Computational Identification of Novel Inhibitors Targeting Multiple Proteins of Tomato Brown Rugose Fruit Virus (ToBRFV) Through AlphaFold-Based Protein Modeling, Molecular Docking, MM/GBSA Binding Free Energy Analysis, and Molecular Dynamics Simulation

Computational Identification of Novel Inhibitors Targeting Multiple Proteins of Tomato Brown Rugose Fruit Virus (ToBRFV) Through AlphaFold-Based Protein Modeling, Molecular Docking, MM/GBSA Binding Free Energy Analysis, and Molecular Dynamics Simulation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Computational Identification of Novel Inhibitors Targeting Multiple Proteins of Tomato Brown Rugose Fruit Virus (ToBRFV) Through AlphaFold-Based Protein Modeling, Molecular Docking, MM/GBSA Binding Free Energy Analysis, and Molecular Dynamics Simulation Nikeshun Vivekananthan, Niranjana Prem Minipreman, Chinnakaruppan Marimuthu, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8550076/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Tomato brown rugose fruit virus (ToBRFV), a tobamovirus, poses a significant threat to global tomato production due to its high infectivity, seed-borne transmission, and severe fruit symptoms. In this study, an integrative computational approach was employed to identify plant-derived phytochemicals capable of inhibiting essential viral proteins such as movement protein (MP), coat protein (CP), helicase domain, and RNA-dependent RNA polymerase (RdRP) domain. The three-dimensional structures of these viral targets were predicted using AlphaFold and subsequently validated using Ramachandran plots. A library of 2,847 phytochemicals was subjected to molecular docking, followed by MM-GBSA binding free energy calculations to evaluate binding affinity and interaction strength. Top-ranked compounds were further validated through 100-ns molecular dynamics (MD) simulations to assess complex stability and conformational behavior. Panasenoside, Kaempferol 3-sophorotrioside, Violanin, and Albireodelphin A exhibited the strongest binding affinities toward MP, CP, Helicase, and RdRP, respectively. RMSD and RMSF analyses confirmed the stability of these complexes, highlighting persistent hydrogen-bonding interactions within the active sites. The findings underscore the potential of flavonoids as effective antiviral agents against ToBRFV and provide a foundation for future in vitro and in vivo validation studies to develop flavonoid-based antiviral formulations for sustainable tomato crop protection. AlphaFold Molecular docking Molecular dynamics multi-target approach ToBRFV Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Plant viral diseases pose a significant threat to global crop yields and quality, causing substantial economic losses annually. Tomato ( Lycopersicon esculentum ), ranking as the second most important vegetable crop after potato, suffers notable yield reductions due to viral infections, with global crop losses from plant diseases estimated to exceed 30% per year, translating into economic damages worth hundreds of billions of dollars [ 1 ]. Among plant viruses, tobamoviruses represent serious threats to vegetable and ornamental crops worldwide [ 2 , 3 ]. They are transmitted over long distances via contaminated seeds and spread mechanically between plants through cultural practices or circulating water, particularly in hydroponic systems [ 4 ]. Mechanical transmission through contact, contaminated tools, and potentially infected seeds facilitates rapid dissemination, especially in controlled environments such as greenhouses [ 5 , 6 ]. In April 2015, tomato plants ( cv. Candela) grown in greenhouses in the Jordan Valley exhibited mild foliar symptoms alongside severe brown rugose symptoms on fruits, which significantly impacted marketability [ 7 ]. Based on symptomatology and disease distribution, a viral etiology was suspected. The causal agent was subsequently isolated and identified using bioassays and molecular diagnostics and was classified by the International Committee on Taxonomy of Viruses (ICTV) as a novel species within the genus Tobamovirus , named Tomato brown rugose fruit virus (ToBRFV). A closely related isolate with high sequence identity to the Jordanian Tom-1 strain, reported during a severe outbreak in southern Israel in 2014 [ 8 ]. Within a year, ToBRFV spread extensively across Israel and was detected in multiple growing regions. Concurrent outbreaks in neighbouring countries, including Jordan, occurred during the 2014/2015 winter growing season. The virus has since spread rapidly, with reported occurrences in 35 countries across Asia, Europe, North America, and Africa. Disease incidence during these outbreaks often approached 100%, with pronounced brown rugose fruit symptoms severely reducing marketability despite relatively mild foliar symptoms [ 9 – 12 ]. ToBRFV is currently considered the most serious threat to global tomato production. Symptom expression in tomato varies according to cultivar, plant age at infection, and environmental factors such as temperature, photoperiod, and cultivation system. Leaf symptoms commonly include mosaic patterns, dark green blistering, and leaf narrowing, while occasional necrotic lesions on peduncles, calyxes, and petioles, as well as longitudinal stem necrosis, have been reported [ 8 ]. Fruit symptoms typically manifest as brown, rugose, or wrinkled patches [ 7 ]. ToBRFV is primarily seed-borne, with contaminated seeds serving as the major pathway for long-distance dissemination [ 13 ]. Seeds harvested from infected fruits are fully contaminated, although the virus is detected only on the external seed coat (testa) [ 14 , 15 ]. Global ToBRFV isolates are highly similar genetically, sharing over 99% nucleotide identity, although structural variations may influence traits such as virulence [ 6 , 16 ]. India, as a major tomato producer and supplier of seed material for solanaceous crops, faces significant risk from ToBRFV, which spreads via plant-to-plant contact and contaminated seeds. In India, ToBRFV was detected in symptomatic tomato ( Solanum lycopersicum ) plants in open fields in Karnataka and Maharashtra in May 2023. Affected plants exhibited mosaic patterns, mottling, chlorosis, leaf deformation, necrotic spots, and brown rugose patches on fruits. Laboratory analyses, including DAS-ELISA, RT-PCR, and sequencing, confirmed ToBRFV presence in 11 symptomatic fruit samples [ 17 ]. The ToBRFV genome closely resembles that of other tobamoviruses such as TMV, ToMV, and Rehmannia mosaic virus, sharing 81–82% nucleotide identity. Its genome consists of a single-stranded positive-sense RNA of approximately 6,400 nucleotides encoding four open reading frames (ORFs) [ 8 , 12 , 13 , 18 , 19 ]. The RNA is capped with a short 5′ untranslated region (UTR) containing CAA repeats and a 3′ UTR capable of forming pseudoknots, followed by a tRNA-like amino acid–accepting structure. ORF1 and ORF2, overlapping via a leaky stop codon, encode a 126-kDa protein with methyltransferase and helicase domains and a 183-kDa readthrough protein, which together function as subunits of the RNA-dependent RNA polymerase (RdRP) [ 20 – 22 ]. ORF3 encodes the movement protein (~ 28–31 kDa) responsible for cell-to-cell transport via plasmodesmata, while ORF4 encodes the coat protein (~ 17.5 kDa) forming the viral capsid. Both ORF3 and ORF4 are expressed from subgenomic RNAs produced during replication [ 12 , 21 , 23 ]. To identify potential inhibitors against ToBRFV, essential viral proteins involved in replication, movement, and encapsidation were selected as molecular targets for further investigation. In this regard, natural compounds, especially phytochemicals, are gaining prominence over synthetic chemicals in the development of antiviral formulations and plant protection measures. Their eco-friendly and sustainable characteristics make these plant-derived molecules a safer and more environmentally sound alternative to conventional synthetic agents for controlling viral infections [ 24 ]. A wide variety of naturally occurring phytochemicals, including flavonoids, carotenoids, polyphenols, isoprenoids, phytosterols, saponins, alkaloids, and polysaccharides, are well known for their potent antioxidant capabilities and diverse biological functions. These encompass antimicrobial, antiviral, antidiarrheal, anthelmintic, antiallergic, and antispasmodic activities [ 25 , 26 ]. Considering the limitations of experimentally testing large libraries of natural metabolites, computational techniques such as molecular docking and molecular dynamics (MD) simulations offer efficient and economical tools for the early-stage discovery and design of potential antiviral molecules [ 27 ]. So far, there have been no computational studies focusing on the identification of inhibitors against ToBRFV. Hence, the current work is designed to explore plant-derived phytochemicals with the potential to inhibit crucial viral proteins such as the movement protein (MP), coat protein (CP), Helicase, and the RdRP domain of ToBRFV. The study utilized molecular docking to perform preliminary virtual screening, which was subsequently complemented by molecular dynamics (MD) simulations and binding free energy (MM-GBSA) calculations to evaluate the stability and interaction strength of the most promising protein–ligand complexes. 2. Materials and Methods 2.1. Sequence retrieval and 3D Structure Prediction The nucleotide sequences encoding the coat protein (CP), movement protein (MP), and replicase protein of tomato brown rugose fruit virus (NCBI Accession ID: KT383474) were retrieved in FASTA format from the NCBI GenBank database. Four distinct protein targets were selected for structural prediction: MP, CP, Helicase domain, and RdRp domain of the replicase protein. The RdRp domain and helicase domain present in the replicase gene were identified using InterPro [ 28 ]. Three-dimensional (3D) structure prediction of the selected proteins was carried out using ColabFold (v1.5.5) [ 29 ]. Protein sequences were provided as input in FASTA format, and Multiple Sequence Alignments (MSAs) were generated using the MMseqs2 algorithm. Amber relaxation was disabled, and predictions were performed using the unpaired MSA option. For modeling the dimeric structure of CP, the Alphafold2_multimer_v3 model was employed [ 30 ]. AlphaFold predicts residue-level 3D coordinates and assigns confidence scores based on the predicted Local Distance Difference Test (pLDDT), which ranges from 0 to 100, indicating the reliability of each prediction. The top-ranked high-confidence models for all four targets were selected and subsequently subjected to energy minimization using the YASARA Energy Minimization Server to enhance structural stability [ 31 ]. Structural validation of the energy-minimized models was performed using the SAVES v6.0 server [ 32 ]. 2.2. Ligand Preparation and Binding Site Prediction A total of 2,847 phytochemical compounds were retrieved from the PubChem database [ 33 ] ( https://pubchem.ncbi.nlm.nih.gov/classification/#hid=5 ) and processed using the LigPrep module of Schrödinger Suite 2024-4. The ligand preparation protocol involved desalting, protonation, and the generation of stereoisomers, tautomeric states, and ionization variants at physiological pH (7 ± 2) [ 34 ]. The predicted three-dimensional structures of the four viral targets of ToBRFV were optimized using the Protein Preparation Wizard in Schrödinger, which included the addition of hydrogen atoms, optimization of hydrogen-bonding networks, and energy minimization to enhance structural accuracy. Binding site identification was subsequently performed using the SiteMap tool, where pockets were evaluated based on cavity volume, hydrophobic–hydrophilic characteristics, and hydrogen bond donor–acceptor profiles. The top-ranked binding site was selected, and a receptor grid was generated with Glide to define the docking region for virtual screening [ 35 ]. 2.3. Molecular Docking Studies The receptor grids generated at the predicted active sites of all four ToBRFV viral targets were utilized as docking receptors. Molecular docking was carried out using the Glide module within the Schrödinger Suite. The docking procedure applied the Extra Precision (XP) mode to improve the accuracy of binding pose prediction and binding affinity estimation [ 36 ]. Standard docking flexibility parameters were employed, including penalties for non-planar amide conformations. Protein–ligand interactions, such as hydrogen bonds and hydrophobic contacts, were examined through the Maestro interface to evaluate binding orientation and affinity. 2.4. Molecular Mechanics Generalized Born Surface Area (MM-GBSA) Calculations To assess the binding strength between ligands and the four viral targets of ToBRFV, binding free energy calculations were performed using the Molecular Mechanics/Generalized Born Surface Area (MM-GBSA) approach. This analysis was conducted with the Prime module of the Schrödinger Suite. For the evaluation, only the top-ranked ligand–protein complexes, identified by the most favorable docking scores, were considered. The solvation effects were represented using the VSGB 2.0 implicit solvent model in combination with the OPLS-2005 force field [ 37 ]. In addition, the method estimated ligand strain energy by positioning the ligand within the solvent environment automatically generated by the VSGB 2.0 model under the OPLS-2005 force field framework [ 38 ]. 2.5. Molecular Dynamics (MD) Simulations with Desmond Molecular dynamics (MD) simulations were carried out to investigate the stability of ligand–protein complexes, emphasizing the conformational dynamics of the macromolecule and its functional relevance. Such simulations enable detailed exploration of binding strength, structural flexibility, and intermolecular behavior within the system [ 39 ]. The ligand with the strongest binding affinity from docking results was subjected to a 100-nanosecond MD simulation using the Desmond module in the Maestro environment (Schrödinger LLC) [ 40 ]. The OPLS-2005 force field was applied to describe atomic interactions with high accuracy. Before initiating the production run, the system was solvated in an explicit water box, neutralized with counterions (Na⁺ and Cl⁻), and equilibrated under both NVT (constant particle number, volume, and temperature) and NPT (constant particle number, pressure, and temperature) ensembles. Simulations were performed under physiological conditions, maintaining 300 K temperature and 1 bar pressure. Long-range electrostatic interactions were computed using the Particle Mesh Ewald (PME) method for precise energy estimation. Post-simulation analyses included the calculation of root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), and interaction profiling to monitor the stability and conformational behaviour of the complex throughout the trajectory. 3. Result 3.1. Structure Prediction Using AlphaFold and Validation The amino acid sequences corresponding to the coat protein (CP), movement protein (MP), and replicase genes were retrieved from the NCBI database. The Helicase and RNA-dependent RNA polymerase (RdRp) domains were subsequently identified using the InterPro database. These amino acid sequences were utilized to predict the three-dimensional (3D) structures of viral targets using AlphaFold. During structural modeling, the CP monomer exhibited conformational instability, likely due to its small molecular size, which impeded reliable structural validation. To overcome this limitation and ensure a biologically plausible assembly, the CP dimer structure was generated using the AlphaFold Multimer model, whereas the remaining proteins were modeled as monomeric forms. AlphaFold generates five structural models for each protein, from which the top-ranked model was selected based on both the predicted Local Distance Difference Test (pLDDT) score and the predicted Template Modeling (pTM) score. The predicted TM-score (pTM), which ranges from 0 to 1, serves as a quantitative measure of the overall accuracy of the predicted model. A pTM value of 1 represents a perfect structural correspondence with the true conformation, whereas a value approaching 0 indicates minimal structural similarity. In addition, the per-residue confidence of the predicted model was assessed using the predicted Local Distance Difference Test (pLDDT) score, which ranges from 0 to 100. Scores exceeding 90 denote high confidence, while those below 50 suggest regions of low reliability [ 41 ]. For the CP structure, the top-ranked model exhibited pLDDT and pTM scores of 82.9 and 0.66, respectively. Similarly, the MP model showed pLDDT and pTM scores of 83.8 and 0.61, respectively. The RdRp domain structure displayed pLDDT and pTM scores of 88.7 and 0.59, respectively, whereas the Helicase domain model demonstrated the highest confidence, with corresponding scores of 90.1 and 0.79. These top-ranked models were subsequently employed for further analyses. The structural validation of the predicted models was performed using the Ramachandran plot generated by PROCHECK available in the SAVES v6.0 server. The plots for each predicted model are depicted in Fig. S1 , and the corresponding validation statistics are summarized in Table 1 . The selected 3D structures were subsequently energy-minimized and validated to ensure structural reliability, followed by visualization using Discovery Studio. The final predicted structures of the four viral proteins are presented in Fig. 1 . Table 1 Structure validation scores of the modeled ToBRFV proteins: movement protein (MP), coat protein (CP), Helicase domain, and RdRP domain. S. No Parameters MP CP Helicase domain RdRp domain 1 Residues in Most favoured regions 92.7% 91.7% 95.7% 94.7% 2 Residues in Additional allowed regions 7.3% 8.3% 4.3% 5.3% 3 Residues in Generously allowed regions 0.0% 0.0% 0.0% 0.0% 4 Residues in Disallowed regions 0.0% 0.0% 0.0% 0.0% 3.2. Molecular Docking Studies Potential phytochemical inhibitors against the four viral targets were identified through molecular docking analysis. The docking studies were performed within grid boxes generated using the Sitemap tool for all four modeled proteins. The selection of grid boxes was guided by the Dscore (Druggability score), which quantitatively evaluates the potential of a protein’s binding site to interact with small-molecule ligands. A higher Dscore value signifies a more hydrophobic and consequently more druggable binding pocket, whereas a lower, more hydrophilic Dscore indicates a less favorable or “undruggable” site. The Dscore is determined based on parameters such as hydrophobicity, pocket enclosure, and the spatial dimensions of the binding cavity, aiding in the identification of potential drug-binding regions. Generally, Dscore values range from below 0.8 to above 1.0, where higher values denote enhanced druggability, while scores below 0.5 indicate sites with poor drug-binding potential [ 42 ]. Accordingly, the grid boxes for the four viral targets were established based on their respective Dscore values: CP (0.73), MP (0.81), RdRp domain (0.61), and Helicase domain (0.66). This grid box for each of the four viral targets was individually used to perform docking against a library of 2,847 prepared phytochemical compounds. 3.2.1. Movement Protein (MP) Molecular docking analysis of the Movement Protein (MP) with the phytochemical library revealed a broad spectrum of binding affinities across all tested compounds. Based on the highest docking scores, the top three phytochemicals were identified. Panasenoside (–10.04 kcal/mol) formed hydrogen bonds with the amino acid residues Asp34, Glu46, Val47, Glu127, and Val53. Coniferin (–9.80 kcal/mol) interacted via hydrogen bonds with Leu50, Leu55, Val47, Val53, and Asn48. Baimaside (–9.26 kcal/mol) established hydrogen bond interactions with Asp34, Asn48, Leu50, Ala126, and Glu127, indicating strong and specific binding within the MP active site. 3.2.2. Coat Protein (CP) Docking analysis of the Coat Protein (CP) similarly demonstrated a wide range of binding affinities with the phytochemical library. The three top-ranked compounds exhibited the strongest interactions. Kaempferol 3-sophorotrioside (–12.13 kcal/mol) formed hydrogen bonds with Arg47, Arg72, Thr82, Asp89, Asn189, and Gln207. Delphinidin 3-O-(6''-O-malonyl)-β-D-glucoside-3'-O-β-D-glucoside (–12.09 kcal/mol) interacted with Glu128, Asp89, Glu210, Lys213, Ser50, Asn185, Asn193, and Arg47. Ternatin C5 (–11.84 kcal/mol) formed hydrogen bonds with Trp53, Gln207, Asn189, Leu186, Lys213, Asn193, and Arg47, suggesting high binding specificity within the CP active site. 3.2.3. Helicase domain Molecular docking of the Helicase domain revealed diverse binding affinities across the compound library. The top three phytochemicals exhibited the highest binding potential. Violanin (–13.98 kcal/mol) formed hydrogen bonds with Lys59, Asp86, Asp106, His240, Tyr139, and Ile140. Bisdemalonylsalvianin (–13.93 kcal/mol) interacted with Ile140, Asp86, Pro57, Glu245, and Asp106. Albireodelphin A (–13.92 kcal/mol) established multiple hydrogen bond interactions with Asp5, Lys59, Arg68, Glu241, Glu245, Met110, Tyr139, and Asp86, indicating strong engagement with the Helicase domain active site. 3.2.4. RNA-dependent RNA Polymerase (RdRp) domain Docking studies of the RdRp domain demonstrated a wide range of binding affinities for the screened phytochemicals. The three top-ranking compounds were Albireodelphin A (–13.26 kcal/mol), Medicagenic acid 3-O-triglucoside (–13.23 kcal/mol), and Senna (–12.82 kcal/mol). Albireodelphin A formed hydrogen bonds with Asp280, Ile153, His395, Lys394, Asn10, Lys40, and Thr60. Medicagenic acid 3-O-triglucoside established hydrogen bond interactions with His395, Pro57, Thr60, Lys40, and Ile43. Senna interacted via hydrogen bonds with Gln59, Arg17, Ala37, and Lys40, indicating stable binding within the RdRp domain catalytic region. Furthermore, the list of the top ten phytochemicals exhibiting the highest docking scores for each viral target is summarized in Table S1 . Based on these docking results, the top three compounds with the most favorable docking scores were selected for subsequent binding free energy analyses to identify the most potent phytochemical inhibitor against each viral target. 3.3. Binding free energy In addition to docking score evaluation, the binding free energy of the top three phytochemicals from each viral target was calculated using the MM-GBSA (Molecular Mechanics Generalized Born Surface Area) method to determine the most potent inhibitor. The inclusion of solvation energy and solvent-accessible surface area in the ligand–protein binding energy estimations improves the accuracy and reliability of compound ranking. The computed binding free energy (ΔG bind ) values are summarized in Table 2 . Among the analyzed compounds, the one exhibiting the lowest binding free energy in combination with a favorable docking score was identified as the most stable and effective inhibitor. Based on this analysis, Panasenoside was identified as the best inhibitor against MP, Kaempferol 3-sophorotrioside against CP, Violanin against Helicase domain, and Albireodelphin A against RdRp domain. These selected compounds were subsequently subjected to molecular dynamics (MD) simulations to further assess their binding stability and interaction behavior within the respective active sites. The 2D and 3D protein–ligand interaction profiles of these tops are depicted in (Fig. 2 (A–H)). Table 2 Predicted molecular docking scores and binding free energies of the top phytochemical compounds against the ToBRFV viral targets. S.No Pubchem ID Compound Class Docking score (kcal/mol) Binding energy (kcal/mol) Movement protein 1 9986191 Panasenoside Flavonoids > Flavonoids > Flavonols -10.04 -92.61 2 5280372 Coniferin Phenylpropanoids > Monolignols > Coniferyl alcohol derivatives -9.81 -63.48 3 5282166 Baimaside Flavonoids > Flavonoids > Flavonols -9.26 -76.22 Coat Protein 1 5282156 Kaempferol 3-sophorotrioside Flavonoids > Flavonoids > Flavonols -12.13 -86.34 2 23724703 Delphinidin 3-O-(6''-O-malonyl)-beta-D-glucoside-3'-O-beta-D-glucoside Flavonoids > Flavonoids > Anthocyanidins and anthocyanins -12.09 -81.69 3 10843319 Ternatin C5 Flavonoids > Flavonoids > Anthocyanidins and anthocyanins -11.84 -44.71 Helicase domain 1 23724701 Violanin Flavonoids > Flavonoids > Anthocyanidins and anthocyanins -13.98 -98.65 2 5282161 Bisdemalonylsalvianin Flavonoids > Flavonoids > Anthocyanidins and anthocyanins -13.93 -44.21 3 6444318 Albireodelphin A Flavonoids > Flavonoids > Anthocyanidins and anthocyanins -13.92 -67.84 RdRp domain 1 6444318 Albireodelphin A Polyketides > Flavonoids > Anthocyanidins -13.26 -78.96 2 441932 Medicagenic acid 3-O-triglucoside Terpenoids > Triterpenoids -13.23 -77.71 3 73111 Senna Polyketides > Anthraquinones > Anthrone type -12.82 -61.35 3.4. Molecular Dynamics Molecular dynamics (MD) simulations were conducted to gain deeper insights into the dynamic behavior of the docked ligand–protein complexes. This analysis aimed to evaluate the stability and conformational quality of the complexes over time. Several structural parameters, including root mean square deviation (RMSD), root mean square fluctuation (RMSF), and protein–ligand interaction profiles, were assessed throughout a 100-ns simulation trajectory. These parameters were employed to comprehensively examine the flexibility, stability, and interaction dynamics of each of the four viral target–ligand complexes individually. Root Mean Square Deviation (RMSD) analysis of the Cα atoms, representing the protein backbone, was carried out over the 100-nanosecond simulation trajectory to assess the conformational stability and dynamic flexibility of the protein structures. The RMSD profiles of the four viral modeled proteins showed deviations ranging approximately from 2–2.5 Å for MP, 6–7 Å for CP, 2.5–3.3 Å for Helicase domain, and 3–4 Å for RdRp domain. Although slight fluctuations in the Cα atoms were observed during the initial stages of the simulation, all four protein–ligand complexes gradually stabilized and reached equilibrium toward the end of the simulation period. No abnormal deviations were detected, suggesting consistent structural stability across all systems. These observations confirm that the proteins maintained their folded conformations throughout the simulation, and the ligand interactions did not induce any significant structural perturbations. Analysis of ligand RMSD revealed that Panasenoside bound to MP exhibited an initial peak of ~ 4 Å but stabilized after 40 ns, maintaining fluctuations around 2.5 Å, suggesting a flexible yet persistent interaction within the MP binding site. Kaempferol 3-sophorotrioside bound to CP achieved stable RMSD values after 20 ns, with an average ligand RMSD of ~ 7 Å, and the overlap of ligand and protein RMSD trajectories indicated sustained interactions within the CP binding cavity. Violanin bound to Helicase domain maintained a stable configuration throughout the 100-ns simulation, with a ligand RMSD of ~ 3.5 Å consistently below the protein RMSD, indicating persistent binding within the Helicase domain pocket. Similarly, Albireodelphin A bound to RdRp domain showed initial stability during the first 30 ns, followed by slight fluctuations, ultimately maintaining a ligand RMSD of ~ 4.8 Å at the end of the simulation, with the ligand RMSD remaining below the protein RMSD, reflecting stable engagement within the RdRp domain binding site. The detailed RMSD trajectories representing the conformational stability of all four protein–ligand complexes are illustrated in Fig. 3 . As illustrated in Fig. 4 , the residue-level flexibility of the proteins was analyzed using Root Mean Square Fluctuation (RMSF) over a 100-nanosecond molecular dynamics simulation. RMSF quantifies the extent of deviation of each amino acid residue from its average position throughout the simulation, thereby providing valuable insights into the local mobility and dynamic behavior of the protein structure. In contrast to RMSD, which evaluates the overall conformational deviation of the protein backbone (Cα atoms), RMSF focuses on residue-specific movements, particularly assessing the flexibility of side chains within the binding pocket. Across all analyzed complexes, residue fluctuations were primarily observed around ~ 4 Å. Although a universally accepted RMSF threshold for modeled proteins has not been established, previous molecular dynamics studies have generally reported that flexible loop regions exhibit fluctuations within the 1–4 Å range, while residues located in the structural core typically fluctuate below ~ 2 Å. In the present analysis, most residues within the active site exhibited comparatively lower RMSF values, signifying the rigidity and structural stability of the binding pocket, despite minor variations in the loop or terminal regions. These findings are consistent with earlier molecular dynamics studies conducted on modeled protein structures. The analysis of protein–ligand interactions provided valuable insights into the dynamic behavior of the complexes, elucidating their structural stability and revealing a strong correlation with the observed molecular dynamics profiles. The physicochemical properties and spatial orientation of amino acid residues within the binding pocket play a pivotal role in maintaining the integrity and stability of protein–ligand associations. For the MP–Panasenoside complex, the ligand exhibited strong and persistent hydrogen bond interactions with residues Asp34, Val130, and Trp131, displaying interaction fractions of 1.75, 0.75, and 0.74, respectively. The interaction fraction represents the persistence of specific contacts throughout the molecular dynamics simulation, where a value of 1.0 denotes a continuous interaction across 100% of the simulation frames, while values exceeding 1.0 indicate that a residue can form multiple concurrent interactions with the ligand within a single frame. Similarly, in the CP–Kaempferol 3-sophorotrioside complex, residues Asn185, Asn189, and Glu182 exhibited interaction fractions of 0.9, 0.7, and 0.6, respectively, signifying stable hydrogen bonding within the binding pocket. For the Helicase domain-Violanin complex, residues Arg142, Ile140, and Asp106 demonstrated notable interaction fractions of 1.35, 1.05, and 1.00, respectively, reflecting consistent and robust interactions throughout the simulation trajectory. Likewise, in the RdRp–Albireodelphin A complex, residues Asp280, Thr60, and Ala155 showed interaction fractions of 1.95, 1.00, and 0.75, indicating persistent and stable hydrogen bonding with the ligand. Collectively, these findings highlight the presence of strong, stable, and often multiple hydrogen-bonding interactions between the phytochemical ligands and their respective viral protein targets, underscoring the robustness and consistency of these associations throughout the simulation period. The detailed interaction profiles of all four complexes are depicted in Fig. 5 . 4. Discussion Emergent plant viruses pose a continual threat, necessitating rapid interventions to prevent or limit the spread of novel pathogens. Tomato brown rugose fruit virus (ToBRFV) is a highly contagious, and destructive tobamovirus, with tomato as its primary host. Within a few years of its initial discovery in Israel and Jordan, ToBRFV disseminated to multiple countries worldwide. Over the past three years, extensive research has focused on understanding its transmission, distribution, host range, prevention strategies, and the development of rapid and specific detection methods. Since its emergence, ToBRFV has caused substantial losses in the global tomato industry and is now recognized as a plant quarantine pathogen of international concern [ 6 , 43 ]. Considering the increasing global threat posed by ToBRFV and other emerging plant viruses, the development of novel, sustainable, and environmentally compatible antiviral strategies has become essential. In the present study, molecular modeling, molecular docking, and molecular dynamics (MD) simulations were employed to identify potential phytochemical inhibitors targeting the coat protein (CP), movement protein (MP), helicase domain, and RNA-dependent RNA polymerase (RdRP) domain of ToBRFV. The three-dimensional structures of these viral proteins were generated using AlphaFold. As the CP monomer exhibited structural instability due to its small size, a CP dimer was constructed using the AlphaFold Multimer model. Dimeric forms of tobamoviral coat proteins are well established, as seen in TMV, where the coat protein exists in monomeric, dimeric, and trimeric states [ 44 ]. Similarly, a significant proportion of TMV strain flavum coat protein in infected tissues exists predominantly as a dimeric form [ 45 ]. Hence, the CP dimer model was considered structurally valid and used for molecular docking against the phytochemical library. The docking studies, supported by MM-GBSA binding free energy analysis, identified the top-scoring phytochemical for each viral target. Based on this analysis, Panasenoside (–10.04 kcal/mol) exhibited the strongest inhibitory potential against MP, Kaempferol 3-sophorotrioside (–12.13 kcal/mol) against CP, Violanin (–13.98 kcal/mol) against Helicase domain, and Albireodelphin A (–13.26 kcal/mol) against RdRP domain. Previous studies have highlighted the antiviral potential of phytochemicals such as curcumin, demethoxycurcumin, and bisdemethoxycurcumin from Curcuma longa, which showed strong binding with key viral proteins of Potato virus Y [ 46 ] and TYLCV-Sardinia Rep protein [ 47 ]. Similarly, kaempferol, quercetin, and luteolin were identified as potent inhibitors of Tobacco mosaic virus structural and replicase proteins [ 48 ]. Panasenoside, a flavonoid derived from Panax ginseng , has shown strong binding affinity against early Huntington’s disease-related proteins in computational studies [ 49 ]. Kaempferol 3-sophorotrioside, found in Pisum sativum seed pods and Solanum melongena, exhibits notable antioxidant, hepatoprotective, and antitumor activities [ 50 , 51 ]. Violanin, a flavonol glycoside and major anthocyanin present in the petals of Viola species such as Viola cornuta , demonstrates significant bioactivity [ 52 ]. Albireodelphin A, a glycosylated anthocyanin flavonoid, has been reported as a potent inhibitor of SARS-CoV-2 proteins, including the main protease, RdRp, and spike protein, in molecular docking studies [ 53 ]. The consistent identification of flavonoids as the top inhibitors across all ToBRFV targets highlights the potential of this class of compounds as effective antiviral agents against this virus. Among plant-derived bioactive compounds, flavonoids are particularly significant, with flavonoids receiving considerable research attention. Flavonoids are a major class of naturally occurring polyphenolic secondary metabolites widely distributed in fruits, vegetables, and other food crops [ 54 ]. To date, over 10,000 flavonoids have been identified, exhibiting remarkable structural diversity and a broad spectrum of pharmacological properties [ 55 ]. Their antiviral activities have been extensively explored, demonstrating inhibitory effects against a wide range of DNA and RNA viruses through mechanisms such as interference with viral entry, replication, translation, and release, as well as modulation of host immune responses [ 56 – 58 ]. Viruses rely on host metabolism and cellular machinery for replication and propagation, exploiting host cells to spread throughout the organism [ 59 ]. Flavonoids can disrupt viral infection through multiple mechanisms, including obstruction of viral attachment and entry, inhibition of genome replication, interference with protein translation and polyprotein processing, and prevention of viral release to adjacent host cells [ 60 ]. Flavonoid glycosides isolated from Clematis lasiandra Maxim, including quercetin-3-O-rutinoside and kaempferol-3-O-rutinoside, exhibited potent antiviral activity against Tobacco mosaic virus (TMV) by directly interacting with viral coat proteins, leading to structural disruption of viral particles and suppression of viral transmission and replication [ 61 ]. Similarly, molecular docking studies of flavonoids such as quercetin, kaempferol, and luteolin with viral proteins of Chilli leaf curl Ahmedabad virus (ChiLCAV) revealed strong binding affinities, indicating their potential inhibitory roles in infection [ 62 ]. Flavonoid-benzothiazole derivatives also demonstrated potent antiviral activity against TMV, exhibiting significant interactions with viral target proteins through both molecular and biological evaluations [ 63 ]. In addition to molecular docking and MM-GBSA calculations, the stability of the four selected complexes, MP-Panasenoside, CP-Kaempferol 3-sophorotrioside, Helicase domain-Violanin, and RdRP domain-Albireodelphin A, was further evaluated using molecular dynamics simulations. The results revealed that the MP–Panasenoside and Helicase–Violanin complexes exhibited pronounced stability throughout the 100-ns simulation, while minor fluctuations observed in the CP and RdRP domain complexes did not compromise overall ligand binding. All four protein–ligand systems maintained structural integrity and stable interactions within their respective binding pockets. The RMSD and RMSF values of the complexes remained within acceptable limits for modeled protein, consistent with previously published studies [ 64 , 65 ]. 5. Conclusion In conclusion, this study integrated molecular modeling, docking, MM-GBSA, and molecular dynamics simulations to identify potential phytochemical inhibitors targeting key proteins of Tomato brown rugose fruit virus (ToBRFV), including the coat protein, movement protein, helicase domain, and RdRp domain. Among the screened compounds, Panasenoside, Kaempferol 3-sophorotrioside, Violanin, and Albireodelphin A demonstrated the strongest binding affinities and stable interactions, with flavonoids emerging as the most promising antiviral candidates. Molecular dynamics simulations further confirmed the stability and strong interaction profiles of these protein–ligand complexes. Furthermore, the combined application of these four compounds could be explored in foliar spray formulations to evaluate their synergistic antiviral efficacy under experimental conditions. This study also emphasizes the significance of flavonoids as potent antiviral agents, suggesting that targeted manipulation of flavonoid biosynthetic pathways to enhance their accumulation may serve as a viable strategy for developing flavonoid-enriched, virus-resistant tomato plants. Future in vitro and in vivo investigations are essential to validate these computational predictions and to advance the development of sustainable, flavonoid-based antiviral strategies for effective ToBRFV management. Declarations Acknowledgements The authors acknowledge the Director, CSIR-NBRI, for providing the necessary computational hardware and software support. Author Contributions VS conceived and designed the experiments. NV, NPM and GJ performed the experiments and analysed the data. The original draft was written by NV, NPM, CM, AS, SW. Review and editing were carried out by SK, VS, CVR. All authors read and approved the final manuscript. Funding Not applicable Data Availability All data generated and analyzed during this study are included in the article. Ethics approval and consent to participate This study does not involve any clinical trial or experimentation involving human or animal subjects. Consent for publication Not applicable Competing interests The authors declare no competing interests. References Savary S, Willocquet L, Pethybridge SJ, et al. The global burden of pathogens and pests on major food crops. Nat Ecol Evol. 2019;3:430–9. https://doi.org/10.1038/s41559-018-0793-y . Adams MJ, Antoniw JF, Kreuze J. Virgaviridae: a new family of rod-shaped plant viruses. Arch Virol. 2009;154(12):1967–72. 10.1007/s00705-009-0506-6 . Pagán I, Firth C, Holmes EC. Phylogenetic analysis reveals rapid evolutionary dynamics in the plant RNA virus genus Tobamovirus . 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Institute","correspondingAuthor":true,"prefix":"","firstName":"Vijayanandraj","middleName":"","lastName":"Selvaraj","suffix":""}],"badges":[],"createdAt":"2026-01-08 10:09:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8550076/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8550076/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102746047,"identity":"0c12fbb5-066f-4993-93d2-4513ae1d84e8","added_by":"auto","created_at":"2026-02-16 08:55:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2298375,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted three-dimensional (3D) structures of the four ToBRFV viral proteins generated using AlphaFold: (A) Movement Protein (MP), (B) Coat Protein (CP), (C) Helicase domain, and (D) RNA-dependent RNA Polymerase (RdRp) domain.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8550076/v1/7ca8245eada2b77a8377e0ad.png"},{"id":102478480,"identity":"25f38751-1ac0-410f-8365-8abae7024d96","added_by":"auto","created_at":"2026-02-12 06:20:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2222444,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentation of the three-dimensional (3D) and two-dimensional (2D) binding interactions between the selected phytochemicals and ToBRFV target proteins: (A, B) Movement Protein –Panasenoside, (C, D) Coat Protein –Kaempferol 3-sophorotrioside, (E, F) Helicase domain –Violanin, and (G, H) RdRP domain –Albireodelphin A.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8550076/v1/16732b3aa6eddcf8940cd563.png"},{"id":102478483,"identity":"4301ac76-1ff1-4c2c-9f50-65a69d2f4274","added_by":"auto","created_at":"2026-02-12 06:20:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2131189,"visible":true,"origin":"","legend":"\u003cp\u003eRoot Mean Square Deviation (RMSD) profiles of ToBRFV protein–ligand complexes obtained from molecular dynamics simulations: (A) Movement Protein–Panasenoside, (B) Coat Protein –Kaempferol 3-sophorotrioside, (C) Helicase domain –Violanin, and (D) RdRP domain –Albireodelphin A.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8550076/v1/3ec24b8409911152e6863700.png"},{"id":102478485,"identity":"d519a2bb-c4e5-40b7-92a5-2d5e0281a737","added_by":"auto","created_at":"2026-02-12 06:20:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1165164,"visible":true,"origin":"","legend":"\u003cp\u003eRoot Mean Square Fluctuation (RMSF) plots of ToBRFV protein–ligand complexes: (A) Movement Protein –Panasenoside, (B) Coat Protein –Kaempferol 3-sophorotrioside, (C) Helicase domain –Violanin, and (D) RdRP domain –Albireodelphin A.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8550076/v1/2cb83de9a279118bf511ad56.png"},{"id":102478481,"identity":"dad37bb8-99b2-4916-b7c6-beedf02d7126","added_by":"auto","created_at":"2026-02-12 06:20:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1042470,"visible":true,"origin":"","legend":"\u003cp\u003eProtein–ligand interaction profiles and percentage occupancy in ToBRFV protein–ligand complexes: (A) Movement Protein –Panasenoside, (B) Coat Protein –Kaempferol 3-sophorotrioside, (C) Helicase domain –Violanin, and (D) RdRP domain –Albireodelphin A.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8550076/v1/66d8ffff4a09d3db0c0e826d.png"},{"id":102750531,"identity":"4f794185-c676-4351-83db-8906253a3d4b","added_by":"auto","created_at":"2026-02-16 09:20:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9613746,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8550076/v1/85767cb4-edec-42de-b0f1-10fc659c37f1.pdf"},{"id":102478482,"identity":"ae554274-bb2e-463e-86ba-dc92f98c4a8d","added_by":"auto","created_at":"2026-02-12 06:20:35","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":271991,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTabletb.docx","url":"https://assets-eu.researchsquare.com/files/rs-8550076/v1/da54055a8b62d741a11922d6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Computational Identification of Novel Inhibitors Targeting Multiple Proteins of Tomato Brown Rugose Fruit Virus (ToBRFV) Through AlphaFold-Based Protein Modeling, Molecular Docking, MM/GBSA Binding Free Energy Analysis, and Molecular Dynamics Simulation","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePlant viral diseases pose a significant threat to global crop yields and quality, causing substantial economic losses annually. Tomato (\u003cem\u003eLycopersicon esculentum\u003c/em\u003e), ranking as the second most important vegetable crop after potato, suffers notable yield reductions due to viral infections, with global crop losses from plant diseases estimated to exceed 30% per year, translating into economic damages worth hundreds of billions of dollars [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Among plant viruses, tobamoviruses represent serious threats to vegetable and ornamental crops worldwide [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. They are transmitted over long distances \u003cem\u003evia\u003c/em\u003e contaminated seeds and spread mechanically between plants through cultural practices or circulating water, particularly in hydroponic systems [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Mechanical transmission through contact, contaminated tools, and potentially infected seeds facilitates rapid dissemination, especially in controlled environments such as greenhouses [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn April 2015, tomato plants (\u003cem\u003ecv.\u003c/em\u003e Candela) grown in greenhouses in the Jordan Valley exhibited mild foliar symptoms alongside severe brown rugose symptoms on fruits, which significantly impacted marketability [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Based on symptomatology and disease distribution, a viral etiology was suspected. The causal agent was subsequently isolated and identified using bioassays and molecular diagnostics and was classified by the International Committee on Taxonomy of Viruses (ICTV) as a novel species within the genus \u003cem\u003eTobamovirus\u003c/em\u003e, named \u003cem\u003eTomato brown rugose fruit virus\u003c/em\u003e (ToBRFV). A closely related isolate with high sequence identity to the Jordanian Tom-1 strain, reported during a severe outbreak in southern Israel in 2014 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Within a year, ToBRFV spread extensively across Israel and was detected in multiple growing regions. Concurrent outbreaks in neighbouring countries, including Jordan, occurred during the 2014/2015 winter growing season. The virus has since spread rapidly, with reported occurrences in 35 countries across Asia, Europe, North America, and Africa. Disease incidence during these outbreaks often approached 100%, with pronounced brown rugose fruit symptoms severely reducing marketability despite relatively mild foliar symptoms [\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. ToBRFV is currently considered the most serious threat to global tomato production.\u003c/p\u003e \u003cp\u003e Symptom expression in tomato varies according to cultivar, plant age at infection, and environmental factors such as temperature, photoperiod, and cultivation system. Leaf symptoms commonly include mosaic patterns, dark green blistering, and leaf narrowing, while occasional necrotic lesions on peduncles, calyxes, and petioles, as well as longitudinal stem necrosis, have been reported [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Fruit symptoms typically manifest as brown, rugose, or wrinkled patches [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. ToBRFV is primarily seed-borne, with contaminated seeds serving as the major pathway for long-distance dissemination [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Seeds harvested from infected fruits are fully contaminated, although the virus is detected only on the external seed coat (testa) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGlobal ToBRFV isolates are highly similar genetically, sharing over 99% nucleotide identity, although structural variations may influence traits such as virulence [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. India, as a major tomato producer and supplier of seed material for solanaceous crops, faces significant risk from ToBRFV, which spreads via plant-to-plant contact and contaminated seeds. In India, ToBRFV was detected in symptomatic tomato (\u003cem\u003eSolanum lycopersicum\u003c/em\u003e) plants in open fields in Karnataka and Maharashtra in May 2023. Affected plants exhibited mosaic patterns, mottling, chlorosis, leaf deformation, necrotic spots, and brown rugose patches on fruits. Laboratory analyses, including DAS-ELISA, RT-PCR, and sequencing, confirmed ToBRFV presence in 11 symptomatic fruit samples [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe ToBRFV genome closely resembles that of other tobamoviruses such as TMV, ToMV, and Rehmannia mosaic virus, sharing 81\u0026ndash;82% nucleotide identity. Its genome consists of a single-stranded positive-sense RNA of approximately 6,400 nucleotides encoding four open reading frames (ORFs) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The RNA is capped with a short 5\u0026prime; untranslated region (UTR) containing CAA repeats and a 3\u0026prime; UTR capable of forming pseudoknots, followed by a tRNA-like amino acid\u0026ndash;accepting structure. ORF1 and ORF2, overlapping \u003cem\u003evia\u003c/em\u003e a leaky stop codon, encode a 126-kDa protein with methyltransferase and helicase domains and a 183-kDa readthrough protein, which together function as subunits of the RNA-dependent RNA polymerase (RdRP) [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. ORF3 encodes the movement protein (~\u0026thinsp;28\u0026ndash;31 kDa) responsible for cell-to-cell transport via plasmodesmata, while ORF4 encodes the coat protein (~\u0026thinsp;17.5 kDa) forming the viral capsid. Both ORF3 and ORF4 are expressed from subgenomic RNAs produced during replication [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. To identify potential inhibitors against ToBRFV, essential viral proteins involved in replication, movement, and encapsidation were selected as molecular targets for further investigation.\u003c/p\u003e \u003cp\u003eIn this regard, natural compounds, especially phytochemicals, are gaining prominence over synthetic chemicals in the development of antiviral formulations and plant protection measures. Their eco-friendly and sustainable characteristics make these plant-derived molecules a safer and more environmentally sound alternative to conventional synthetic agents for controlling viral infections [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. A wide variety of naturally occurring phytochemicals, including flavonoids, carotenoids, polyphenols, isoprenoids, phytosterols, saponins, alkaloids, and polysaccharides, are well known for their potent antioxidant capabilities and diverse biological functions. These encompass antimicrobial, antiviral, antidiarrheal, anthelmintic, antiallergic, and antispasmodic activities [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Considering the limitations of experimentally testing large libraries of natural metabolites, computational techniques such as molecular docking and molecular dynamics (MD) simulations offer efficient and economical tools for the early-stage discovery and design of potential antiviral molecules [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. So far, there have been no computational studies focusing on the identification of inhibitors against ToBRFV. Hence, the current work is designed to explore plant-derived phytochemicals with the potential to inhibit crucial viral proteins such as the movement protein (MP), coat protein (CP), Helicase, and the RdRP domain of ToBRFV. The study utilized molecular docking to perform preliminary virtual screening, which was subsequently complemented by molecular dynamics (MD) simulations and binding free energy (MM-GBSA) calculations to evaluate the stability and interaction strength of the most promising protein\u0026ndash;ligand complexes.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Sequence retrieval and 3D Structure Prediction\u003c/h2\u003e \u003cp\u003eThe nucleotide sequences encoding the coat protein (CP), movement protein (MP), and replicase protein of tomato brown rugose fruit virus (NCBI Accession ID: KT383474) were retrieved in FASTA format from the NCBI GenBank database. Four distinct protein targets were selected for structural prediction: MP, CP, Helicase domain, and RdRp domain of the replicase protein. The RdRp domain and helicase domain present in the replicase gene were identified using InterPro [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Three-dimensional (3D) structure prediction of the selected proteins was carried out using ColabFold (v1.5.5) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Protein sequences were provided as input in FASTA format, and Multiple Sequence Alignments (MSAs) were generated using the MMseqs2 algorithm. Amber relaxation was disabled, and predictions were performed using the unpaired MSA option. For modeling the dimeric structure of CP, the Alphafold2_multimer_v3 model was employed [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. AlphaFold predicts residue-level 3D coordinates and assigns confidence scores based on the predicted Local Distance Difference Test (pLDDT), which ranges from 0 to 100, indicating the reliability of each prediction. The top-ranked high-confidence models for all four targets were selected and subsequently subjected to energy minimization using the YASARA Energy Minimization Server to enhance structural stability [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Structural validation of the energy-minimized models was performed using the SAVES v6.0 server [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Ligand Preparation and Binding Site Prediction\u003c/h2\u003e \u003cp\u003eA total of 2,847 phytochemical compounds were retrieved from the PubChem database [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/classification/#hid=5\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/classification/#hid=5\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and processed using the LigPrep module of Schr\u0026ouml;dinger Suite 2024-4. The ligand preparation protocol involved desalting, protonation, and the generation of stereoisomers, tautomeric states, and ionization variants at physiological pH (7\u0026thinsp;\u0026plusmn;\u0026thinsp;2) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The predicted three-dimensional structures of the four viral targets of ToBRFV were optimized using the Protein Preparation Wizard in Schr\u0026ouml;dinger, which included the addition of hydrogen atoms, optimization of hydrogen-bonding networks, and energy minimization to enhance structural accuracy. Binding site identification was subsequently performed using the SiteMap tool, where pockets were evaluated based on cavity volume, hydrophobic\u0026ndash;hydrophilic characteristics, and hydrogen bond donor\u0026ndash;acceptor profiles. The top-ranked binding site was selected, and a receptor grid was generated with Glide to define the docking region for virtual screening [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Molecular Docking Studies\u003c/h2\u003e \u003cp\u003eThe receptor grids generated at the predicted active sites of all four ToBRFV viral targets were utilized as docking receptors. Molecular docking was carried out using the Glide module within the Schr\u0026ouml;dinger Suite. The docking procedure applied the Extra Precision (XP) mode to improve the accuracy of binding pose prediction and binding affinity estimation [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Standard docking flexibility parameters were employed, including penalties for non-planar amide conformations. Protein\u0026ndash;ligand interactions, such as hydrogen bonds and hydrophobic contacts, were examined through the Maestro interface to evaluate binding orientation and affinity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Molecular Mechanics Generalized Born Surface Area (MM-GBSA) Calculations\u003c/h2\u003e \u003cp\u003eTo assess the binding strength between ligands and the four viral targets of ToBRFV, binding free energy calculations were performed using the Molecular Mechanics/Generalized Born Surface Area (MM-GBSA) approach. This analysis was conducted with the Prime module of the Schr\u0026ouml;dinger Suite. For the evaluation, only the top-ranked ligand\u0026ndash;protein complexes, identified by the most favorable docking scores, were considered. The solvation effects were represented using the VSGB 2.0 implicit solvent model in combination with the OPLS-2005 force field [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In addition, the method estimated ligand strain energy by positioning the ligand within the solvent environment automatically generated by the VSGB 2.0 model under the OPLS-2005 force field framework [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Molecular Dynamics (MD) Simulations with Desmond\u003c/h2\u003e \u003cp\u003eMolecular dynamics (MD) simulations were carried out to investigate the stability of ligand\u0026ndash;protein complexes, emphasizing the conformational dynamics of the macromolecule and its functional relevance. Such simulations enable detailed exploration of binding strength, structural flexibility, and intermolecular behavior within the system [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The ligand with the strongest binding affinity from docking results was subjected to a 100-nanosecond MD simulation using the Desmond module in the Maestro environment (Schr\u0026ouml;dinger LLC) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The OPLS-2005 force field was applied to describe atomic interactions with high accuracy. Before initiating the production run, the system was solvated in an explicit water box, neutralized with counterions (Na⁺ and Cl⁻), and equilibrated under both NVT (constant particle number, volume, and temperature) and NPT (constant particle number, pressure, and temperature) ensembles. Simulations were performed under physiological conditions, maintaining 300 K temperature and 1 bar pressure. Long-range electrostatic interactions were computed using the Particle Mesh Ewald (PME) method for precise energy estimation. Post-simulation analyses included the calculation of root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), and interaction profiling to monitor the stability and conformational behaviour of the complex throughout the trajectory.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Structure Prediction Using AlphaFold and Validation\u003c/h2\u003e \u003cp\u003eThe amino acid sequences corresponding to the coat protein (CP), movement protein (MP), and replicase genes were retrieved from the NCBI database. The Helicase and RNA-dependent RNA polymerase (RdRp) domains were subsequently identified using the InterPro database. These amino acid sequences were utilized to predict the three-dimensional (3D) structures of viral targets using AlphaFold. During structural modeling, the CP monomer exhibited conformational instability, likely due to its small molecular size, which impeded reliable structural validation. To overcome this limitation and ensure a biologically plausible assembly, the CP dimer structure was generated using the AlphaFold Multimer model, whereas the remaining proteins were modeled as monomeric forms. AlphaFold generates five structural models for each protein, from which the top-ranked model was selected based on both the predicted Local Distance Difference Test (pLDDT) score and the predicted Template Modeling (pTM) score. The predicted TM-score (pTM), which ranges from 0 to 1, serves as a quantitative measure of the overall accuracy of the predicted model. A pTM value of 1 represents a perfect structural correspondence with the true conformation, whereas a value approaching 0 indicates minimal structural similarity. In addition, the per-residue confidence of the predicted model was assessed using the predicted Local Distance Difference Test (pLDDT) score, which ranges from 0 to 100. Scores exceeding 90 denote high confidence, while those below 50 suggest regions of low reliability [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. For the CP structure, the top-ranked model exhibited pLDDT and pTM scores of 82.9 and 0.66, respectively. Similarly, the MP model showed pLDDT and pTM scores of 83.8 and 0.61, respectively. The RdRp domain structure displayed pLDDT and pTM scores of 88.7 and 0.59, respectively, whereas the Helicase domain model demonstrated the highest confidence, with corresponding scores of 90.1 and 0.79. These top-ranked models were subsequently employed for further analyses. The structural validation of the predicted models was performed using the Ramachandran plot generated by PROCHECK available in the SAVES v6.0 server. The plots for each predicted model are depicted in Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, and the corresponding validation statistics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The selected 3D structures were subsequently energy-minimized and validated to ensure structural reliability, followed by visualization using Discovery Studio. The final predicted structures of the four viral proteins are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStructure validation scores of the modeled ToBRFV proteins: movement protein (MP), coat protein (CP), Helicase domain, and RdRP domain.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHelicase domain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRdRp domain\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResidues in Most favoured regions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e95.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e94.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResidues in Additional allowed regions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResidues in Generously allowed regions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResidues in Disallowed regions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Molecular Docking Studies\u003c/h2\u003e \u003cp\u003ePotential phytochemical inhibitors against the four viral targets were identified through molecular docking analysis. The docking studies were performed within grid boxes generated using the Sitemap tool for all four modeled proteins. The selection of grid boxes was guided by the Dscore (Druggability score), which quantitatively evaluates the potential of a protein\u0026rsquo;s binding site to interact with small-molecule ligands. A higher Dscore value signifies a more hydrophobic and consequently more druggable binding pocket, whereas a lower, more hydrophilic Dscore indicates a less favorable or \u0026ldquo;undruggable\u0026rdquo; site. The Dscore is determined based on parameters such as hydrophobicity, pocket enclosure, and the spatial dimensions of the binding cavity, aiding in the identification of potential drug-binding regions. Generally, Dscore values range from below 0.8 to above 1.0, where higher values denote enhanced druggability, while scores below 0.5 indicate sites with poor drug-binding potential [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Accordingly, the grid boxes for the four viral targets were established based on their respective Dscore values: CP (0.73), MP (0.81), RdRp domain (0.61), and Helicase domain (0.66). This grid box for each of the four viral targets was individually used to perform docking against a library of 2,847 prepared phytochemical compounds.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. Movement Protein (MP)\u003c/h2\u003e \u003cp\u003eMolecular docking analysis of the Movement Protein (MP) with the phytochemical library revealed a broad spectrum of binding affinities across all tested compounds. Based on the highest docking scores, the top three phytochemicals were identified. Panasenoside (\u0026ndash;10.04 kcal/mol) formed hydrogen bonds with the amino acid residues Asp34, Glu46, Val47, Glu127, and Val53. Coniferin (\u0026ndash;9.80 kcal/mol) interacted via hydrogen bonds with Leu50, Leu55, Val47, Val53, and Asn48. Baimaside (\u0026ndash;9.26 kcal/mol) established hydrogen bond interactions with Asp34, Asn48, Leu50, Ala126, and Glu127, indicating strong and specific binding within the MP active site.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. Coat Protein (CP)\u003c/h2\u003e \u003cp\u003eDocking analysis of the Coat Protein (CP) similarly demonstrated a wide range of binding affinities with the phytochemical library. The three top-ranked compounds exhibited the strongest interactions. Kaempferol 3-sophorotrioside (\u0026ndash;12.13 kcal/mol) formed hydrogen bonds with Arg47, Arg72, Thr82, Asp89, Asn189, and Gln207. Delphinidin 3-O-(6''-O-malonyl)-β-D-glucoside-3'-O-β-D-glucoside (\u0026ndash;12.09 kcal/mol) interacted with Glu128, Asp89, Glu210, Lys213, Ser50, Asn185, Asn193, and Arg47. Ternatin C5 (\u0026ndash;11.84 kcal/mol) formed hydrogen bonds with Trp53, Gln207, Asn189, Leu186, Lys213, Asn193, and Arg47, suggesting high binding specificity within the CP active site.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3. Helicase domain\u003c/h2\u003e \u003cp\u003eMolecular docking of the Helicase domain revealed diverse binding affinities across the compound library. The top three phytochemicals exhibited the highest binding potential. Violanin (\u0026ndash;13.98 kcal/mol) formed hydrogen bonds with Lys59, Asp86, Asp106, His240, Tyr139, and Ile140. Bisdemalonylsalvianin (\u0026ndash;13.93 kcal/mol) interacted with Ile140, Asp86, Pro57, Glu245, and Asp106. Albireodelphin A (\u0026ndash;13.92 kcal/mol) established multiple hydrogen bond interactions with Asp5, Lys59, Arg68, Glu241, Glu245, Met110, Tyr139, and Asp86, indicating strong engagement with the Helicase domain active site.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.2.4. RNA-dependent RNA Polymerase (RdRp) domain\u003c/h2\u003e \u003cp\u003eDocking studies of the RdRp domain demonstrated a wide range of binding affinities for the screened phytochemicals. The three top-ranking compounds were Albireodelphin A (\u0026ndash;13.26 kcal/mol), Medicagenic acid 3-O-triglucoside (\u0026ndash;13.23 kcal/mol), and Senna (\u0026ndash;12.82 kcal/mol). Albireodelphin A formed hydrogen bonds with Asp280, Ile153, His395, Lys394, Asn10, Lys40, and Thr60. Medicagenic acid 3-O-triglucoside established hydrogen bond interactions with His395, Pro57, Thr60, Lys40, and Ile43. Senna interacted via hydrogen bonds with Gln59, Arg17, Ala37, and Lys40, indicating stable binding within the RdRp domain catalytic region.\u003c/p\u003e \u003cp\u003eFurthermore, the list of the top ten phytochemicals exhibiting the highest docking scores for each viral target is summarized in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Based on these docking results, the top three compounds with the most favorable docking scores were selected for subsequent binding free energy analyses to identify the most potent phytochemical inhibitor against each viral target.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Binding free energy\u003c/h2\u003e \u003cp\u003eIn addition to docking score evaluation, the binding free energy of the top three phytochemicals from each viral target was calculated using the MM-GBSA (Molecular Mechanics Generalized Born Surface Area) method to determine the most potent inhibitor. The inclusion of solvation energy and solvent-accessible surface area in the ligand\u0026ndash;protein binding energy estimations improves the accuracy and reliability of compound ranking. The computed binding free energy (ΔG\u003csub\u003ebind\u003c/sub\u003e) values are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Among the analyzed compounds, the one exhibiting the lowest binding free energy in combination with a favorable docking score was identified as the most stable and effective inhibitor. Based on this analysis, Panasenoside was identified as the best inhibitor against MP, Kaempferol 3-sophorotrioside against CP, Violanin against Helicase domain, and Albireodelphin A against RdRp domain. These selected compounds were subsequently subjected to molecular dynamics (MD) simulations to further assess their binding stability and interaction behavior within the respective active sites. The 2D and 3D protein\u0026ndash;ligand interaction profiles of these tops are depicted in (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(A\u0026ndash;H)).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePredicted molecular docking scores and binding free energies of the top phytochemical compounds against the ToBRFV viral targets.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS.No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePubchem ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCompound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDocking score (kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBinding energy (kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eMovement protein\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9986191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePanasenoside\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFlavonoids\u0026thinsp;\u0026gt;\u0026thinsp;Flavonoids\u0026thinsp;\u0026gt;\u0026thinsp;Flavonols\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-10.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-92.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5280372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConiferin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePhenylpropanoids\u0026thinsp;\u0026gt;\u0026thinsp;Monolignols\u0026thinsp;\u0026gt;\u0026thinsp;Coniferyl alcohol derivatives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-9.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-63.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5282166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBaimaside\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFlavonoids\u0026thinsp;\u0026gt;\u0026thinsp;Flavonoids\u0026thinsp;\u0026gt;\u0026thinsp;Flavonols\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-9.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-76.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCoat Protein\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5282156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKaempferol 3-sophorotrioside\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFlavonoids\u0026thinsp;\u0026gt;\u0026thinsp;Flavonoids\u0026thinsp;\u0026gt;\u0026thinsp;Flavonols\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-12.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-86.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23724703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDelphinidin 3-O-(6''-O-malonyl)-beta-D-glucoside-3'-O-beta-D-glucoside\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFlavonoids\u0026thinsp;\u0026gt;\u0026thinsp;Flavonoids\u0026thinsp;\u0026gt;\u0026thinsp;Anthocyanidins and anthocyanins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-12.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-81.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10843319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTernatin C5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFlavonoids\u0026thinsp;\u0026gt;\u0026thinsp;Flavonoids\u0026thinsp;\u0026gt;\u0026thinsp;Anthocyanidins and anthocyanins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-11.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-44.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHelicase domain\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23724701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eViolanin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFlavonoids\u0026thinsp;\u0026gt;\u0026thinsp;Flavonoids\u0026thinsp;\u0026gt;\u0026thinsp;Anthocyanidins and anthocyanins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-13.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-98.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5282161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBisdemalonylsalvianin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFlavonoids\u0026thinsp;\u0026gt;\u0026thinsp;Flavonoids\u0026thinsp;\u0026gt;\u0026thinsp;Anthocyanidins and anthocyanins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-13.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-44.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6444318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlbireodelphin A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFlavonoids\u0026thinsp;\u0026gt;\u0026thinsp;Flavonoids\u0026thinsp;\u0026gt;\u0026thinsp;Anthocyanidins and anthocyanins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-13.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-67.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRdRp domain\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6444318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlbireodelphin A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePolyketides\u0026thinsp;\u0026gt;\u0026thinsp;Flavonoids\u0026thinsp;\u0026gt;\u0026thinsp;Anthocyanidins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-13.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-78.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e441932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedicagenic acid 3-O-triglucoside\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTerpenoids\u0026thinsp;\u0026gt;\u0026thinsp;Triterpenoids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-13.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-77.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSenna\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePolyketides\u0026thinsp;\u0026gt;\u0026thinsp;Anthraquinones\u0026thinsp;\u0026gt;\u0026thinsp;Anthrone type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-12.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-61.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Molecular Dynamics\u003c/h2\u003e \u003cp\u003eMolecular dynamics (MD) simulations were conducted to gain deeper insights into the dynamic behavior of the docked ligand\u0026ndash;protein complexes. This analysis aimed to evaluate the stability and conformational quality of the complexes over time. Several structural parameters, including root mean square deviation (RMSD), root mean square fluctuation (RMSF), and protein\u0026ndash;ligand interaction profiles, were assessed throughout a 100-ns simulation trajectory. These parameters were employed to comprehensively examine the flexibility, stability, and interaction dynamics of each of the four viral target\u0026ndash;ligand complexes individually.\u003c/p\u003e \u003cp\u003eRoot Mean Square Deviation (RMSD) analysis of the Cα atoms, representing the protein backbone, was carried out over the 100-nanosecond simulation trajectory to assess the conformational stability and dynamic flexibility of the protein structures. The RMSD profiles of the four viral modeled proteins showed deviations ranging approximately from 2\u0026ndash;2.5 \u0026Aring; for MP, 6\u0026ndash;7 \u0026Aring; for CP, 2.5\u0026ndash;3.3 \u0026Aring; for Helicase domain, and 3\u0026ndash;4 \u0026Aring; for RdRp domain. Although slight fluctuations in the Cα atoms were observed during the initial stages of the simulation, all four protein\u0026ndash;ligand complexes gradually stabilized and reached equilibrium toward the end of the simulation period. No abnormal deviations were detected, suggesting consistent structural stability across all systems. These observations confirm that the proteins maintained their folded conformations throughout the simulation, and the ligand interactions did not induce any significant structural perturbations.\u003c/p\u003e \u003cp\u003eAnalysis of ligand RMSD revealed that Panasenoside bound to MP exhibited an initial peak of ~\u0026thinsp;4 \u0026Aring; but stabilized after 40 ns, maintaining fluctuations around 2.5 \u0026Aring;, suggesting a flexible yet persistent interaction within the MP binding site. Kaempferol 3-sophorotrioside bound to CP achieved stable RMSD values after 20 ns, with an average ligand RMSD of ~\u0026thinsp;7 \u0026Aring;, and the overlap of ligand and protein RMSD trajectories indicated sustained interactions within the CP binding cavity. Violanin bound to Helicase domain maintained a stable configuration throughout the 100-ns simulation, with a ligand RMSD of ~\u0026thinsp;3.5 \u0026Aring; consistently below the protein RMSD, indicating persistent binding within the Helicase domain pocket. Similarly, Albireodelphin A bound to RdRp domain showed initial stability during the first 30 ns, followed by slight fluctuations, ultimately maintaining a ligand RMSD of ~\u0026thinsp;4.8 \u0026Aring; at the end of the simulation, with the ligand RMSD remaining below the protein RMSD, reflecting stable engagement within the RdRp domain binding site. The detailed RMSD trajectories representing the conformational stability of all four protein\u0026ndash;ligand complexes are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the residue-level flexibility of the proteins was analyzed using Root Mean Square Fluctuation (RMSF) over a 100-nanosecond molecular dynamics simulation. RMSF quantifies the extent of deviation of each amino acid residue from its average position throughout the simulation, thereby providing valuable insights into the local mobility and dynamic behavior of the protein structure. In contrast to RMSD, which evaluates the overall conformational deviation of the protein backbone (Cα atoms), RMSF focuses on residue-specific movements, particularly assessing the flexibility of side chains within the binding pocket. Across all analyzed complexes, residue fluctuations were primarily observed around ~\u0026thinsp;4 \u0026Aring;. Although a universally accepted RMSF threshold for modeled proteins has not been established, previous molecular dynamics studies have generally reported that flexible loop regions exhibit fluctuations within the 1\u0026ndash;4 \u0026Aring; range, while residues located in the structural core typically fluctuate below ~\u0026thinsp;2 \u0026Aring;. In the present analysis, most residues within the active site exhibited comparatively lower RMSF values, signifying the rigidity and structural stability of the binding pocket, despite minor variations in the loop or terminal regions. These findings are consistent with earlier molecular dynamics studies conducted on modeled protein structures.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe analysis of protein\u0026ndash;ligand interactions provided valuable insights into the dynamic behavior of the complexes, elucidating their structural stability and revealing a strong correlation with the observed molecular dynamics profiles. The physicochemical properties and spatial orientation of amino acid residues within the binding pocket play a pivotal role in maintaining the integrity and stability of protein\u0026ndash;ligand associations. For the MP\u0026ndash;Panasenoside complex, the ligand exhibited strong and persistent hydrogen bond interactions with residues Asp34, Val130, and Trp131, displaying interaction fractions of 1.75, 0.75, and 0.74, respectively. The interaction fraction represents the persistence of specific contacts throughout the molecular dynamics simulation, where a value of 1.0 denotes a continuous interaction across 100% of the simulation frames, while values exceeding 1.0 indicate that a residue can form multiple concurrent interactions with the ligand within a single frame. Similarly, in the CP\u0026ndash;Kaempferol 3-sophorotrioside complex, residues Asn185, Asn189, and Glu182 exhibited interaction fractions of 0.9, 0.7, and 0.6, respectively, signifying stable hydrogen bonding within the binding pocket. For the Helicase domain-Violanin complex, residues Arg142, Ile140, and Asp106 demonstrated notable interaction fractions of 1.35, 1.05, and 1.00, respectively, reflecting consistent and robust interactions throughout the simulation trajectory. Likewise, in the RdRp\u0026ndash;Albireodelphin A complex, residues Asp280, Thr60, and Ala155 showed interaction fractions of 1.95, 1.00, and 0.75, indicating persistent and stable hydrogen bonding with the ligand. Collectively, these findings highlight the presence of strong, stable, and often multiple hydrogen-bonding interactions between the phytochemical ligands and their respective viral protein targets, underscoring the robustness and consistency of these associations throughout the simulation period. The detailed interaction profiles of all four complexes are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eEmergent plant viruses pose a continual threat, necessitating rapid interventions to prevent or limit the spread of novel pathogens. Tomato brown rugose fruit virus (ToBRFV) is a highly contagious, and destructive tobamovirus, with tomato as its primary host. Within a few years of its initial discovery in Israel and Jordan, ToBRFV disseminated to multiple countries worldwide. Over the past three years, extensive research has focused on understanding its transmission, distribution, host range, prevention strategies, and the development of rapid and specific detection methods. Since its emergence, ToBRFV has caused substantial losses in the global tomato industry and is now recognized as a plant quarantine pathogen of international concern [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConsidering the increasing global threat posed by ToBRFV and other emerging plant viruses, the development of novel, sustainable, and environmentally compatible antiviral strategies has become essential. In the present study, molecular modeling, molecular docking, and molecular dynamics (MD) simulations were employed to identify potential phytochemical inhibitors targeting the coat protein (CP), movement protein (MP), helicase domain, and RNA-dependent RNA polymerase (RdRP) domain of ToBRFV. The three-dimensional structures of these viral proteins were generated using AlphaFold. As the CP monomer exhibited structural instability due to its small size, a CP dimer was constructed using the AlphaFold Multimer model. Dimeric forms of tobamoviral coat proteins are well established, as seen in TMV, where the coat protein exists in monomeric, dimeric, and trimeric states [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Similarly, a significant proportion of TMV strain flavum coat protein in infected tissues exists predominantly as a dimeric form [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Hence, the CP dimer model was considered structurally valid and used for molecular docking against the phytochemical library.\u003c/p\u003e \u003cp\u003eThe docking studies, supported by MM-GBSA binding free energy analysis, identified the top-scoring phytochemical for each viral target. Based on this analysis, Panasenoside (\u0026ndash;10.04 kcal/mol) exhibited the strongest inhibitory potential against MP, Kaempferol 3-sophorotrioside (\u0026ndash;12.13 kcal/mol) against CP, Violanin (\u0026ndash;13.98 kcal/mol) against Helicase domain, and Albireodelphin A (\u0026ndash;13.26 kcal/mol) against RdRP domain. Previous studies have highlighted the antiviral potential of phytochemicals such as curcumin, demethoxycurcumin, and bisdemethoxycurcumin from Curcuma longa, which showed strong binding with key viral proteins of Potato virus Y [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] and TYLCV-Sardinia Rep protein [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Similarly, kaempferol, quercetin, and luteolin were identified as potent inhibitors of Tobacco mosaic virus structural and replicase proteins [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePanasenoside, a flavonoid derived from \u003cem\u003ePanax ginseng\u003c/em\u003e, has shown strong binding affinity against early Huntington\u0026rsquo;s disease-related proteins in computational studies [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Kaempferol 3-sophorotrioside, found in \u003cem\u003ePisum sativum\u003c/em\u003e seed pods and Solanum melongena, exhibits notable antioxidant, hepatoprotective, and antitumor activities [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Violanin, a flavonol glycoside and major anthocyanin present in the petals of \u003cem\u003eViola\u003c/em\u003e species such as \u003cem\u003eViola cornuta\u003c/em\u003e, demonstrates significant bioactivity [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Albireodelphin A, a glycosylated anthocyanin flavonoid, has been reported as a potent inhibitor of SARS-CoV-2 proteins, including the main protease, RdRp, and spike protein, in molecular docking studies [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. The consistent identification of flavonoids as the top inhibitors across all ToBRFV targets highlights the potential of this class of compounds as effective antiviral agents against this virus.\u003c/p\u003e \u003cp\u003eAmong plant-derived bioactive compounds, flavonoids are particularly significant, with flavonoids receiving considerable research attention. Flavonoids are a major class of naturally occurring polyphenolic secondary metabolites widely distributed in fruits, vegetables, and other food crops [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. To date, over 10,000 flavonoids have been identified, exhibiting remarkable structural diversity and a broad spectrum of pharmacological properties [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Their antiviral activities have been extensively explored, demonstrating inhibitory effects against a wide range of DNA and RNA viruses through mechanisms such as interference with viral entry, replication, translation, and release, as well as modulation of host immune responses [\u003cspan additionalcitationids=\"CR57\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Viruses rely on host metabolism and cellular machinery for replication and propagation, exploiting host cells to spread throughout the organism [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Flavonoids can disrupt viral infection through multiple mechanisms, including obstruction of viral attachment and entry, inhibition of genome replication, interference with protein translation and polyprotein processing, and prevention of viral release to adjacent host cells [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFlavonoid glycosides isolated from \u003cem\u003eClematis lasiandra\u003c/em\u003e Maxim, including quercetin-3-O-rutinoside and kaempferol-3-O-rutinoside, exhibited potent antiviral activity against Tobacco mosaic virus (TMV) by directly interacting with viral coat proteins, leading to structural disruption of viral particles and suppression of viral transmission and replication [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Similarly, molecular docking studies of flavonoids such as quercetin, kaempferol, and luteolin with viral proteins of Chilli leaf curl Ahmedabad virus (ChiLCAV) revealed strong binding affinities, indicating their potential inhibitory roles in infection [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Flavonoid-benzothiazole derivatives also demonstrated potent antiviral activity against TMV, exhibiting significant interactions with viral target proteins through both molecular and biological evaluations [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition to molecular docking and MM-GBSA calculations, the stability of the four selected complexes, MP-Panasenoside, CP-Kaempferol 3-sophorotrioside, Helicase domain-Violanin, and RdRP domain-Albireodelphin A, was further evaluated using molecular dynamics simulations. The results revealed that the MP\u0026ndash;Panasenoside and Helicase\u0026ndash;Violanin complexes exhibited pronounced stability throughout the 100-ns simulation, while minor fluctuations observed in the CP and RdRP domain complexes did not compromise overall ligand binding. All four protein\u0026ndash;ligand systems maintained structural integrity and stable interactions within their respective binding pockets. The RMSD and RMSF values of the complexes remained within acceptable limits for modeled protein, consistent with previously published studies [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e].\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, this study integrated molecular modeling, docking, MM-GBSA, and molecular dynamics simulations to identify potential phytochemical inhibitors targeting key proteins of Tomato brown rugose fruit virus (ToBRFV), including the coat protein, movement protein, helicase domain, and RdRp domain. Among the screened compounds, Panasenoside, Kaempferol 3-sophorotrioside, Violanin, and Albireodelphin A demonstrated the strongest binding affinities and stable interactions, with flavonoids emerging as the most promising antiviral candidates. Molecular dynamics simulations further confirmed the stability and strong interaction profiles of these protein\u0026ndash;ligand complexes. Furthermore, the combined application of these four compounds could be explored in foliar spray formulations to evaluate their synergistic antiviral efficacy under experimental conditions. This study also emphasizes the significance of flavonoids as potent antiviral agents, suggesting that targeted manipulation of flavonoid biosynthetic pathways to enhance their accumulation may serve as a viable strategy for developing flavonoid-enriched, virus-resistant tomato plants. Future \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e investigations are essential to validate these computational predictions and to advance the development of sustainable, flavonoid-based antiviral strategies for effective ToBRFV management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the Director, CSIR-NBRI, for providing the necessary computational hardware and software support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVS conceived and designed the experiments. NV, NPM and GJ performed the experiments and analysed the data. The original draft was written by NV, NPM, CM, AS, SW. Review and editing were carried out by SK, VS, CVR. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated and analyzed during this study are included in the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study does not involve any clinical trial or experimentation involving human or animal\u0026nbsp;subjects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSavary S, Willocquet L, Pethybridge SJ, et al. 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J Infect Public Health. 2023;16:117\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jiph.2022.12.006\u003c/span\u003e\u003cspan address=\"10.1016/j.jiph.2022.12.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-plants","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Plants](https://link.springer.com/journal/44372)","snPcode":"44372","submissionUrl":"https://submission.springernature.com/new-submission/44372/3","title":"Discover Plants","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"AlphaFold, Molecular docking, Molecular dynamics, multi-target approach, ToBRFV","lastPublishedDoi":"10.21203/rs.3.rs-8550076/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8550076/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTomato brown rugose fruit virus (ToBRFV), a tobamovirus, poses a significant threat to global tomato production due to its high infectivity, seed-borne transmission, and severe fruit symptoms. In this study, an integrative computational approach was employed to identify plant-derived phytochemicals capable of inhibiting essential viral proteins such as movement protein (MP), coat protein (CP), helicase domain, and RNA-dependent RNA polymerase (RdRP) domain. The three-dimensional structures of these viral targets were predicted using AlphaFold and subsequently validated using Ramachandran plots. A library of 2,847 phytochemicals was subjected to molecular docking, followed by MM-GBSA binding free energy calculations to evaluate binding affinity and interaction strength. Top-ranked compounds were further validated through 100-ns molecular dynamics (MD) simulations to assess complex stability and conformational behavior. Panasenoside, Kaempferol 3-sophorotrioside, Violanin, and Albireodelphin A exhibited the strongest binding affinities toward MP, CP, Helicase, and RdRP, respectively. RMSD and RMSF analyses confirmed the stability of these complexes, highlighting persistent hydrogen-bonding interactions within the active sites. The findings underscore the potential of flavonoids as effective antiviral agents against ToBRFV and provide a foundation for future \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e validation studies to develop flavonoid-based antiviral formulations for sustainable tomato crop protection.\u003c/p\u003e","manuscriptTitle":"Computational Identification of Novel Inhibitors Targeting Multiple Proteins of Tomato Brown Rugose Fruit Virus (ToBRFV) Through AlphaFold-Based Protein Modeling, Molecular Docking, MM/GBSA Binding Free Energy Analysis, and Molecular Dynamics Simulation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-12 06:20:30","doi":"10.21203/rs.3.rs-8550076/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-02T11:07:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-02T06:04:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-27T07:27:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"302627016050801617211867836536707124466","date":"2026-03-21T14:30:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"249176380713547346593057311154066119366","date":"2026-03-19T05:23:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"224667640182098680254518128524918602072","date":"2026-03-19T01:52:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"145908452172964371820215304631800334136","date":"2026-03-18T16:26:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"12008648302010659045895862338503521688","date":"2026-02-22T14:14:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-08T13:51:43+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-29T14:02:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-14T07:30:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-14T07:28:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Plants","date":"2026-01-08T09:56:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-plants","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Plants](https://link.springer.com/journal/44372)","snPcode":"44372","submissionUrl":"https://submission.springernature.com/new-submission/44372/3","title":"Discover Plants","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5b7b4199-f103-4ec3-a14d-ed82af9888db","owner":[],"postedDate":"February 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T06:23:26+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-12 06:20:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8550076","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8550076","identity":"rs-8550076","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}