free drug to distribute into tissues (84). None of the compounds act as a CYP3A4 substrate; this finding indicates that the compound was not eliminated by this enzyme, but through other pathways. On the other hand, CYP3A4 acts as a general tool for eliminating exogenous substances (85). Total Clearance results show moderate values ranging from 1.71 to 2.00 mL/min/kg, indicating that the compound is not rapidly eliminated, as higher values indicate faster elimination (86). The results show that only Propanethiol acts as a renal OCT2 substrate, indicating that this compound will enter the kidneys and be excreted in the urine, which means it is rapidly cleared, but poses a risk of toxicity to the kidneys (87, 88). None of the compounds caused Ames toxicity and hepatotoxicity. These findings indicate that the test compounds did not cause genetic mutations and were not harmful to the liver (89, 90). Maximum tolerated dose for all compounds show greater than 0.447, explaining that the test compound has a fairly good dose tolerance limit in humans around, based on pkCSM threshold value considered low is equal or less than 0.447 (91). Lethal Dose 50 (LD50) shown Dipropyl disulfide has the highest value 2.454 mol/kg, followed by γ-Glutamyl-S-propenylcysteine 2.438 mol/kg, and Propanethiol 2.251 mol/kg, then S-n-Propenyl-L-cysteine-S-oxide 2.052 mol/kg. The higher the value, the lower the oral toxicity (92).

Table 3. Physicochemical properties analysis.

IndicatorCompound
PropanethiolDipropyl disulfideγ-Glutamyl-S-propenylcysteineS-n-Propenyl-L-cysteine-S-oxide
Molecular Weight ≤ 500 g/mol76.164150.312290.341177.225
LogP ≤ 51.323.18-0.3329-0.3194
Hydrogen acceptor ≤ 101253
Hydrogen donors ≤ 51042

Table 4. ADMET property prediction.

PropertyParameterCompound
PropanethiolDipropyl disulfideγ-Glutamyl-S-propenylcysteineS-n-Propenyl-L-cysteine-S-oxide
AbsorptionWater solubility(log mol/L)-1.025-3.527-2.891-2.888
Human intestinal absorption (HIA) (%)93.40693.8188.31276.556
Caco2 permeability(log pap)1.3851.399-0.5170.569
P-gp substrateYesNoYesNo
DistributionVDss (L/kg)1.151.690.330.28
Fraction unbound (Fu)0.680.4950.4520.463
MetabolismCYP3A4 SubstrateNoNoNoNo
ExcretionTotal Clearance(log ml/min/kg)1.712.692.001.74
Renal OCT2 substrateYesNoNoNo
ToxicityAMES toxicityNoNoNoNo
Max. tolerated dose (log ml/min/kg)1.1650.6821.1191.172
HepatotoxicityNoNoNoNo
Oral Rat Acute Toxicity (LD50) (mol/kg)2.2512.4542.4382.052

Protein modeling and validation

3D structures of Influenza A (H5N1) proteins, namely PB2, NP, and NA, were modeled and subsequently validated using multiple structural assessment tools (Figure 4). The PB2 protein exhibited an ERRAT score of 85.99, a Z-score of -8.92, and 92% of residues located in the favored region of the Ramachandran plot, indicating good overall structural reliability. The NP protein showed an ERRAT score of 76.18, a Z-score of -8.57, and a notably high proportion of residues in the favored region (97%), suggesting excellent stereochemical quality. Meanwhile, the NA protein demonstrated an ERRAT score of 80.86, a Z-score of -8.78, and 96% of residues in the favored region, reflecting a stable and well-refined model. Collectively, these results indicate that all predicted protein structures possess acceptable to high structural quality and are suitable for further computational analyses.

3D structures and structural validation of Influenza A (H5N1) proteins; **(A)** PB2 protein, including its 3D model, ERRAT assessment (85.99), ProSA Z-score (-8.92), and Ramachandran plot showing 92% residues in favored regions. **(B)** NP protein, including its 3D model, ERRAT assessment (76.18), ProSA Z-score (-8.57), and Ramachandran plot showing 97% residues in favored regions. **(C)** NA protein, including its 3D model, ERRAT assessment (80.86), ProSA Z-score (-8.78), and Ramachandran plot showing 96% residues in favored regions.
Figure 4. 3D structures and structural validation of Influenza A (H5N1) proteins; (A) PB2 protein, including its 3D model, ERRAT assessment (85.99), ProSA Z-score (-8.92), and Ramachandran plot showing 92% residues in favored regions. (B) NP protein, including its 3D model, ERRAT assessment (76.18), ProSA Z-score (-8.57), and Ramachandran plot showing 97% residues in favored regions. (C) NA protein, including its 3D model, ERRAT assessment (80.86), ProSA Z-score (-8.78), and Ramachandran plot showing 96% residues in favored regions.

The validation results indicate that all predicted protein models are of reliable quality for further computational studies. ERRAT scores above 50 suggest acceptable overall model accuracy, while Z-scores within the range of native proteins confirm structural plausibility (93). Additionally, the high percentage of residues in favored regions (> 90%) from Ramachandran analysis reflects good stereochemical quality (52). Among the three proteins, NP demonstrated the highest stereochemical stability, whereas PB2 showed the best overall structural quality based on ERRAT. These findings suggest that the generated models are suitable for downstream applications such as molecular docking and interaction analysis.

Molecular docking study

Molecular docking was performed to evaluate the binding interactions between the investigated compounds and the viral protein targets. As shown in Figure 5, ligands with simpler structures and shorter carbon chains, such as Propanethiol and Dipropyl disulfide, predominantly rely on hydrophobic interactions to stabilize their positions within the binding pocket. For instance, in the PB2 receptor, Propanethiol mainly interacts with aromatic residues including PHE328, PHE335, PHE368, and PHE409 through hydrophobic contacts. In contrast, more structurally complex compounds, namely γ-glutamyl-S-propenylcysteine and S-n-propenyl-L-cysteine-S-oxide, exhibit a broader range of intermolecular interactions, including both hydrophobic contacts and directional hydrogen bonds. Notably, γ-glutamyl-S-propenylcysteine forms multiple hydrogen bonds across the three receptors. In PB2, hydrogen bonds were observed with ASN515, HIS362, and PHE328, while in NP, interactions were observed with SER176, ARG278, PHE499, PHE349, and SER497. In NA, hydrogen bonds were observed with THR314, CYS318, TYR321, GLN407, and GLU409. The presence of multiple hydrogen bonds with these amino acid residues may enhance the stabilization of the ligand within the predicted binding pocket and support favorable ligand–protein interactions (20, 30). For comparison, the reference antiviral drugs Oseltamivir and Favipiravir were also docked against the same targets, and their interaction profiles are provided in Supplementary Figure 4.

Visualization mechanism interaction against various receptors.
Figure 5. Visualization mechanism interaction against various receptors.

All molecular docking results produced root mean square deviation (RMSD) l.b. values below the acceptable threshold of 2.0 Å, ranging from 1.421 Å to 1.97 Å, indicating a good geometric fit of the ligands within the predicted binding pockets (Figure 6). Analysis of the binding energy (BE) revealed that γ-glutamyl-S-propenylcysteine exhibited the most favorable binding energies among the four evaluated compounds derived from shallot (Allium ascalonicum L.), with values of -4.9 kcal/mol for PB2, -5.8 kcal/mol for NP, and -5.2 kcal/mol for NA. Meanwhile, S-n-propenyl-L-cysteine-S-oxide also demonstrated moderate binding energies across the three targets, with binding energies ranging from -4.5 to -4.8 kcal/mol. The relatively favorable binding energies of these compounds may be associated with the presence of organosulfur functional groups, which have been reported to facilitate interactions with viral proteins and potentially contribute to multitarget antiviral activity (29). When compared with the established influenza inhibitors Oseltamivir and Favipiravir (Supplementary Figure 5), γ-glutamyl-S-propenylcysteine exhibited lower binding energy toward PB2 than Oseltamivir (-5.7 kcal/mol) and Favipiravir (-5.9 kcal/mol), but showed comparable docking performance against NP and NA. The interacting residues identified for neuraminidase showed limited correspondence with the commonly reported catalytic residues, including Arg118, Asp151, Arg152, Glu276, and Arg371, suggesting that the docked ligands may interact with alternative binding regions predicted from the modeled protein structure. Notably, γ-glutamyl-S-propenylcysteine displayed a more favorable binding energy than Favipiravir against NP (-5.8 vs. -5.2 kcal/mol) and then Oseltamivir against NA (-5.2 vs. -3.6 kcal/mol). Overall, these findings indicate that the investigated organosulfur compounds exhibit moderate binding interactions with H5N1 viral targets and may serve as preliminary candidates for further investigation. Nevertheless, the docking results should be interpreted cautiously and supported by additional validation through molecular dynamics simulations, MM-GBSA calculations, and experimental studies.

Molecular docking results; **(A)** Binding energy, **(B)** RMSD l.b. evaluation.
Figure 6. Molecular docking results; (A) Binding energy, (B) RMSD l.b. evaluation.

Molecular dynamics simulations study

Molecular dynamics simulations (MDs) were performed to evaluate the dynamic interactions between ligands and viral proteins by simulating atomic motions over time. To visualize the overall conformational changes during the simulation, structural superposition of the MD trajectories was performed. As shown in Figure 7, the structural superposition of MD trajectories over the 0–100 ns interval illustrates the overall spatial displacement of the PB2, NP, and NA protein systems. The complexes formed with the larger organosulfur compounds, particularly γ-Glutamyl-S-propenylcysteine and S-n-Propenyl-L-cysteine-S-oxide, display more compact conformational clustering compared with the shorter-chain ligands (Propanethiol and Dipropyl disulfide). This reduced structural dispersion may indicate a more stable conformational behavior of the corresponding protein–ligand complexes during the simulation, which is consistent with previous reports describing stable protein–ligand interactions during MDs (94).

Superimposed molecular dynamics simulation for receptor–ligand complexes at 0–100 ns.
Figure 7. Superimposed molecular dynamics simulation for receptor–ligand complexes at 0–100 ns.

Root mean square deviation (RMSD) analysis was performed to evaluate the structural stability and conformational behavior of the protein–ligand complexes throughout the 100 ns MDs. All organosulfur compounds derived from Allium ascalonicum L. exhibited relatively small structural deviations, with RMSD values remaining below 0.2 nm (ranging from 0.05 to 0.16 nm) along the simulation trajectory (Figure 8). In general, smaller RMSD values (lower than 2.0 Å) indicate limited structural displacement and suggest that the protein–ligand complexes maintain stable conformations during the simulation (95, 96). These results suggest that the protein–ligand complexes maintained stable conformations without substantial structural deviations throughout the simulation period.

RMSD analysis during simulation at 0–100 ns.
Figure 8. RMSD analysis during simulation at 0–100 ns.

Unlike RMSD, root mean square fluctuations (RMSF) analysis is used to evaluate the residue-level flexibility of protein structures MD simulations. Based on Figure 9, the RMSF profiles of the protein systems over the 100 ns simulation reveal varying levels of intrinsic residue flexibility among the PB2, NP, and NA. In the PB2 polymerase subunit, higher fluctuations are mainly observed in the N-terminal loop region, whereas the core domains remain relatively stable, indicating that the structural framework of the protein is largely preserved during the simulation. In contrast, the NP shows generally low residue fluctuations across most regions, with a noticeable peak around the ~420 residue region, suggesting the presence of a flexible loop region with increased local mobility, as also reported in previous studies (97). Meanwhile, NA exhibits relatively stable fluctuation patterns throughout the protein structure, reflecting a comparatively rigid structural framework consistent with its functional architecture reported in earlier studies (98).

RMSF analysis of protein systems at 0–100 ns; **(A)** PB2, **(B)** NP, **(C)** NA.
Figure 9. RMSF analysis of protein systems at 0–100 ns; (A) PB2, (B) NP, (C) NA.

Radius of gyration (RoG) analysis was performed to evaluate the overall compactness and structural stability of the protein systems throughout the MDs. The results indicate that all systems, including PB2, NP, and NA, exhibit a gradual decrease in RoG values from the initial fluctuation phase toward a relatively stable plateau at the final stage of the simulation, particularly when complexed with γ-Glutamyl-S-propenylcysteine and S-n-propenyl-L-cysteine-S-oxide (Figures 10A–C). The formation of a more compact conformation may indicate stabilization of the protein systems during the simulation and is consistent with favorable protein–ligand interactions (99). The average RoG values of the protein systems for each complex are summarized in Supplementary Table 2.

RoG analysis protein systems results at 0–100 ns; **(A)** PB2, **(B)** NP, **(C)** NA.
Figure 10. RoG analysis protein systems results at 0–100 ns; (A) PB2, (B) NP, (C) NA.

Solvent accessible surface area (SASA) analysis was performed to evaluate the extent of protein surface exposure to the surrounding solvent throughout the MDs. As shown in Figure 11, the SASA profiles visualize the dynamics of the protein surface area exposed to the solvent during the 100 ns simulation. It was observed that the PB2 and NP complex systems demonstrated a consistent decreasing trend in SASA values from the initial fluctuation phase toward the final equilibrium state, whereas NA maintained a relatively stable and low surface exposure profile. A decrease in SASA generally indicates the formation of a more compact protein structure, often associated with conformational stabilization upon ligand binding. The reduced contact area with external water molecules may limit the flexibility of amino acid loops, induce greater structural rigidity, and ultimately stabilize the inhibitor within the hydrophobic binding pocket of the protein (100). The average SASA values for each system are summarized in Supplementary Table 2.

SASA analysis protein systems results at 0–100 ns; **(A)** PB2, **(B)** NP, **(C)** NA.
Figure 11. SASA analysis protein systems results at 0–100 ns; (A) PB2, (B) NP, (C) NA.

Hydrogen bond analysis was performed to evaluate the specific interactions formed between the ligands and the amino acid residues of the viral proteins during the MDs. The hydrogen bond profiles illustrate the number of hydrogen-bond interactions formed between the ligands and amino acid residues of the viral proteins (PB2, NP, and NA) during the 100 ns simulation (Figure 12). Hydrogen bond occupancy analysis further confirms the persistence of these interactions. Propanethiol and Dipropyl disulfide showed negligible occupancy (0%) across PB2, NP, and NA systems, whereas γ-glutamyl-S-propenylcysteine and S-propenyl-L-cysteine-S-oxide display high occupancies ranging from 72.313% to 100%, indicating stable hydrogen bonding interactions throughout the simulation (Supplementary Table 2). This observation is consistent with the molecular docking results, where Propanethiol and Dipropyl disulfide interacted predominantly through hydrophobic contacts, including alkyl and π-alkyl interactions, rather than conventional hydrogen bonds. Therefore, their interactions with the protein targets are likely maintained primarily by hydrophobic and van der Waals forces rather than persistent hydrogen-bond formation. The persistence of hydrogen bonding interactions observed for the organosulfur derivatives may contribute to the stabilization of the protein–ligand complexes by maintaining favorable intermolecular contacts within the binding sites during the simulation (101). Furthermore, a higher number of hydrogen bonds may indicate stronger intermolecular interaction, which could contribute to increased binding energy (102).

Hydrogen bond analysis based on ligand and protein interaction at 0–100 ns; **(A)** PB2, **(B)** NP, **(C)** NA.
Figure 12. Hydrogen bond analysis based on ligand and protein interaction at 0–100 ns; (A) PB2, (B) NP, (C) NA.

To further quantify the binding strength between the candidate compounds and the viral proteins, the MM-GBSA approach was employed to estimate the ΔGbind of the protein–ligand complexes. This method decomposes ΔGbind into several energetic components, including van der Waals interactions, electrostatic interactions, and solvation energy, thereby providing a more comprehensive evaluation of ligand binding stability. The per-residue energy decomposition is shown in Figure 13, and the detailed per-residue energy decomposition results are presented in Supplementary Table 3, while the resulting ΔGbind values are presented in Table 5. In general, more negative ΔGbind values indicate stronger and more favorable protein–ligand interactions (34, 103). Among the evaluated compounds, γ-Glutamyl-S-propenylcysteine exhibits the most favorable ΔGbind toward the three viral targets, with ΔGbind values of -30.52 ± 0.29 kcal/mol for PB2, -22.76 ± 0.12 kcal/mol for NP, and -26.13 ± 0.35 kcal/mol for NA. Similarly, S-n-Propenyl- L-cysteine-S-oxide also demonstrated relatively medium binding energy, with ΔGbind values of -12.63 ± 0.22 kcal/mol (PB2), -11.54 ± 0.65 kcal/mol (NP), and -12.96 ± 0.23 kcal/mol (NA). In contrast, the shorter-chain ligands, Propanethiol, exhibited comparatively less favorable binding energies across the three receptors. However, further experimental studies are required to validate the inhibitory potential of these compounds.

The per-residue decomposition for MM-GBSA analysis results at 0–100 ns. **(A)** Propanethiol with PB2, **(B)** Dipropyl disulfide with PB2, **(C)** γ-Glutamyl-S-propenylcysteine with PB2, **(D)** S-n-Propenyl- L-cysteine-S-oxide with PB2, **(E)** Propanethiol with NP, **(F)** Dipropyl disulfide with NP, **(G)** γ-Glutamyl-S-propenylcysteine with NP, **(H)** S-n-Propenyl-L-cysteine-S-oxide with NP, **(I)** Propanethiol with NA, **(J)** Dipropyl disulfide with NA, **(K)** γ-Glutamyl-S-propenylcysteine with NA, **(L)** S-n-Propenyl- L-cysteine-S-oxide with NA.
Figure 13. The per-residue decomposition for MM-GBSA analysis results at 0–100 ns. (A) Propanethiol with PB2, (B) Dipropyl disulfide with PB2, (C) γ-Glutamyl-S-propenylcysteine with PB2, (D) S-n-Propenyl- L-cysteine-S-oxide with PB2, (E) Propanethiol with NP, (F) Dipropyl disulfide with NP, (G) γ-Glutamyl-S-propenylcysteine with NP, (H) S-n-Propenyl-L-cysteine-S-oxide with NP, (I) Propanethiol with NA, (J) Dipropyl disulfide with NA, (K) γ-Glutamyl-S-propenylcysteine with NA, (L) S-n-Propenyl- L-cysteine-S-oxide with NA.

Table 5. All energy components (kcal/mol) in 0–100 ns based on MM-GBSA result.

ReceptorCompoundΔEvdwΔEelecΔGgasΔGsolveleΔGsolvnonpolarΔGsolvΔGbind
PB2Propanethiol-7.97 ± 0.09-1.38 ± 0.02-9.35 ± 0.093.84 ± 0.02-1.17 ± 0.022.67 ± 0.03-6.68 ± 0.10
Dipropyl disulfide-19.03 ± 0.03-2.41 ± 0.02-21.44 ± 0.039.93 ± 0.00-2.82 ± 0.007.11 ± 0.00-14.33 ± 0.03
γ-Glutamyl-S-propenylcysteine-34.79 ± 0.02-35.98 ± 0.28-70.77 ± 0.2845.67 ± 0.06-5.81 ± 0.0139.85 ± 0.06-30.52 ± 0.29
S-n-Propenyl- L-cysteine-S-oxide-16.98 ± 0.03-37.47 ± -0.21-54.45 ± 0.2244.99 ± 0.02-3.16 ± 0.0141.83 ± 0.02-12.63 ± 0.22
NPPropanethiol-15.05 ± 0.02-2.47 ± 0.01-17.52 ± 0.026.87 ± 0.01-2.09 ± 0.004.78 ± 0.01-12.73 ± 0.02
Dipropyl disulfide-16.91 ± 0.01-4.31 ± 0.03-21.22 ± 0.039.58 ± 0.06-2.46 ± 0.007.12 ± 0.06-14.10 ± 0.06
γ-Glutamyl-S-propenylcysteine-19.16 ± 0.02-61.45 ± 0.06-80.61 ± 0.0761.96 ± 0.10-4.10 ± 0.0057.85 ± 0.10-22.76 ± 0.12
S-n-Propenyl- L-cysteine-S-oxide-8.51 ± 0.0594.28 ± 0.5285.77 ± 0.53-94.78 ± 0.38-2.52 ± 0.01-97.31 ± 0.38-11.54 ± 0.65
NAPropanethiol-2.36 ± 0.01-0.34 ± 0.01-2.70 ± 0.011.26 ± 0.02-0.38 ± 0.010.88 ± 0.02-1.82 ± 0.02
Dipropyl disulfide-14.54 ± 0.04-1.87 ± 0.00-16.41 ± 0.046.25 ± 0.01-2.17 ± 0.004.08 ± 0.01-12.33 ± 0.04
γ-Glutamyl-S-propenylcysteine-19.90 ± 0.03-61.76 ± 0.31-81.66 ± 0.3159.63 ± 0.16-4.09 ± 0.0055.53 ± 0.16-26.13 ± 0.35
S-n-Propenyl- L-cysteine-S-oxide-12.74 ± 0.03-20.51 ± 0.21-33.25 ± 0.2122.82 ± 0.07-2.53 ± 0.0220.29 ± 0.07-12.96 ± 0.23

To investigate the conformational stability and thermodynamic behavior of the protein systems, Figure 14 presents the free energy landscape (FEL) analysis derived from the MD trajectories. In general, the blue to green regions represent the global minimum energy states, indicating the most stable conformations, while yellow to red regions correspond to higher energy regions (unfavorable conformations) (104, 105). According to the FEL analysis (Figure 14), most systems still exhibit a relatively broad distribution of conformational states. Previous MDs studies on H5N1 proteins have also employed FEL analysis to characterize conformational stability and identify energetically favorable binding states (106, 107). The presence of low-energy basins in the FEL profiles indicates that the protein systems sampled energetically favorable conformational states during the simulations, supporting the overall stability of the complexes.

FEL analysis for the protein–ligand complexes results at 50 ns.
Figure 14. FEL analysis for the protein–ligand complexes results at 50 ns.

Conclusion

This study presents an in silico evaluation of organosulfur compounds from shallot (Allium ascalonicum L.) originating from Tolaki-Mekongga, Sulawesi, Indonesia as potential inhibitors against Influenza A (H5N1). DFT and TD-DFT analyses indicate favorable electronic properties and molecular reactivity of the evaluated compounds. PASS prediction indicated potential antiviral activity and acceptable drug-likeness characteristics, with Propanethiol (Pa = 0.337) and Dipropyl disulfide (Pa = 0.361) exceeding the medium-confidence threshold (Pa > 0.3). In addition, pharmacokinetic analysis revealed that all compounds comply with Lipinski’s rule of five and exhibit acceptable ADMET properties, suggesting favorable drug-likeness and safety profiles. Molecular docking shows reliable binding conformations (RMSD 1.421–1.97 Å), where γ-glutamyl-S-propenylcysteine exhibits the most favorable binding energies toward PB2 (-4.9 kcal/mol), NP (-5.8 kcal/mol), and NA (-5.2 kcal/mol). Molecular dynamics and MM-GBSA analyses further support stable protein–ligand interactions across the three viral targets. Overall, these computational findings suggest that organosulfur compounds derived from shallot (Allium ascalonicum L.) may serve as potential antiviral candidates against Influenza A (H5N1). Nevertheless, further validation through in vitro and in vivo experiments is required to confirm their biological activity. Among the evaluated compounds, γ-glutamyl-S-propenylcysteine has proposed emerged as the most promising candidate inhibitor based on its favorable docking, molecular dynamics, and MM-GBSA results.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fviro.2026.1857484/full#supplementary-material

Supplementary material Supplementary material at the source

Footnotes

Edited by: Sreekanth Gopinathan Pillai, Manipal Institute of Virology, India

Reviewed by: Gurushankar Krishnamoorthy, Kalasalingam University, India

Anson S. Maroky, Kerala University of Health Sciences, India

Statements

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author AAn declared that they were an editorial board member of Frontiers at the time of submission. This had no impact on the peer review process and the final decision.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Data availability

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.

Author contributions

RP: Data curation, Methodology, Formal analysis, Validation, Project administration, Conceptualization, Investigation, Software, Writing – original draft, Funding acquisition, Writing – review & editing, Visualization, Resources. APu: Data curation, Project administration, Conceptualization, Methodology, Writing – original draft, Writing – review & editing, Software, Investigation, Formal analysis. MF: Writing – original draft, Formal analysis, Data curation, Software, Writing – review & editing, Project administration, Conceptualization, Methodology, Investigation. VS: Data curation, Methodology, Software, Conceptualization, Writing – review & editing, Investigation, Writing – original draft, Formal analysis, Project administration. MA: Investigation, Project administration, Data curation, Conceptualization, Validation, Funding acquisition, Writing – review & editing, Methodology, Formal analysis, Writing – original draft, Visualization, Software, Resources. SS: Investigation, Software, Writing – review & editing, Resources, Visualization, Formal analysis, Data curation, Conceptualization, Methodology, Supervision. FD: Visualization, Supervision, Formal analysis, Methodology, Software, Data curation, Investigation, Resources, Writing – review & editing, Conceptualization. AK: Supervision, Writing – review & editing, Conceptualization, Software, Methodology, Formal analysis, Investigation, Visualization, Data curation. IM: Supervision, Data curation, Methodology, Formal analysis, Conceptualization, Software, Investigation, Writing – review & editing. AS: Data curation, Conceptualization, Investigation, Software, Writing – review & editing, Methodology, Supervision, Formal analysis. MR: Formal analysis, Investigation, Data curation, Software, Supervision, Writing – review & editing, Conceptualization, Methodology. AAb: Conceptualization, Investigation, Supervision, Methodology, Formal analysis, Software, Data curation, Writing – review & editing. VJ: Data curation, Supervision, Investigation, Conceptualization, Software, Writing – review & editing, Methodology, Formal analysis. NS: Software, Conceptualization, Writing – review & editing, Investigation, Formal analysis, Supervision, Methodology, Data curation. NK: Writing – original draft, Writing – review & editing, Data curation, Methodology, Supervision, Conceptualization, Formal analysis, Validation, Investigation, Visualization. IS: Validation, Data curation, Methodology, Writing – original draft, Conceptualization, Supervision, Project administration, Investigation, Visualization, Resources, Funding acquisition, Software, Formal analysis, Writing – review & editing. AAn: Formal analysis, Validation, Methodology, Data curation, Supervision, Conceptualization, Project administration, Writing – original draft, Software, Writing – review & editing, Resources, Visualization, Funding acquisition, Investigation. APa: Validation, Project administration, Data curation, Methodology, Supervision, Conceptualization, Resources, Investigation, Visualization, Funding acquisition, Software, Formal analysis, Writing – review & editing.

Acknowledgements

The authors would like to thank the Bioinformatics Laboratory, UCoE Research Center for Bio-Molecule Engineering (BIOME), Universitas Airlangga, Surabaya, Indonesia for providing facilities and support for this research. This research was supported by the Virtual Research Center for Bioinformatics and Biotechnology (VRCBB), Surabaya, Indonesia (https://jalantengah.site/vrcbb). The authors also thank Jalan Tengah, Indonesia (https://jalantengah.site/) for assistance with manuscript editing.

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Citation

Prastika, R. A., et al. (2026). In silico identification of organosulfur compounds from Allium ascalonicum L. as potential inhibitors of influenza A (H5N1): integrated DFT, docking, and molecular dynamics analysis. Frontiers in Virology, Volume 6. https://doi.org/
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Copyright © 2026 Prastika, Putra, Fadhana, Setiawan, Afnani, Sahadewa, Durry, Khairullah, Adnyana, Singh, Rebezov, Abdurasulov, Jakhmola, Sharma, Kupaeva, Siswanto, Ansori and Parikesit.

License: https://creativecommons.org/licenses/by/4.0/

*Correspondence: Imam Siswanto; Arif Nur Muhammad Ansori

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